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Liu SH, Luo BC, Kao YC, Feng GH. Study on the combination of virtual machine tools and wearable vibration devices for operators experiencing cutting forces in the milling process. Sci Rep 2024; 14:8843. [PMID: 38632292 PMCID: PMC11024115 DOI: 10.1038/s41598-024-59208-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/08/2024] [Indexed: 04/19/2024] Open
Abstract
The primary goal of this study is to develop a wearable system for providing CNC machine operators with visual and tactile perception of triaxial cutting forces, thereby assisting operators in industrial environments to enhance work efficiency and prevent mechanical failures. To achieve this goal, we successfully integrated a virtual machining tool simulator with the remote-control wearable system (RCWS). Using the 'King Path' milling parameters, we employed the simulation software developed by the AIM-HI team to calculate static and dynamic cutting forces, converting this data into vibrational commands for the RCWS to generate corresponding tactile feedback. Furthermore, we conducted extensive experiments, testing various data conversion methods, including three sampling techniques and two data compression strategies, aiming to provide accurate tactile feedback related to cutting forces under different operating conditions.
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Affiliation(s)
- Shang-Hsien Liu
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan
| | - Bo-Cheng Luo
- Advanced Institute of Manufacturing with High-Tech Innovations, and Department of Mechanical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan
| | - Yung-Chou Kao
- Advanced Institute of Manufacturing with High-Tech Innovations, and Department of Mechanical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan
| | - Guo-Hua Feng
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, 300044, Taiwan.
- Institute of Nano Engineering and MicroSystems, National Tsing Hua University, Hsinchu, 30013, Taiwan.
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2
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Gohari H, Hassan M, Shi B, Sadek A, Attia H, M’Saoubi R. Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:2324. [PMID: 38610535 PMCID: PMC11014020 DOI: 10.3390/s24072324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber-physical optimization system.
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Affiliation(s)
- Hossein Gohari
- Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada; (H.G.); (H.A.)
- Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Montreal, QC H3T 1J4, Canada; (B.S.); (A.S.)
| | - Mahmoud Hassan
- Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Montreal, QC H3T 1J4, Canada; (B.S.); (A.S.)
| | - Bin Shi
- Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Montreal, QC H3T 1J4, Canada; (B.S.); (A.S.)
| | - Ahmad Sadek
- Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Montreal, QC H3T 1J4, Canada; (B.S.); (A.S.)
| | - Helmi Attia
- Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada; (H.G.); (H.A.)
- Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Montreal, QC H3T 1J4, Canada; (B.S.); (A.S.)
| | - Rachid M’Saoubi
- R&D Material and Technology Development, Seco Tools AB, SE-73782 Fagersta, Sweden;
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3
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Sun H, Cheng Y, Jiang B, Lu F, Wang N. Anomaly Detection Method for Rocket Engines Based on Convex Optimized Information Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:415. [PMID: 38257508 PMCID: PMC10820208 DOI: 10.3390/s24020415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
The power system, as a core component of a launch vehicle, has a crucial impact on the reliability and safety of a rocket launch. Due to the limited measurement information inside the engine, it is often challenging to realize fast and accurate anomaly detection. For this reason, this paper introduces the rocket flight state data to expand the information source for anomaly detection. However, engine measurement and rocket flight state information have different data distribution characteristics. To find the optimal data fusion scheme for anomaly detection, a data set information fusion algorithm based on convex optimization is proposed, which solves the optimal fusion parameter using the convex quadratic programming problem and then adopts the adaptive CUSUM algorithm to realize the fast and accurate anomaly detection of engine faults. Numerical simulation tests show that the algorithm proposed in this paper has a higher detection accuracy and lower detection time than the traditional algorithm.
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Affiliation(s)
- Hao Sun
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (H.S.)
| | - Yuehua Cheng
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (H.S.)
| | - Bin Jiang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (H.S.)
| | - Feng Lu
- College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
| | - Na Wang
- College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; (H.S.)
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4
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Guo M, Zhou J, Li X, Lin Z, Guo W. Prediction of surface roughness based on fused features and ISSA-DBN in milling of die steel P20. Sci Rep 2023; 13:15951. [PMID: 37743378 PMCID: PMC10518346 DOI: 10.1038/s41598-023-42968-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/17/2023] [Indexed: 09/26/2023] Open
Abstract
The roughness of the part surface is one of the most crucial standards for evaluating machining quality due to its relationship with service performance. For a preferable comprehension of the evolution of surface roughness, this study proposes a novel surface roughness prediction model on the basis of the unity of fuse d signal features and deep learning architecture. The force and vibration signals produced in the milling of P20 die steel are collected, and time and frequency domain feature from the acquired signals are extracted by variational modal decomposition. The GA-MI algorithm is taken to select the signal features that are relevant to the surface roughness of the workpiece. The optimal feature subset is analyzed and used as the input of the prediction model. DBN is adopted to estimate the surface roughness and the model parameters are optimized by ISSA. (Reviewer 1, Q1) The separate force, vibration and fusion signal information are brought into the DBN model and the ISSA-DBN model for the prediction of surface roughness, and the results show that the accuracy of the roughness prediction is as follows, respectively DBN: 78.1%, 68.8% and 84.4%, and ISSA-DBN: 93.8%, 87.5% and 100%.
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Affiliation(s)
- Miaoxian Guo
- College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Jin Zhou
- College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xing Li
- Beijing Spacecrafts Co. Ltd., Beijing, 100094, China
| | - Zhijian Lin
- Aplos Machines Manufacturing (Shanghai) Co. Ltd., Shanghai, 201306, China
| | - Weicheng Guo
- College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
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5
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Liu W, Rong Y, Yang R, Wu C, Zhang G, Huang Y. Revealing the interaction mechanism of pulsed laser processing with the application of acoustic emission. FRONTIERS OF OPTOELECTRONICS 2023; 16:14. [PMID: 37314583 DOI: 10.1007/s12200-023-00070-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/23/2023] [Indexed: 06/15/2023]
Abstract
The mechanisms of interaction between pulsed laser and materials are complex and indistinct, severely influencing the stability and quality of laser processing. This paper proposes an intelligent method based on the acoustic emission (AE) technique to monitor laser processing and explore the interaction mechanisms. The validation experiment is designed to perform nanosecond laser dotting on float glass. Processing parameters are set differently to generate various outcomes: ablated pits and irregular-shaped cracks. In the signal processing stage, we divide the AE signals into two bands, main and tail bands, according to the laser processing duration, to study the laser ablation and crack behavior, respectively. Characteristic parameters extracted by a method that combines framework and frame energy calculation of AE signals can effectively reveal the mechanisms of pulsed laser processing. The main band features evaluate the degree of laser ablation from the time and intensity scales, and the tail band characteristics demonstrate that the cracks occur after laser dotting. In addition, from the analysis of the parameters of the tail band very large cracks can be efficiently distinguished. The intelligent AE monitoring method was successfully applied in exploring the interaction mechanism of nanosecond laser dotting float glass and can be used in other pulsed laser processing fields.
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Affiliation(s)
- Weinan Liu
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Youmin Rong
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Ranwu Yang
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Congyi Wu
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Guojun Zhang
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yu Huang
- State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
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6
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Elitist random swapped particle swarm optimization embedded with variable k-nearest neighbour classification: a new PSO variant applied to gene identification. Soft comput 2022. [DOI: 10.1007/s00500-022-07515-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
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7
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Mahmood T, Ali Z. Fuzzy superior mandelbrot sets. Soft comput 2022. [DOI: 10.1007/s00500-022-07254-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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8
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Window-Based Multi-Objective Optimization for Dynamic Patient Scheduling with Problem-Specific Operators. COMPUTERS 2022. [DOI: 10.3390/computers11050063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.
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9
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Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks. SENSORS 2022; 22:s22082858. [PMID: 35458841 PMCID: PMC9030568 DOI: 10.3390/s22082858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/01/2022] [Accepted: 04/05/2022] [Indexed: 02/02/2023]
Abstract
The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time.
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10
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Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. MATHEMATICS 2022. [DOI: 10.3390/math10071192] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
E-learning is a popular area in terms of learning from social media websites in various terms and contents for every group of people in this world with different knowledge backgrounds and jobs. E-learning sites help users such as students, business workers, instructors, and those searching for different educational institutions. Excluding the benefits of this system, there are various challenges that the users face in online platforms. One of the important challenges is the true information and right content based on these resources, search results and quality. This research proposes virtual and intelligent agent-based recommendation, which requires users’ profile information and preferences to recommend the proper content and search results based on their search history. We applied Natural Language Processing (NLP) techniques and semantic analysis approaches for the recommendation of course selection to e-learners and tutors. Moreover, machine learning performance analysis applied to improve the user rating results in the e-learning environment. The system automatically learns and analyzes the learner characteristics and processes the learning style through the clustering strategy. Compared with the recent state-of-the-art in this field, the proposed system and the simulation results show the minimizing number of metric errors compared to other works. The achievements of the presented approach are providing a comfortable platform to the user for course selection and recommendations. Similarly, we avoid recommending the same contents and courses. We analyze the user preferences and improving the recommendation system performance to provide highly related content based on the user profile situation. The prediction accuracy of the proposed system is 98% compared to hybrid filtering, self organization systems and ensemble modeling.
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11
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Tsanousa A, Bektsis E, Kyriakopoulos C, González AG, Leturiondo U, Gialampoukidis I, Karakostas A, Vrochidis S, Kompatsiaris I. A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22051734. [PMID: 35270880 PMCID: PMC8914726 DOI: 10.3390/s22051734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/14/2022] [Accepted: 02/18/2022] [Indexed: 05/05/2023]
Abstract
Manufacturing companies increasingly become "smarter" as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.
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Affiliation(s)
- Athina Tsanousa
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
- Correspondence:
| | - Evangelos Bektsis
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
| | - Constantine Kyriakopoulos
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
| | - Ana Gómez González
- Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), P. J. M. Arizmendiarrieta 2, 20500 Arrasate-Mondragón, Spain; (A.G.G.); (U.L.)
| | - Urko Leturiondo
- Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), P. J. M. Arizmendiarrieta 2, 20500 Arrasate-Mondragón, Spain; (A.G.G.); (U.L.)
| | - Ilias Gialampoukidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
| | - Anastasios Karakostas
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
| | - Stefanos Vrochidis
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
| | - Ioannis Kompatsiaris
- Information Technologies Institute, Centre for Research and Technology Hellas, 6th km Charilaou-Thermi Road, 57001 Thessaloniki, Greece; (E.B.); (C.K.); (I.G.); (A.K.); (S.V.); (I.K.)
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12
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Abstract
For a reliable and convenient system, it is essential to build a secure system that will be protected from outer attacks and also serve the purpose of keeping the inner data safe from intruders. A juice jacking is a popular and spreading cyber-attack that allows intruders to get inside the system through the web and theive potential data from the system. For peripheral communications, Universal Serial Bus (USB) is the most commonly used standard in 5G generation computer systems. USB is not only used for communication, but also to charge gadgets. However, the transferal of data between devices using USB is prone to various security threats. It is necessary to maintain the confidentiality and sensitivity of data on the bus line to maintain integrity. Therefore, in this paper, a juice jacking attack is analyzed, using the maximum possible means through which a system can be affected using USB. Ten different malware attacks are used for experimental purposes. Various machine learning and deep learning models are used to predict malware attacks. An extensive experimental analysis reveals that the deep learning model can efficiently recognize the juice jacking attack. Finally, various techniques are discussed that can either prevent or avoid juice jacking attacks.
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13
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Improvement of Trajectory Tracking by Robot Manipulator Based on a New Co-Operative Optimization Algorithm. MATHEMATICS 2021. [DOI: 10.3390/math9243231] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The tracking of a predefined trajectory with less error, system-settling time, system, and overshoot is the main challenge with the robot-manipulator controller. In this regard, this paper introduces a new design for the robot-manipulator controller based on a recently developed algorithm named the butterfly optimization algorithm (BOA). The proposed BOA utilizes the neighboring butterflies’ co-operation by sharing their knowledge in order to tackle the issue of trapping at the local optima and enhance the global search. Furthermore, the BOA requires few adjustable parameters via other optimization algorithms for the optimal design of the robot-manipulator controller. The BOA is combined with a developed figure of demerit fitness function in order to improve the trajectory tracking, which is specified by the simultaneous minimization of the response steady-state error, settling time, and overshoot by the robot manipulator. Various test scenarios are created to confirm the performance of the BOA-based robot manipulator to track different trajectories, including linear and nonlinear manners. Besides, the proposed algorithm can provide a maximum overshoot and settling time of less than 1.8101% and 0.1138 s, respectively, for the robot’s response compared to other optimization algorithms in the literature. The results emphasize the capability of the BOA-based robot manipulator to provide the best performance compared to the other techniques.
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14
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Multi-Label Feature Selection Combining Three Types of Conditional Relevance. ENTROPY 2021; 23:e23121617. [PMID: 34945923 PMCID: PMC8700541 DOI: 10.3390/e23121617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/19/2021] [Accepted: 11/25/2021] [Indexed: 11/17/2022]
Abstract
With the rapid growth of the Internet, the curse of dimensionality caused by massive multi-label data has attracted extensive attention. Feature selection plays an indispensable role in dimensionality reduction processing. Many researchers have focused on this subject based on information theory. Here, to evaluate feature relevance, a novel feature relevance term (FR) that employs three incremental information terms to comprehensively consider three key aspects (candidate features, selected features, and label correlations) is designed. A thorough examination of the three key aspects of FR outlined above is more favorable to capturing the optimal features. Moreover, we employ label-related feature redundancy as the label-related feature redundancy term (LR) to reduce unnecessary redundancy. Therefore, a designed multi-label feature selection method that integrates FR with LR is proposed, namely, Feature Selection combining three types of Conditional Relevance (TCRFS). Numerous experiments indicate that TCRFS outperforms the other 6 state-of-the-art multi-label approaches on 13 multi-label benchmark data sets from 4 domains.
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15
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Zhang Q, Jiang Q, Li Y, Wang N, He L. Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion. SENSORS (BASEL, SWITZERLAND) 2021; 21:7108. [PMID: 34770414 PMCID: PMC8588266 DOI: 10.3390/s21217108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 10/24/2021] [Accepted: 10/24/2021] [Indexed: 11/16/2022]
Abstract
The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster-Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy's average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.
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Affiliation(s)
- Qi Zhang
- School of Civil Engineering, Southeast University, Nanjing 211189, China; (Q.J.); (N.W.); (L.H.)
- State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China;
| | - Qing Jiang
- School of Civil Engineering, Southeast University, Nanjing 211189, China; (Q.J.); (N.W.); (L.H.)
- Institute of Future Underground Space, Southeast University, Nanjing 211189, China
| | - Yuanhai Li
- State Key Laboratory for GeoMechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China;
| | - Ning Wang
- School of Civil Engineering, Southeast University, Nanjing 211189, China; (Q.J.); (N.W.); (L.H.)
- Institute of Future Underground Space, Southeast University, Nanjing 211189, China
| | - Lei He
- School of Civil Engineering, Southeast University, Nanjing 211189, China; (Q.J.); (N.W.); (L.H.)
- Institute of Future Underground Space, Southeast University, Nanjing 211189, China
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16
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A Critical Study on Stability Measures of Feature Selection with a Novel Extension of Lustgarten Index. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2021. [DOI: 10.3390/make3040038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Stability of feature selection algorithm refers to its robustness to the perturbations of the training set, parameter settings or initialization. A stable feature selection algorithm is crucial for identifying the relevant feature subset of meaningful and interpretable features which is extremely important in the task of knowledge discovery. Though there are many stability measures reported in the literature for evaluating the stability of feature selection, none of them follows all the requisite properties of a stability measure. Among them, the Kuncheva index and its modifications, are widely used in practical problems. In this work, the merits and limitations of the Kuncheva index and its existing modifications (Lustgarten, Wald, nPOG/nPOGR, Nogueira) are studied and analysed with respect to the requisite properties of stability measure. One more limitation of the most recent modified similarity measure, Nogueira’s measure, has been pointed out. Finally, corrections to Lustgarten’s measure have been proposed to define a new modified stability measure that satisfies the desired properties and overcomes the limitations of existing popular similarity based stability measures. The effectiveness of the newly modified Lustgarten’s measure has been evaluated with simple toy experiments.
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Wang R, Song Q, Liu Z, Ma H, Gupta MK, Liu Z. A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts. SENSORS 2021; 21:s21175779. [PMID: 34502670 PMCID: PMC8434337 DOI: 10.3390/s21175779] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/19/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023]
Abstract
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.
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Affiliation(s)
- Runqiong Wang
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
| | - Qinghua Song
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
- Correspondence:
| | - Zhanqiang Liu
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
| | - Haifeng Ma
- Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China; (R.W.); (Z.L.); (H.M.)
- National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250061, China
| | - Munish Kumar Gupta
- Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland;
| | - Zhaojun Liu
- School of Information Science and Engineering, Shandong University, Qingdao 266237, China;
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Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11157149] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed between the normal and the pathological voice signals for normalized skewness (p = 0.000) and kurtosis (p = 0.000), except for normalized kurtosis (p = 0.051) that was estimated in the /u/ samples in women. These parameters are useful and meaningful for classifying pathological voice signals. The highest accuracy, 82.69%, was achieved by the CNN classifier with the LPCCs parameter in the /u/ vowel in men. The second-best performance, 80.77%, was obtained with a combination of the FNN classifier, MFCCs, and HOSs for the /i/ vowel samples in women. There was merit in combining the acoustic measures with HOS parameters for better characterization in terms of accuracy. The combination of various parameters and deep learning methods was also useful for distinguishing normal from pathological voices.
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