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Xue H, Wu M, Zhang Z, Wang H. Intelligent diagnosis of mechanical faults of in-wheel motor based on improved artificial hydrocarbon networks. ISA TRANSACTIONS 2022; 120:360-371. [PMID: 33812690 DOI: 10.1016/j.isatra.2021.03.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 02/02/2021] [Accepted: 03/12/2021] [Indexed: 05/27/2023]
Abstract
For the driving safety of electric vehicle (EV), intelligent diagnosis based on artificial hydrocarbon networks (AHNs) is proposed to detect mechanical faults of in-wheel motor (IWM) which is a promising force pattern of EV. AHNs, a novel mathematical model of supervised learning algorithm, can encapsulate or inherit or mix any information, then are adapted to deal with serious external interference and the variable operating conditions. Based on the basic AHNs, complex error function is proposed to optimize more information of classification targets, and distance error ratio is defined to evaluate the performance. Then, the improved AHNs is employed to build two intelligent diagnosis systems namely one-stop diagnosis and sequential diagnosis, which select the same and different symptom parameters as the object of a follow-on process, respectively. The effectiveness of the proposed methods is validated by two case studies of Case Western Reserve University dataset and mechanical faults data from IWM's test bench.
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Affiliation(s)
- Hongtao Xue
- School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China.
| | - Meng Wu
- School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China
| | - Ziming Zhang
- School of Automotive and Traffic Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, China
| | - Huaqing Wang
- School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, 15 Beisanhuan East Road, ChaoYang District, 100029, Beijing, China.
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Contrast and brightness balance in image enhancement using Cuckoo Search-optimized image fusion. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2021. [DOI: 10.1016/j.jksuci.2021.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Luque-Chang A, Cuevas E, Pérez-Cisneros M, Fausto F, Valdivia-González A, Sarkar R. Moth Swarm Algorithm for Image Contrast Enhancement. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106607] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Abstract
Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.
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Das P, Das A. Shift invariant extrema based feature analysis scheme to discriminate the spiculation nature of mammograms. ISA TRANSACTIONS 2020; 103:156-165. [PMID: 32216985 DOI: 10.1016/j.isatra.2020.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 03/11/2020] [Accepted: 03/12/2020] [Indexed: 06/10/2023]
Abstract
Since uncontrolled growth of malignant masses introduces uneven shape irregularities and spiculations in the boundary, shape representing shift invariant features are essential to resolve the problem of discrimination. However, ambiguous nature of shape, size, margin, orientation of masses produces imprecise feature values. In this view, a new concept of extrema based feature characterization scheme is proposed for capturing radiating nature of mass morphology. Computation of extrema patterns needs only few algorithmic steps. Beside this, present study employs an automated enhancement procedure to improve the classification accuracy. Experimental results show that extrema characterization reduces the feature redundancy to produce high efficiency in reasonably low time.
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Affiliation(s)
- Poulomi Das
- OmDayal Group of Institutions, Maulana Abul Kalam Azad University of Technology, India.
| | - Arpita Das
- Department of Radio Physics and Electronics, University of Calcutta, Rajabazar Science College Campus, India.
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Asokan A, Anitha J. Adaptive Cuckoo Search based optimal bilateral filtering for denoising of satellite images. ISA TRANSACTIONS 2020; 100:308-321. [PMID: 31727322 DOI: 10.1016/j.isatra.2019.11.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 09/04/2019] [Accepted: 11/04/2019] [Indexed: 06/10/2023]
Abstract
A satellite image transmitted from satellite to the ground station is corrupted by different kinds of noises such as impulse noise, speckle noise and Gaussian noise. The traditional methods of denoising can remove the noise components but cannot preserve the quality of the image and lead to over-blurring of the edges in the image. To overcome these drawbacks, this paper develops an optimized bilateral filter for image denoising and preserving the edges using different nature inspired optimization algorithms which can effectively denoise the image without blurring the edges in the image. Denoising the image using a bilateral filter requires the decision of the control parameters so that the noise is removed and the edge details are preserved. With the help of optimization algorithms such as Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Adaptive Cuckoo Search (ACS), the control parameters in the bilateral filter are decided for optimal performance. It is observed that the proposed Adaptive Cuckoo Search based bilateral filter denoising gives better results in terms of Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Feature Similarity Index (FSIM), Entropy and CPU time in comparison to traditional methods such as Median filter and RGB spatial filter.
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Affiliation(s)
- Anju Asokan
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India.
| | - J Anitha
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore 641114, India.
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A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101677] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Cuckoo search algorithm-based brightness preserving histogram scheme for low-contrast image enhancement. Soft comput 2019. [DOI: 10.1007/s00500-019-03992-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhu J, Ji W, Liu J. A Contrast Enhancement Method for Improving Surface Defects Detection of Elevator Compensation Chain. INT J PATTERN RECOGN 2019. [DOI: 10.1142/s0218001419540119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.
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Affiliation(s)
- Jianjiang Zhu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, No. 99, Hushan Road, Changshu 215500, P. R. China
| | - Wei Ji
- School of Mechatronic Engineering, China University of Mining and Technology, No. 1, Daxue Road, Xuzhou 221000, P. R. China
| | - Jicheng Liu
- School of Electrical and Automatic Engineering, Changshu Institute of Technology, No. 99, Hushan Road, Changshu 215500, P. R. China
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Abstract
Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.
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Bhandari AK. A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3771-z] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Cuckoo Search Approach for Parameter Identification of an Activated Sludge Process. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:3476851. [PMID: 29670647 PMCID: PMC5833157 DOI: 10.1155/2018/3476851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 12/05/2017] [Accepted: 12/20/2017] [Indexed: 11/18/2022]
Abstract
A parameter identification problem for a hybrid model is presented. The latter describes the operation of an activated sludge process used for waste water treatment. Parameter identification problem can be considered as an optimization one by minimizing the error between simulation and experimental data. One of the new and promising metaheuristic methods for solving similar mathematical problem is Cuckoo Search Algorithm. It is inspired by the parasitic brood behavior of cuckoo species. To confirm the effectiveness and the efficiency of the proposed algorithm, simulation results will be compared with other algorithms, firstly, with a classical method which is the Nelder-Mead algorithm and, secondly, with intelligent methods such as Genetic Algorithm and Particle Swarm Optimization approaches.
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Dhal KG, Das S. Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement. PATTERN RECOGNITION AND IMAGE ANALYSIS 2017. [DOI: 10.1134/s1054661817040046] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Suresh S, Lal S. Modified differential evolution algorithm for contrast and brightness enhancement of satellite images. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.08.019] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Daniel E, Anitha J, Gnanaraj J. Optimum laplacian wavelet mask based medical image using hybrid cuckoo search – grey wolf optimization algorithm. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.05.017] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Daniel E, Anitha J. Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm. Comput Biol Med 2016; 71:149-55. [DOI: 10.1016/j.compbiomed.2016.02.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 02/17/2016] [Accepted: 02/18/2016] [Indexed: 11/29/2022]
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Kumar A, Pooja R, Singh GK. An efficient closed-form design method for nearly perfect reconstruction of non-uniform filter bank. ISA TRANSACTIONS 2016; 61:167-178. [PMID: 26861726 DOI: 10.1016/j.isatra.2016.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2014] [Revised: 12/04/2015] [Accepted: 01/13/2016] [Indexed: 06/05/2023]
Abstract
In this paper, an efficient closed form method for the design of multi-channel nearly perfect reconstruction of non-uniform filter bank with the prescribed stopband attenuation and channel overlapping is presented. In this method, the design problem of multi-channel non-uniform filter bank (NUFB) is considered as the design of a prototype filter whose magnitude response at quadrature frequency is 0.707, which is exploited for finding the optimum passband edge frequency through empirical formula instead of using single or multivariable optimization technique. Two main attributes used in assessing the performance of filter bank are peak reconstruction error (PRE) and computational time (CPU time). As compared to existing methods, this method is very simple and easy to implement for NUFBs. To implement this algorithm, a Matlab program has been developed, and several examples are presented to illustrate the performance of proposed method.
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Affiliation(s)
- A Kumar
- PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, MP, India.
| | - R Pooja
- PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur 482005, MP, India.
| | - G K Singh
- Indian Institute of Technology Roorkee, Roorkee 247667, Uttrakhand.
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Optimal sub-band adaptive thresholding based edge preserved satellite image denoising using adaptive differential evolution algorithm. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.079] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Kumar M, Rawat TK. Optimal fractional delay-IIR filter design using cuckoo search algorithm. ISA TRANSACTIONS 2015; 59:39-54. [PMID: 26391486 DOI: 10.1016/j.isatra.2015.08.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Revised: 06/04/2015] [Accepted: 08/16/2015] [Indexed: 06/05/2023]
Abstract
This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics. Since fractional delay-IIR filter design is a multi-modal optimization problem, it cannot be computed efficiently using conventional gradient based optimization techniques. A weighted least square (WLS) based fitness function is used to improve the performance to a great extent. FD-IIR filters of different orders have been designed using the CSA. The simulation results of the proposed CSA based approach have been compared to those of well accepted evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the CSA based FD-IIR filter is superior to those obtained by GA and PSO. The simulation and statistical results affirm that the proposed approach using CSA outperforms GA and PSO, not only in the convergence rate but also in optimal performance of the designed FD-IIR filter (i.e., smaller magnitude error, smaller phase error, higher percentage improvement in magnitude and phase error, fast convergence rate). The absolute magnitude and phase error obtained for the designed 5th order FD-IIR filter are as low as 0.0037 and 0.0046, respectively. The percentage improvement in magnitude error for CSA based 5th order FD-IIR design with respect to GA and PSO are 80.93% and 74.83% respectively, and phase error are 76.04% and 71.25%, respectively.
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Affiliation(s)
- Manjeet Kumar
- Division of Electronics and Communication Engineering, Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi 110078, India.
| | - Tarun Kumar Rawat
- Division of Electronics and Communication Engineering, Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi 110078, India.
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Bhandari AK, Kumar A, Singh GK, Soni V. Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold. J EXP THEOR ARTIF IN 2015. [DOI: 10.1080/0952813x.2015.1020518] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Adaptive cuckoo search algorithm for unconstrained optimization. ScientificWorldJournal 2014; 2014:943403. [PMID: 25298971 PMCID: PMC4178923 DOI: 10.1155/2014/943403] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2014] [Accepted: 08/15/2014] [Indexed: 11/17/2022] Open
Abstract
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
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