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Goyal B, Dogra A, Jalamneh A, Chyophel Lepcha D, Alkhayyat A, Singh R, Jyoti Saikia M. Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications. Sci Rep 2024; 14:17122. [PMID: 39054308 PMCID: PMC11272774 DOI: 10.1038/s41598-024-64421-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 06/09/2024] [Indexed: 07/27/2024] Open
Abstract
Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement properties of light intensity, detail presentation, and colour integrity in low-light enhancement tasks. This study presents a novel image enhancement framework using a detailed-based dictionary learning and camera response model (CRM). It combines dictionary learning with edge-aware filter-based detail enhancement. It assumes each small detail patch could be sparsely characterised in the over-complete detail dictionary that was learned from many training detail patches using iterative ℓ 1 -norm minimization. Dictionary learning will effectively address several enhancement concerns in the progression of detail enhancement if we remove the visibility limit of training detail patches in the enhanced detail patches. We apply illumination estimation schemes to the selected CRM and the subsequent exposure ratio maps, which recover a novel enhanced detail layer and generate a high-quality output with detailed visibility when there is a training set of higher-quality images. We estimate the exposure ratio of each pixel using illumination estimation techniques. The selected camera response model adjusts each pixel to the desired exposure based on the computed exposure ratio map. Extensive experimental analysis shows an advantage of the proposed method that it can obtain enhanced results with acceptable distortions. The proposed research article can be generalised to address numerous other similar problems, such as image enhancement for remote sensing or underwater applications, medical imaging, and foggy or dusty conditions.
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Affiliation(s)
- Bhawna Goyal
- Department of UCRD and ECE, Chandigarh University, Mohali, Punjab, 140413, India.
| | - Ayush Dogra
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India
| | - Ammar Jalamneh
- College of Arts & Science Applied Science University, Manama, Kingdom of Bahrain
| | - Dawa Chyophel Lepcha
- Department of UCRD and ECE, Chandigarh University, Mohali, Punjab, 140413, India
| | - Ahmed Alkhayyat
- College of Technical Engineering, The Islamic University, Najaf, Iraq
| | - Rajesh Singh
- Department of ECE, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
| | - Manob Jyoti Saikia
- Department of Electrical Engineering, University of North Florida, Jacksonville, FL, 32224, USA
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2
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You S, Lin S, Feng Y, Fan J, Yan Z, Liu S, Ji Y. ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method. SENSORS (BASEL, SWITZERLAND) 2024; 24:3216. [PMID: 38794069 PMCID: PMC11126124 DOI: 10.3390/s24103216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/04/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage's foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.
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Affiliation(s)
- Shuai You
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.Y.); (Y.F.)
| | - Shijun Lin
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.L.); (S.L.)
| | - Yujian Feng
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.Y.); (Y.F.)
| | - Jianhua Fan
- The 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China;
| | - Zhenzheng Yan
- Northern Information Control Research Academy Group Co., Ltd., Nanjing 211153, China;
| | - Shangdong Liu
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.L.); (S.L.)
| | - Yimu Ji
- School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; (S.L.); (S.L.)
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3
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Li W, Zhou G, Wang X. Low illumination fog noise image denoising method based on ACE-GPM. PLoS One 2024; 19:e0302492. [PMID: 38713661 DOI: 10.1371/journal.pone.0302492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 04/05/2024] [Indexed: 05/09/2024] Open
Abstract
The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.
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Affiliation(s)
- Wuyi Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
| | - Guanglu Zhou
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
| | - Xingjian Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
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4
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Sun Y, Hu J, Yun J, Liu Y, Bai D, Liu X, Zhao G, Jiang G, Kong J, Chen B. Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam. SENSORS (BASEL, SWITZERLAND) 2022; 22:7576. [PMID: 36236676 PMCID: PMC9571389 DOI: 10.3390/s22197576] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 09/28/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. Our proposed method can not only reduce the absolute positional errors (APE) and improve the positioning performance of the system but also construct the object-oriented dense semantic point cloud map and output point cloud model of each object to reconstruct each object in the indoor scene. In fact, eight categories of objects are used for detection and semantic mapping using coco weights in our experiments, and most objects in the actual scene can be reconstructed in theory. Experiments show that the number of points in the point cloud is significantly reduced. The average positioning error of the eight categories of objects in Technical University of Munich (TUM) datasets is very small. The absolute positional error of the camera is also reduced with the introduction of semantic constraints, and the positioning performance of the system is improved. At the same time, our algorithm can segment the point cloud model of objects in the environment with high accuracy.
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Affiliation(s)
- Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Jun Hu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Juntong Yun
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Ying Liu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Dongxu Bai
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Xin Liu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Guojun Zhao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Guozhang Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Jianyi Kong
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang 443002, China
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5
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Zhao X, Wu W, Chen W, Lin Y, Ke J. Multi-network collaborative lift-drag ratio prediction and airfoil optimization based on residual network and generative adversarial network. Front Bioeng Biotechnol 2022; 10:927064. [PMID: 36147536 PMCID: PMC9486308 DOI: 10.3389/fbioe.2022.927064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
As compared with the computational fluid dynamics(CFD), the airfoil optimization based on deep learning significantly reduces the computational cost. In the airfoil optimization based on deep learning, due to the uncertainty in the neural network, the optimization results deviate from the true value. In this work, a multi-network collaborative lift-to-drag ratio prediction model is constructed based on ResNet and penalty functions. Latin supersampling is used to select four angles of attack in the range of 2°–10° with significant uncertainty to limit the prediction error. Moreover, the random drift particle swarm optimization (RDPSO) algorithm is used to control the prediction error. The experimental results show that multi-network collaboration significantly reduces the error in the optimization results. As compared with the optimization based on a single network, the maximum error of multi-network coordination in single angle of attack optimization reduces by 16.0%. Consequently, this improves the reliability of airfoil optimization based on deep learning.
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Affiliation(s)
- Xiaoyu Zhao
- Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan, China
| | - Weiguo Wu
- Green and Smart River-Sea-Going Ship, Cruise and Yacht Research Center, Wuhan, China
| | - Wei Chen
- Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan, China
| | - Yongshui Lin
- Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics, Department of Engineering Structure and Mechanics, Wuhan University of Technology, Wuhan, China
| | - Jiangcen Ke
- School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China
- *Correspondence: Jiangcen Ke,
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Zhao H, Tao B, Huang L, Chen B. A siamese network-based approach for vehicle pose estimation. Front Bioeng Biotechnol 2022; 10:948726. [PMID: 36118568 PMCID: PMC9478513 DOI: 10.3389/fbioe.2022.948726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.
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Affiliation(s)
- Haoyi Zhao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Wisdri Utility Tunnel Designing Institute, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Bo Tao, ; Baojia Chen,
| | - Licheng Huang
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
- *Correspondence: Bo Tao, ; Baojia Chen,
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7
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Liu J, Gu Q, Chen D, Yan D. VSLAM method based on object detection in dynamic environments. Front Neurorobot 2022; 16:990453. [PMID: 36119718 PMCID: PMC9478733 DOI: 10.3389/fnbot.2022.990453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/17/2022] [Indexed: 12/02/2022] Open
Abstract
Augmented Reality Registration field now requires improved SLAM systems to adapt to more complex and highly dynamic environments. The commonly used VSLAM algorithm has problems such as excessive pose estimation errors and easy loss of camera tracking in dynamic scenes. To solve these problems, we propose a real-time tracking and mapping method based on GMM combined with YOLOv3. The method utilizes the ORB-SLAM2 system framework and improves its tracking thread. It combines the affine transformation matrix to correct the front and back frames, and employs GMM to model the background image and segment the foreground dynamic region. Then, the obtained dynamic region is sent to the YOLO detector to find the possible dynamic target. It uses the improved Kalman filter algorithm to predict and track the detected dynamic objects in the tracking stage. Before building a map, the method filters the feature points detected in the current frame and eliminates dynamic feature points. Finally, we validate the proposed method using the TUM dataset and conduct real-time Augmented Reality Registration experiments in a dynamic environment. The results show that the method proposed in this paper is more robust under dynamic datasets and can register virtual objects stably and in real time.
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8
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Wei S. Vibration Signal Analysis Based on Spherical Error Compensation. Front Bioeng Biotechnol 2022; 10:950580. [PMID: 36061432 PMCID: PMC9438900 DOI: 10.3389/fbioe.2022.950580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
A vibrating screen is important equipment in industrial production. According to the principle of bionics, a vibrating screen can be divided into a linear vibrating screen, elliptical vibrating screen, ball vibrating screen, and banana vibrating screen. There are also great problems with the use of a vibrating screen. The vibrating screen works due to the vibration excitation force generated by vibration. This work studies the motion trajectory of a vibrating screen by taking the vibrating screen with line motion trajectory as the research object. In this study, the vibration information is detected by an intelligent sensor, and the signal is filtered by an intelligent algorithm. Then, the spherical error compensation is used to improve the calculation accuracy, and the least square method is used to evaluate the error. Finally, the accurate vibration trajectory of the vibrating screen is obtained. The acquisition of a vibration track can provide the working efficiency and safety performance of the vibrating screen, and has social and economic benefits.
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Affiliation(s)
- Shan Wei
- Industry Design Department, Xin Xiang Universtiy, Xinxiang, China
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9
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Yun J, Jiang D, Liu Y, Sun Y, Tao B, Kong J, Tian J, Tong X, Xu M, Fang Z. Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network. Front Bioeng Biotechnol 2022; 10:861286. [PMID: 36051585 PMCID: PMC9426345 DOI: 10.3389/fbioe.2022.861286] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
The continuous development of deep learning improves target detection technology day by day. The current research focuses on improving the accuracy of target detection technology, resulting in the target detection model being too large. The number of parameters and detection speed of the target detection model are very important for the practical application of target detection technology in embedded systems. This article proposed a real-time target detection method based on a lightweight convolutional neural network to reduce the number of model parameters and improve the detection speed. In this article, the depthwise separable residual module is constructed by combining depthwise separable convolution and non–bottleneck-free residual module, and the depthwise separable residual module and depthwise separable convolution structure are used to replace the VGG backbone network in the SSD network for feature extraction of the target detection model to reduce parameter quantity and improve detection speed. At the same time, the convolution kernels of 1 × 3 and 3 × 1 are used to replace the standard convolution of 3 × 3 by adding the convolution kernels of 1 × 3 and 3 × 1, respectively, to obtain multiple detection feature graphs corresponding to SSD, and the real-time target detection model based on a lightweight convolutional neural network is established by integrating the information of multiple detection feature graphs. This article used the self-built target detection dataset in complex scenes for comparative experiments; the experimental results verify the effectiveness and superiority of the proposed method. The model is tested on video to verify the real-time performance of the model, and the model is deployed on the Android platform to verify the scalability of the model.
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Affiliation(s)
- Juntong Yun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Du Jiang, ; Ying Liu, ; Ying Sun, ; Zifan Fang,
| | - Ying Liu
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Du Jiang, ; Ying Liu, ; Ying Sun, ; Zifan Fang,
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Du Jiang, ; Ying Liu, ; Ying Sun, ; Zifan Fang,
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jianyi Kong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jinrong Tian
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Manman Xu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Zifan Fang
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
- *Correspondence: Du Jiang, ; Ying Liu, ; Ying Sun, ; Zifan Fang,
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10
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Wang Y, Li T, Wu J, Ding CHQ. Bio-driven visual saliency detection with color factor. Front Bioeng Biotechnol 2022; 10:946084. [PMID: 35992342 PMCID: PMC9386261 DOI: 10.3389/fbioe.2022.946084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Most visual saliency computing methods build models based on the content of an image without considering the colorized effects. Biologically, human attention can be significantly influenced by color. This study firstly investigates the sole contribution of colors in visual saliency and then proposes a bio-driven saliency detection method with a color factor. To study the color saliency despite the contents, an eye-tracking dataset containing color images and gray-scale images of the same content is proposed, collected from 18 subjects. The CIELab color space was selected to conduct extensive analysis to identify the contribution of colors in guiding visual attention. Based on the observations that some particular colors and combinations of color blocks can attract much attention than others, the influence of colors on visual saliency is represented computationally. Incorporating the color factor, a novel saliency detection model is proposed to model the human color perception prioritization, and a deep neural network model is proposed for eye fixation prediction. Experiments validate that the proposed bio-driven saliency detection models make substantial improvements in finding informative content, and they benefit the detection of salient objects which are close to human visual attention in natural scenes.
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Affiliation(s)
- Yan Wang
- School of Computer Science and Technology, Anhui University, Hefei, China
| | - Teng Li
- School of artificial intelligence, Anhui University, Hefei, China
- *Correspondence: Teng Li,
| | - Jun Wu
- Guangdong CAS Cogniser Information Technology Co., Ltd., Guangzhou, China
| | - Chris H. Q. Ding
- School of Data Science, The Chinese University of Hong Kong, Shenzhen, China
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11
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Zhu P, Ouyang W, Guo Y, Zhou X. A Two-To-One Deep Learning General Framework for Image Fusion. Front Bioeng Biotechnol 2022; 10:923364. [PMID: 35979172 PMCID: PMC9376963 DOI: 10.3389/fbioe.2022.923364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/09/2022] [Indexed: 11/28/2022] Open
Abstract
The image fusion algorithm has great application value in the domain of computer vision, which makes the fused image have a more comprehensive and clearer description of the scene, and is beneficial to human eye recognition and automatic mechanical detection. In recent years, image fusion algorithms have achieved great success in different domains. However, it still has huge challenges in terms of the generalization of multi-modal image fusion. In reaction to this problem, this paper proposes a general image fusion framework based on an improved convolutional neural network. Firstly, the feature information of the input image is captured by the multiple feature extraction layers, and then multiple feature maps are stacked along the number of channels to acquire the feature fusion map. Finally, feature maps, which are derived from multiple feature extraction layers, are stacked in high dimensions by skip connection and convolution filtering for reconstruction to produce the final result. In this paper, multi-modal images are gained from multiple datasets to produce a large sample space to adequately train the network. Compared with the existing convolutional neural networks and traditional fusion algorithms, the proposed model not only has generality and stability but also has some strengths in subjective visualization and objective evaluation, while the average running time is at least 94% faster than the reference algorithm based on neural network.
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Affiliation(s)
- Pan Zhu
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Wanqi Ouyang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Wanqi Ouyang,
| | - Yongxing Guo
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xinglin Zhou
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
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12
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Cao Y, Wang H, Zhao H, Yang X. Neural-Network-Based Model-Free Calibration Method for Stereo Fisheye Camera. Front Bioeng Biotechnol 2022; 10:955233. [PMID: 35910026 PMCID: PMC9334662 DOI: 10.3389/fbioe.2022.955233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
The fisheye camera has a field of view (FOV) of over 180°, which has advantages in the fields of medicine and precision measurement. Ordinary pinhole models have difficulty in fitting the severe barrel distortion of the fisheye camera. Therefore, it is necessary to apply a nonlinear geometric model to model this distortion in measurement applications, while the process is computationally complex. To solve the problem, this paper proposes a model-free stereo calibration method for binocular fisheye camera based on neural-network. The neural-network can implicitly describe the nonlinear mapping relationship between image and spatial coordinates in the scene. We use a feature extraction method based on three-step phase-shift method. Compared with the conventional stereo calibration of fisheye cameras, our method does not require image correction and matching. The spatial coordinates of the points in the common field of view of binocular fisheye camera can all be calculated by the generalized fitting capability of the neural-network. Our method preserves the advantage of the broad field of view of the fisheye camera. The experimental results show that our method is more suitable for fisheye cameras with significant distortion.
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Affiliation(s)
- Yuwei Cao
- School of Automation, Wuhan University of Technology, Wuhan, China
| | - Hui Wang
- School of Automation, Wuhan University of Technology, Wuhan, China
- Key Laboratory of Icing and Anti/De-icing, China Aerodynamics Research and Development Center, Mianyang, China
| | - Han Zhao
- School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Xu Yang
- School of Automation, Wuhan University of Technology, Wuhan, China
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13
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Hao Z, Wang Z, Bai D, Tong X. Surface Defect Segmentation Algorithm of Steel Plate Based on Geometric Median Filter Pruning. Front Bioeng Biotechnol 2022; 10:945248. [PMID: 35845429 PMCID: PMC9283705 DOI: 10.3389/fbioe.2022.945248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Problems such as redundancy of detection model parameters make it difficult to apply to factory embedded device applications. This paper focuses on the analysis of different existing deep learning model compression algorithms and proposes a model pruning algorithm based on geometric median filtering for structured pruning and compression of defect segmentation detection networks on the basis of structured pruning. Through experimental comparisons and optimizations, the proposed optimization algorithm can greatly reduce the network parameters and computational effort to achieve effective pruning of the defect detection algorithm for steel plate surfaces.
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Affiliation(s)
- Zhiqiang Hao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Zhigang Wang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Dongxu Bai
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
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14
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Shi K, Huang L, Jiang D, Sun Y, Tong X, Xie Y, Fang Z. Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm. Front Bioeng Biotechnol 2022; 10:905983. [PMID: 35845413 PMCID: PMC9283690 DOI: 10.3389/fbioe.2022.905983] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.
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Affiliation(s)
- Kangjing Shi
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Li Huang
- College of Computer Science and Technology, Wuhan University of Science and Tec-hnology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Yuanming Xie
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Zifan Fang
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, Three Gorges University, Yichang, China
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15
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Chi Q, Yang Z, Liang HP. A Transendothelial Leukocyte Transmigration Model Based on Computational Fluid Dynamics and BP Neural Network. Front Bioeng Biotechnol 2022; 10:881797. [PMID: 35800330 PMCID: PMC9253467 DOI: 10.3389/fbioe.2022.881797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/04/2022] [Indexed: 11/24/2022] Open
Abstract
The mechanism of immune infiltration involving immune cells is closely related to various diseases. A key issue in immune infiltration is the transendothelial transmigration of leukocytes. Previous studies have primarily interpreted the leukocyte infiltration of from biomedical perspective. The physical mechanism of leukocyte infiltration remains to be explored. By integrating the immune cell transmigration computational fluid dynamics (CFD) data, the paper builds a time-dependent leukocyte transmigration prediction model based on the bio-inspired methods, namely back propagation neural networks (BPNN) model. The model can efficiently predict the immune cell transmigration in a special microvascular environment, and obtain good prediction accuracy. The model accurately predicted the cell movement and flow field changes during the transmigration. In the test data set, it has high prediction accuracy for cell deformation, motion velocity and flow lift forces during downstream motion, and maintains a good prediction accuracy for drag force. The two prediction models achieved the prediction of leukocyte transmigration in a specific microvascular environment and maintained a high prediction accuracy, indicating the feasibility and robustness of the BPNN model applied to the prediction of immune cell infiltration. Compared with traditional CFD simulations, BPNN models avoid complex and time-dependent physical modeling and computational processes.
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Affiliation(s)
- Qingjia Chi
- Department of Engineering Structure and Mechanics, School of Science, Wuhan University of Technology, Wuhan, China
| | - Zichang Yang
- Department of Engineering Structure and Mechanics, School of Science, Wuhan University of Technology, Wuhan, China
| | - Hua-Ping Liang
- State Key Laboratory of Trauma, Burns and Combined Injury, Department of Wound Infection and Drug, Daping Hospital, Army Medical University, Chongqing, China
- *Correspondence: Hua-Ping Liang,
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16
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Sun Y, Huang P, Cao Y, Jiang G, Yuan Z, Bai D, Liu X. Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm. Front Bioeng Biotechnol 2022; 10:900655. [PMID: 35782507 PMCID: PMC9240744 DOI: 10.3389/fbioe.2022.900655] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/16/2022] [Indexed: 12/26/2022] Open
Abstract
Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. First, the parametric finite element model of the new ladle refractory lining is established by using ANSYS Workbench software. The refractory lining is mainly composed of insulating layer, permanent layer and working layer. Secondly, a mathematical model for multi-objective optimization is established to reveal the functional relationship between the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell, the total mass of the ladle and the structural parameters of the ladle refractory lining. Genetic algorithm translates the optimization process of ladle refractory lining into natural evolution and selection. The optimization results show that, compared with the unoptimized ladle refractory lining structure (insulation layer thickness of 0 mm, permanent layer thickness of 81 mm, and working layer thickness of 152 mm), the refractory lining with insulation layer thickness of 8.02 mm, permanent layer thickness of 76.20 mm, and working layer thickness of 148.61 mm has the best thermal insulation performance and longer service life within the variation of ladle refractory lining structure parameters. Finally, the results of the optimization are verified and analyzed in this paper. The study found that by optimizing the design of the ladle refractory lining, the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell and the ladle mass were reduced. The thermal insulation performance and the lightweight performance of the ladle are improved, which is very important for improving the service life of the ladle.
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Affiliation(s)
- Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Peng Huang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Yongcheng Cao
- Hubei Jingmen Wusan Machinery Equipment Manufacturing Co., Ltd, Jingshan, China
| | - Guozhang Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Zhongping Yuan
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Dongxu Bai
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Xin Liu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
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17
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Wang S, Huang L, Jiang D, Sun Y, Jiang G, Li J, Zou C, Fan H, Xie Y, Xiong H, Chen B. Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition. Front Bioeng Biotechnol 2022; 10:909023. [PMID: 35747495 PMCID: PMC9209772 DOI: 10.3389/fbioe.2022.909023] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.
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Affiliation(s)
- Shudi Wang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Li Huang
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, China
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Guozhang Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Jun Li
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Cejing Zou
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Hanwen Fan
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Yuanmin Xie
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Hegen Xiong
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, Yichang, China
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18
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He T, Fu W, Xu J, Zhang Z, Zhou J, Yin Y, Xie Z. Discovering Interdisciplinary Research Based on Neural Networks. Front Bioeng Biotechnol 2022; 10:908733. [PMID: 35721858 PMCID: PMC9203848 DOI: 10.3389/fbioe.2022.908733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods.
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19
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Shen X, Shi G, Ren H, Zhang W. Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm. Front Bioeng Biotechnol 2022; 10:905583. [PMID: 35669053 PMCID: PMC9163545 DOI: 10.3389/fbioe.2022.905583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The development of drones equipped with high-definition cameras has greatly increased the sample size and image segmentation and target detection are important links during the process of image information. As biomimetic remote sensing images are usually prone to blur distortion and distortion in the imaging, transmission and processing stages, this paper improves the vertical grid number of the YOLO algorithm. Firstly, the light and shade of a high-resolution zoom sensing image were abstracted, and the grey-level cooccurrence matrix extracted feature parameters to quantitatively describe the texture characteristics of the zoom sensing image. The Simple Linear Iterative Clustering (SLIC) superpixel segmentation method was used to achieve the segmentation of light/dark scenes, and the saliency area was obtained. Secondly, a high-resolution zoom sensing image model for segmenting light and dark scenes was established to made the dataset meet the recognition standard. Due to the refraction of the light passing through the lens and other factors, the difference of the contour boundary light and dark value between the target pixel and the background pixel would make it difficult to detect the target, and the pixels of the main part of the separated image would be sharper for edge detection. Thirdly, a YOLO algorithm with an improved vertical grid number was proposed to detect the target in real time on the processed superpixel image array. The adjusted aspect ratio of the target in the remote sensing image modified the number of vertical grids in the YOLO network structure by using 20 convolutional layers and five maximum aggregation layers, which was more accurately adapted to “short and coarse” of the identified object in the information density. Finally, through comparison with the improved algorithm and other mainstream algorithms in different environments, the test results on the aid dataset showed that in the target detection of high spatial resolution zoom sensing images, the algorithm in this paper showed higher accuracy than the YOLO algorithm and had real-time performance and detection accuracy.
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Affiliation(s)
- Xinyi Shen
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Guolong Shi
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
- *Correspondence: Guolong Shi,
| | - Huan Ren
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Wu Zhang
- School of Information and Computer, Anhui Agricultural University, Hefei, China
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20
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Huang L, Chen C, Yun J, Sun Y, Tian J, Hao Z, Yu H, Ma H. Multi-Scale Feature Fusion Convolutional Neural Network for Indoor Small Target Detection. Front Neurorobot 2022; 16:881021. [PMID: 35663726 PMCID: PMC9160233 DOI: 10.3389/fnbot.2022.881021] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 03/30/2022] [Indexed: 12/05/2022] Open
Abstract
The development of object detection technology makes it possible for robots to interact with people and the environment, but the changeable application scenarios make the detection accuracy of small and medium objects in the practical application of object detection technology low. In this paper, based on multi-scale feature fusion of indoor small target detection method, using the device to collect different indoor images with angle, light, and shade conditions, and use the image enhancement technology to set up and amplify a date set, with indoor scenarios and the SSD algorithm in target detection layer and its adjacent features fusion. The Faster R-CNN, YOLOv5, SSD, and SSD target detection models based on multi-scale feature fusion were trained on an indoor scene data set based on transfer learning. The experimental results show that multi-scale feature fusion can improve the detection accuracy of all kinds of objects, especially for objects with a relatively small scale. In addition, although the detection speed of the improved SSD algorithm decreases, it is faster than the Faster R-CNN, which better achieves the balance between target detection accuracy and speed.
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Affiliation(s)
- Li Huang
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Science and Technology, Wuhan, China
| | - Cheng Chen
- College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
| | - Juntong Yun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- *Correspondence: Juntong Yun
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Jinrong Tian
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Zhiqiang Hao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Hui Yu
- School of Creative Technologies, University of Portsmouth, Portsmouth, United Kingdom
| | - Hongjie Ma
- School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth, United Kingdom
- Hongjie Ma
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21
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Lu Y, Du J, Liu P, Zhang Y, Hao Z. Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm. Front Bioeng Biotechnol 2022; 10:855667. [PMID: 35573246 PMCID: PMC9091375 DOI: 10.3389/fbioe.2022.855667] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Rice blast, rice sheath blight, and rice brown spot have become the most popular diseases in the cold areas of northern China. In order to further improve the accuracy and efficiency of rice disease diagnosis, a framework for automatic classification and recognition of rice diseases is proposed in this study. First, we constructed a training and testing data set including 1,500 images of rice blast, 1,500 images of rice sheath blight, and 1,500 images of rice brown spot, and 1,100 healthy images were collected from the rice experimental field. Second, the deep belief network (DBN) model is designed to include 15 hidden restricted Boltzmann machine layers and a support vector machine (SVM) optimized with switching particle swarm (SPSO). It is noted that the developed DBN and SPSO-SVM can simultaneously learn three proposed features including color, texture, and shape to recognize the disease type from the region of interest obtained by preprocessing the disease images. The proposed model leads to a hit rate of 91.37%, accuracy of 94.03%, and a false measurement rate of 8.63%, with the 10-fold cross-validation strategy. The value of the area under the receiver operating characteristic curve (AUC) is 0.97, whose accuracy is much higher than that of the conventional machine learning model. The simulation results show that the DBN and SPSO-SVM models can effectively extract the image features of rice diseases during recognition, and have good anti-interference and robustness.
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Affiliation(s)
- Yang Lu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
- *Correspondence: Yang Lu,
| | - Jiaojiao Du
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
| | - Pengfei Liu
- College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, China
| | - Yong Zhang
- School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, China
| | - Zhiqiang Hao
- Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
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22
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Zhang P, Baek SS. Detection and Analysis of Bionic Motion Pose of Single Leg and Hip Joint Based on Random Process. Front Bioeng Biotechnol 2022; 10:884318. [PMID: 35573231 PMCID: PMC9091170 DOI: 10.3389/fbioe.2022.884318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 04/06/2022] [Indexed: 11/13/2022] Open
Abstract
Based on the spectral representation method of random function and combined with memoryless nonlinear translation theory, this paper analyzes the transformation relationship between potential Gaussian random process and non-Gaussian random process, and successfully generates a stationary non-Gaussian random process that conforms to the target non-Gaussian random process. For the non-stationary non-Gaussian random process simulation, on the basis of the stationary Gaussian random process, the intensity non-stationary uniform modulation model is used to modulate it, and combined with the nonlinear translation theory, the non-stationary non-Gaussian random process conforming to the target non-Gaussian random process is obtained. Aiming at the single-leg bouncing model based on the flexible rotary hip joint, the stability of its bouncing motion under passive motion is studied, and the influence of the flexible hip rotary joint on the motion stability is analyzed by comparing the single-leg bouncing motion characteristics of the free rotary hip joint. Based on the inverse dynamic control of the air phase, the fixed point distribution of the single-leg bounce of the flexible rotary hip joint was improved, and the function of the flexible rotary hip joint in the energy conversion of the bouncing motion was studied by establishing the energy consumption evaluation function. The kinematic performance verification, dynamic performance verification, dynamic parameter identification verification, and modal experiment simulation analysis were carried out for the built experimental platform, and the comparison and analysis with its theoretical model were carried out. The results show that the theoretical motion trajectory of the test mobile platform is basically consistent with the actual motion trajectory in the X and Y directions, and there is a small error in the Z-axis direction, and the error is within an acceptable range, indicating that the experimental platform system can be used to simulate the human hip joint. There is a large error between the theoretical value of the driving torque calculated by the theoretical value of the dynamic parameters and the measured value, and the dynamic theoretical model cannot accurately predict the driving torque. The predicted value of the driving torque calculated by using the identification value of the dynamic parameters is in good agreement with the measured torque, and its confidence is increased by 10–16%, indicating that the dynamic parameter identification method in this paper has a high degree of confidence.
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Affiliation(s)
- Peng Zhang
- School of Physical Education, Xinyang Normal University, Xinyang, China
- School of Physical Education, Sangmyung University, Seoul, Korea
| | - Seung-soo Baek
- School of Physical Education, Sangmyung University, Seoul, Korea
- *Correspondence: Seung-soo Baek,
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Han Y, Li X, Li X, Zhou Z, Li J. Recognition and Detection of Wide Field Bionic Compound Eye Target Based on Cloud Service Network. Front Bioeng Biotechnol 2022; 10:865130. [PMID: 35445001 PMCID: PMC9014010 DOI: 10.3389/fbioe.2022.865130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 03/09/2022] [Indexed: 11/17/2022] Open
Abstract
In this paper, a multidisciplinary cross-fusion of bionics, robotics, computer vision, and cloud service networks was used as a research platform to study wide-field bionic compound eye target recognition and detection from multiple perspectives. The current research status of wide-field bionic compound-eye target recognition and detection was analyzed, and improvement directions were proposed. The surface microlens array arrangement was designed, and the spaced surface bionic compound eye design principle cloud service network model was established for the adopted spaced-type circumferential hierarchical microlens array arrangement. In order to realize the target localization of the compound eye system, the content of each step of the localization scheme was discussed in detail. The distribution of virtual spherical targets was designed by using the subdivision of the positive icosahedron to ensure the uniformity of the targets. The spot image was pre-processed to achieve spot segmentation. The energy symmetry-based spot center localization algorithm was explored and its localization effect was verified. A suitable spatial interpolation method was selected to establish the mapping relationship between target angle and spot coordinates. An experimental platform of wide-field bionic compound eye target recognition and detection system was acquired. A super-resolution reconstruction algorithm combining pixel rearrangement and an improved iterative inverse projection method was used for image processing. The model was trained and evaluated in terms of detection accuracy, leakage rate, time overhead, and other evaluation indexes, and the test results showed that the cloud service network-based wide-field bionic compound eye target recognition and detection performs well in terms of detection accuracy and leakage rate. Compared with the traditional algorithm, the correct rate of the algorithm was increased by 21.72%. Through the research of this paper, the wide-field bionic compound eye target recognition and detection and cloud service network were organically provide more technical support for the design of wide-field bionic compound eye target recognition and detection system.
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Zhang X, Xiao F, Tong X, Yun J, Liu Y, Sun Y, Tao B, Kong J, Xu M, Chen B. Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm. Front Bioeng Biotechnol 2022; 10:852408. [PMID: 35392405 PMCID: PMC8981035 DOI: 10.3389/fbioe.2022.852408] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot's trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.
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Affiliation(s)
- Xiaofeng Zhang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Fan Xiao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - XiLiang Tong
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Juntong Yun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Liu
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Jianyi Kong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Manman Xu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
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Tao B, Wang Y, Qian X, Tong X, He F, Yao W, Chen B, Chen B. Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network. Front Bioeng Biotechnol 2022; 10:818112. [PMID: 35387296 PMCID: PMC8978563 DOI: 10.3389/fbioe.2022.818112] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/22/2022] [Indexed: 12/19/2022] Open
Abstract
Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder-decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.
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Affiliation(s)
- Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Yan Wang
- Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
| | - Xinbo Qian
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Fuqiang He
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Weiping Yao
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Bin Chen
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Baojia Chen
- Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, CTGU, Yichang, China
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26
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Wu X, Jiang D, Yun J, Liu X, Sun Y, Tao B, Tong X, Xu M, Kong J, Liu Y, Zhao G, Fang Z. Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC. Front Bioeng Biotechnol 2022; 10:843020. [PMID: 35295652 PMCID: PMC8918931 DOI: 10.3389/fbioe.2022.843020] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/02/2022] [Indexed: 12/22/2022] Open
Abstract
Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they are often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling control algorithm is proposed and A single control volume-single attitude angle model is constructed for the problem of severe coupling in the control system of attitude of six degrees of freedom Autonomous Underwater Vehicle. Aiming at the problem of complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, the PSO-ADRC algorithm is proposed to realize the automatic adjustment of its parameters, which improves the anti-interference ability and control accuracy of Autonomous Underwater Vehicle in dynamic environment. The anti-interference ability and control accuracy of the method were verified through experiments.
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Affiliation(s)
- Xiong Wu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
| | - Du Jiang
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Juntong Yun
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xin Liu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Sun
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Bo Tao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Xiliang Tong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Manman Xu
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Jianyi Kong
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Ying Liu
- Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China
- Precision Manufacturing Research Institute, Wuhan University of Science and Technology, Wuhan, China
| | - Guojun Zhao
- Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, China
- Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China
| | - Zifan Fang
- Hubei Key Laboratory of Hydroelectric Machinery Design & Maintenance, China Three Gorges University, Yichang, China
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27
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Zang H, Wang Y, Ru L, Zhou M, Chen D, Zhao Q, Zhang J, Li G, Zheng G. Detection method of wheat spike improved YOLOv5s based on the attention mechanism. FRONTIERS IN PLANT SCIENCE 2022; 13:993244. [PMID: 36247573 PMCID: PMC9554473 DOI: 10.3389/fpls.2022.993244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/30/2022] [Indexed: 05/17/2023]
Abstract
In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. Labeled images, source code, and trained models are available at: https://github.com/228384274/improved-yolov5.
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Affiliation(s)
- Hecang Zang
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Yanjing Wang
- College of Life Sciences, Zhengzhou Normal University, Zhengzhou, China
- *Correspondence: Yanjing Wang,
| | - Linyuan Ru
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Meng Zhou
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Dandan Chen
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Qing Zhao
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Jie Zhang
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
| | - Guoqiang Li
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
- Guoqiang Li,
| | - Guoqing Zheng
- Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Zhengzhou, China
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