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Factor analysis thermography for defect detection of panel paintings

Quantitative InfraRed Thermography Journal 2023年第1期20卷 25-37页

作者： Liu, Kaixin Huang, Kai-Lun Sfarra, Stefano Yang, Jianguo Yi, Liu Yuan, Yao Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Active infrared thermography is an important non-destructive testing method used for revealing defect structures in materials. In many applications, thermographic data processing is necessary to extract defect features from a large number of thermal images. This work proposes to use a factor analysis thermography (FAT) method that automatically extracts defect features from thermograms via exploratory factor analysis, in tandem with a fuzzy c-means (FCM) clustering algorithm to segment the defects and background. By means of factor rotation, factor analysis minimises the complexity of factor loadings and makes the results more interpretable. Consequently, the defect information is extracted while large signal-to-noise ratios are obtained. Employing the FCM image segmentation algorithm on factor loading images reduces the interference of background on human visual detection. Additionally, the parameter selection is emphasised and addressed. Experiments on a panel painting illustrate that the proposed method promotes the accuracy and efficiency of thermographic detection of defects, compared with the popular principal component thermography (PCT) method. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Safety stabilization of switched systems with unstable subsystems

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control Theory and Technology 2022年第1期20卷 95-102页

作者： Lijun Long Yiguang Hong State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang110819LiaoningChina College of Information Science and Engineering Northeastern UniversityShenyang110819LiaoningChina Department of Control Science and Engineering Tongji UniversityShanghai201804China

This paper is concerned with the problem of safety stabilization for switched systems where the solvability of the problem under study for individual subsystems is not assumed.A new state-dependent switching strategy with guaranteed dwell-time for switched systems is constructed,and a sufficient condition for absence of Zeno behavior is ***,a novel switched control design method is proposed to simultaneously guarantee the safety of the switched closed-loop system and stabilize the system based on the union of a common barrier function and a single Lyapunov function,which effectively handles the conflict between safety and stability ***,two examples are presented to demonstrate the effectiveness of the proposed design approach.

关键词： Safety stabilization Switched systems Common barrier function Single Lyapunov function State-dependent switching

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EOG acquisition system based on ATmega AVR microcontroller

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16589-16605页

作者： Abdel-Samei, Abdel-Gawad A. Shaaban, Abdel-Salam Brisha, Ayman M. El-Samie, Fathi E. Abd Ali, Ahmed S. Department of Electronics Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Process of Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Department of Mechanical Engineering Mechatronics Division Faculty of Engineering Assiut University Assiut Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

This paper presents a hardware and software of an electrooculogram (EOG) acquisition system based on ATmega AVR microcontroller for the acquisition of vertical and horizontal eye movements. The presented system is used to acquire a dataset of eye movements for volunteers. This system gives two channels representing vertical and horizontal EOG signals. The frequency range of the EOG signal is known to be 0.1 to 10 Hz, and hence this frequency range is isolated with a High-Pass Filter (HPF) with a cutoff frequency of 0.1 Hz followed by a Low-Pass Filter (LPF) with a cutoff frequency of 10 Hz. The EOG acquisition system is interfaced with an ATmega AVR microcontroller to acquire a dataset that can be used for controlling hardware such as Light Emitting Diodes (LEDs), wheelchair, and robot arm. The presented system is composed of EOG signal acquisition, Ag/AgCl electrodes, analog-to-digital converter through Arduino Mega 2560 board microcontroller unit, trainer board, laptop, keypad, and Liquid Crystal Display (LCD). The eye movement is detected by measuring the potential difference between cornea and retina using five Ag–Agcl disposable electrodes. Different volunteers of different ages at different times have been treated with the presented system to obtain data. Classified vertical and horizontal EOG signals and the basic eye movements e.g., open eye, left, right, up, and down can be used to control robots and wheelchairs for rehabilitation purposes. © 2023, The Author(s).

关键词： Electrodes

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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Adaptive Deep Reinforcement Learning for Critical Boundary Scenario Generation 43

Adaptive Deep Reinforcement Learning for Critical Boundary S...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Junjie Wang, Lin Wang, Xiaofan Meng, Qiang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai202400 China Ministry of Education of China Key Laboratory of System Control and Information Process Shanghai202400 China National University of Singapore Department of Civil and Environmental Engineering Singapore117576 Singapore Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai202400 China

ISBN: (纸本)9789887581581

Critical boundary scenarios play a vital role in the comprehensive testing of autonomous vehicles, as they can significantly expedite testing processes and reduce economic and time costs. To tackle the intricate, multidimensional, and diverse nature of intelligent driving scenarios, this paper proposes an innovative bi-level adaptive deep reinforcement learning (BADRL) approach aimed at generating authentic and diverse critical boundary scenarios. Leveraging naturalistic driving data, background agents are trained to exhibit naturalistic and realistic driving behaviors using a neural-based naturalistic driving behavior model. To address the intricacies of multi-interaction, high-dimensional environments, the scenario complexity model is introduced to evaluate the relative complexity between the traffic environment and the tested autonomous vehicle in real time. By integrating the scenario complexity model, the naturalistic driving behavior learning, intelligent driving testing, and critical boundary scenario generation are concatenated together to form a closed loop. BADRL facilitates the upper-level neural network in learning to adaptively increase the complexity of test scenarios, which are then fed into lower-level models to optimize the behavior of principal traffic participants, thereby generating naturalistic and critical boundary test scenarios. Extensive simulations were carried out to verify the efficacy of the BADRL technique in complex intersection environments. Results indicate that the BADRL approach boosts the efficiency of critical boundary scenario generation by approximately 10% compared to the state-of-the-art method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deep reinforcement learning

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A Markov Chain Monte Carlo approach for Pseudo-Input selection in Sparse Gaussian processes

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IFAC-PapersOnLine 2023年第2期56卷 10515-10520页

作者： Anna Scampicchio Sanjay Chandrasekaran Melanie N. Zeilinger Institute for Dynamic Systems & Control Department of Mechanical & Process Engineering ETH Zürich Department of Information Technology & Electrical Engineering ETH Zürich

The effectiveness of non-parametric methods for regression comes at the price of high computational complexity. In fact, these methods scale as O(N 3 ), where N is the number of available data points. One possible option to address this issue consists in introducing a set of fictitious (pseudo-) inputs of size M ≪ N such that the computational effort is reduced to O(M 2 N). To estimate hyper-parameters and pseudo-inputs, a non-convex optimization problem needs to be solved. As opposed to the conventional gradient-based approach used in the literature, this paper proposes a stochastic scheme leveraging Markov Chain Monte Carlo methods. Numerical comparisons show that the latter returns a more efficient set of pseudo-inputs, leading to a superior performance in terms of mean squared error.

关键词： Non-parametric Methods Bayesian Statistics stochastic simulation Monte Carlo Methods

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Distributed Dimensionality Reduction Filtering for CPSs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1080-1082页

作者： Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)*** each sensor exchanging measurements with neighbors subjecte... 详细信息

关键词： DoS service filtering

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Slow Feature Thermography for Non-Destructive Evaluation of Defects in Composite Materials 13

Slow Feature Thermography for Non-Destructive Evaluation of ...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Liu, Kaixin Zhong, Xin Chen, Ping Dai, Yun Liu, Yi North University of China The State Key Lab for Electronic Testing Technology China North University of China Department of Information and Communication Engineering Taiyuan030051 China College of Mechanical Engineering Nantong Vocational University Nantong226007 China Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China

ISBN: (纸本)9798350361674

In recent years, various methods for processing and analyzing thermographic data have been applied in non-destructive testing to improve the visibility of defects. Representatively, traditional linear dimensionality reduction methods (e.g. principal component analysis) have achieved good results in thermographic data analysis. In this work, a slow feature thermography (SFT) method for non-destructive evaluation of defects in composite materials. Thermographic data can be regarded as a continuous time series generated by an infrared imager at different moments. Slow feature analysis algorithm is subsequently used to extract slow features of the thermal image sequence. Slow features refer to features that are unchanged or slowly changing in time-series data, such as the background of a thermal image. By extracting the slow features of data, the inhomogeneous background, noise and defect features contained in thermal images can be effectively distinguished. Test results on carbon fiber reinforced polymer specimens demonstrate the feasibility and effectiveness of the SFT method. © 2024 IEEE.

关键词： Carbon fiber reinforced plastics

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Numerical modeling of droplets injection in the secondary flow of the wet steam ejector in the refrigeration cycle

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INTERNATIONAL JOURNAL OF REFRIGERATION 2022年 136卷 103-113页

作者： Dadpour, Daryoush Lakzian, Esmail Gholizadeh, Mohammad Ding, Hongbing Han, Xu [a]Center of Computational Energy Department of Mechanical Engineering Hakim Sabzevari University Sabzevar Iran [b]Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin 300072 China [c]Key Lab of Condition Monitoring and Control for Power Plant Equipment North China Electric Power University Ministry of Education Baoding 071003 China

Steam ejector plays a key role in the refrigeration system. The water has been used as a working fluid because water is more economical and non-toxic. The operating fluid often enters the evaporator as a water liquid and droplets evaporate by absorbing heat. Cooling load changing causes the vapor in the outlet of the evaporator to be accompanied by droplets. Therefore, there are droplets at the inlet of secondary nozzle and flow should be simulated as a wet steam flow with injection droplets at the secondary flow. In this paper, the effects of droplets injection at secondary flow have been investigated on the performance of the refrigeration cycle. For this purpose, the Eulerian-Eulerian method has been used and the results have been validated with experimental data. Six cases are considered with different wetness and number of droplets in the secondary flow. The results of case 6 show COP, T-outlet, and ER decrease about 22.93%, 15.66%, and 22.93%, respectively. In addition, the steam accompanies by droplets at the outlet of ejector. Therefore, it is essential for designers and operators to model and consider the effects of the droplets on the wall erosion to optimize the wet steam ejector and refrigeration cycle performance.

关键词： Wet steam ejector Refrigeration cycle Droplets injection COP ER

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A Semisupervised Soft-Sensor of Just-in-Time Learning With Structure Entropy Clustering and Applications for Industrial processes Monitoring

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 722-733页

作者： Li, Dong Liu, Yiqi Huang, Daoping Xu, Chong South China University of Technology Key Laboratory of Autonomous Systems and Networked Control Ministry of Education School of Automation Science and Engineering Guangzhou510640 China Technical University of Denmark Process and Systems Engineering Center Department of Chemical and Biochemical Engineering Lyngby2800 Denmark South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Gannan Normal University School of Physics and Electronic Information Ganzhou341000 China

To monitor industrial processes properly, soft-sensors are widely used to predict significant but difficult-to-measure quality variables. However, the prediction performances of traditional data-driven soft-sensors are usually unacceptable once suffering from high-nonlinear, high-dimension, and imblance data issues. Therefore, a semisupervised soft-sensor, which is learned by a just-in-time method with structure entropy clustering (SS-JITL-SEC), is proposed aiming to improve prediction performance with a simpler way. Inspired by a divide and conquer strategy, a novel SEC method is proposed to achieve several clusters and then to translate the highly complex and nonlinear modeling problems into simple and linear ones. Moreover, the training dataset is extended through a mixed SS labeling approach. Finally, dissimilarity-based JITL works together with the resulting clustering subdatasets to formulate a local adaptive prediction model. Two datasets from different types of wastewater treatment plants are used to verify the effectiveness of the proposed soft-sensor. The results show that the SS-JITL-SEC soft-sensor can achieve better prediction performance than other standard counterparts, and even for effective process monitoring with the resulted residuals. © 2020 IEEE.

关键词： Entropy

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