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SSRN 2023年

作者： Giannella, Venanzio Bardozzo, Francesco Postiglione, Alberto Tagliaferri, Roberto Sepe, Raffaele Armentani, Enrico Department of Industrial Engineering University of Salerno via Giovanni Paolo II SA Fisciano132 Italy Department of Management and Innovation Systems University of Salerno Via Giovanni Paolo II SA Fisciano132 Italy Department of Chemical Materials and Production Engineering University of Naples Federico II Piazzale V. Tecchio 80 Napoli80125 Italy

Crack propagation analyses are fundamental for all mechanical structures for which safety must be guaranteed, e.g. as for the aviation and aerospace fields. The estimation of life for structures in presence of defects is a process inevitably affected by numerous and unavoidable uncertainty and variability sources, whose effects need to be quantified to avoid unexpected *** this work, residual fatigue life prediction models have been created through neural networks for the purpose of performing probabilistic life predictions of damaged structures in real-time and under stochastically varying input parameters. In detail, five different neural network architectures have been compared in terms of accuracy, computational runtimes and minimum number of samples needed for training, so to determine the ideal architecture with the strongest generalization power. The networks have been trained, validated and tested by using the fatigue life predictions computed by means of simulations developed with finite element and Monte Carlo methods. A real-world case study has been presented to show how the proposed approach can deliver accurate life predictions even when input data are uncertain and highly *** demonstrated that the "H1-L1" neural network has been the best model, achieving an accuracy (Mean Square Error) of 4.8e-7 on the test dataset, and the best and the most stable results when decreasing the amount of data. Additionally, since requiring only very few parameters, its potential applicability for Structural Health Monitoring purposes in small cost-effective GPU devices resulted to be attractive. © 2023, The Authors. All rights reserved.

关键词： Structural health monitoring

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Expression of Concern for: Prediction of Coronary Artery Disease by Adapting Hybrid Approach of Machine Learning Methods

Expression of Concern for: Prediction of Coronary Artery Dis...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Vijay Parthasarthy L. Pallavi Harishchander Anandaram M. Praveen S. Arun R. Krishnamoorthy Chemical Engineering - Energy Cluster School of Engineering University of Petroleum and Energy Studies Dehradun Department of CSE B V Raju institute of technology Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Department of ECE Saveetha Engineering college Chennai India Department of ECE Jerusalem College of Engineering Chennai India Centre for Nonlinear Systems Chennai Institute of Technology Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497657

Recently, there have been many implementations of AI and its application tactics in the field of medicine, and it is commonly mentioned as a substantial rich data. One of the major causes of death from one end of the world to the other is coronary artery disease, which can be prevented with early diagnosis. The goal of this work is to use reliable clinical data to predict coronary course infection. Expecting Coronary Artery Disease (CAD) is a very challenging and challenging undertaking in the clinical profession. One of the virtuosi in the clinical field is the early forecast, especially in the cardiovascular region.. The earlier studies on the creation of the early forecast model encouraged an understanding of the new approaches to find the variation in clinical imaging. An eating plan graph prepared by the concerned doctor following early anticipation might satisfy the cardiovascular counteraction. Our exam paper includes a forecast based on a suggested computation created using a pooling region bend AI technology. This data-based ID is a crucial element for accurate expectation. Despite the weak pixels around it, this extensive methodology has a respectable impact on deciding variety in clinical images. With the help of vein halting and vein plaque, this pooling region development in our AI calculation is storing contracting veins and tissues. The new flexible picture-based grouping strategies are presented in this investigation piece, which also contrasts the current characterization techniques with anticipated CAD previous for a higher exact worth. This suggested method uses any prior cardiac ailment as evidence to draw a conclusion. In our suggested calculation, the decision-production of grouped yield yields more precise results.

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Centralized Cloud Predictive control of Multiple Lighting systems

Centralized Cloud Predictive Control of Multiple Lighting Sy...

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第41届中国控制会议

作者： Chuanguo Chi Clara-Mihaela lonescu Ricardo Cajo Guo-Ping Liu School of Astronautics Harbin Institute of Technology Ghent University Research group on Dynamical Systems and Control Department of Electronic and Electrical Engineering Southern University of Science and Technology

This paper studies the application of centralized cloud predictive control based on dSPACE system in multiple lighting system control.A centralized cloud predictive control scheme is designed to achieve the consistency and stability of multiple lighting control at the same *** dSPACE multiple lighting control system is corrected and *** design of cloud prediction controller based on dSPACE system is introduced in *** cloud predictive control scheme is analyzed through simulation experiments,and the effective parameters of stability and consistency of closed-loop multiple lighting control system are *** effectiveness of the proposed scheme on the dynamic behavior and control performance of multiple lighting system is *** research results provide a foundation for multiple lighting cooperative control and its ***,the stability and consistency of the controller are verified by the physical experiment platform based on dSPACE.

关键词： Multiple lighting control dSPACE controller cloud predictive control consensus and stability

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Chinese Automation Congress (CAC)

作者： Yibo Wu Shuai Li Zijun Li Shijie Zhu Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Hongta Liaoning Tobacco Co. Ltd Shenyang China College of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

As industrial systems expand in scale and production processes grow more complex, the safety and quality of products have become increasingly important. Consequently, fault detection and diagnosis have taken on greater significance. Traditional fault detection and diagnosis methods rely heavily on mathematical modeling, while data-driven approaches such as Partial Least Squares (PLS) have gained prominence with advancements in information and automation technologies. The impact of faults on product quality and Variable Operating Conditions (VOC) bring challenges to the industrial applications of fault detection and diagnosis methods. This review summarizes recent data-driven methods for quality-related and VOC scenarios. Firstly, we summarized recent quality-related fault detection and diagnosis methods centered around PLS. Then, we categorized recent fault detection and diagnosis researches for VOC scenarios and highlighted representative studies. Following this, we delved into specific scenarios to explore quality-related fault detection and diagnosis for VOC scenarios. Finally, on the basis of the remaining issues and challenges in quality-related fault detection and diagnosis for VOC scenarios, we discussed and envisioned future research directions.

关键词： Fault diagnosis Automation Reviews Fault detection Production Product design Mathematical models Safety Quality assessment

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Robustifying Event-Triggered control to Measurement Noise

arXiv

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arXiv 2022年

作者： Scheres, Koen J.A. Postoyan, Romain Maurice Heemels, W.P.H. The Control Systems Technology Section Department Mechanical Engineering Eindhoven University of Technology Netherlands The Université de Lorraine CNRS CRAN NancyF-54000 France

While many event-triggered control strategies are available in the literature, most of them are designed ignoring the presence of measurement noise. As measurement noise is omnipresent in practice and can have detrimental effects, for instance, by inducing Zeno behavior in the closed-loop system and with that the lack of a positive lower bound on the inter-event times, rendering the event-triggered control design practically useless, it is of great importance to address this gap in the literature. To do so, we present a general framework for set stabilization of (distributed) event-triggered control systems affected by additive measurement noise. It is shown that, under general conditions, Zeno-free static as well as dynamic triggering rules can be designed such that the closed-loop system satisfies an input-to-state practical set stability property. We ensure Zeno-freeness by proving the existence of a uniform strictly positive lower-bound on the minimum inter-event time. The general framework is applied to point stabilization and consensus problems as particular cases, where we show that, under similar assumptions as the original work, existing schemes can be redesigned to robustify them to measurement noise. Consequently, using this framework, noise-robust triggering conditions can be designed both from the ground up and by simple redesign of several important existing schemes. Simulation results are provided that illustrate the strengths of this novel approach. Copyright © 2022, The Authors. All rights reserved.

关键词： Closed loop systems

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Distributional Reinforcement Learning for Scheduling of chemical Production Processes

arXiv

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arXiv 2022年

作者： Mowbray, Max Zhang, Dongda Del Rio Chanona, Ehecatl Antonio Centre for Process Integration School of Chemical Engineering and Analytical Science The University of Manchester ManchesterM13 9PL United Kingdom Sargent Centre for Process Systems Engineering Department of Chemical Engineering Imperial College London LondonSW7 2AZ United Kingdom

Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficiently address production scheduling problems in the presence of uncertainty. We consider commonly imposed restrictions on these problems such as precedence and disjunctive constraints which are not naturally considered by RL in other contexts. Additionally, this work naturally enables the optimization of risk-sensitive formulations such as the conditional value-at-risk (CVaR), which are essential in realistic scheduling processes. The proposed strategy is investigated thoroughly in a parallel batch production environment, and benchmarked against mixed integer linear programming (MILP) strategies. We show that the policy identified by our approach is able to account for plant uncertainties in online decision-making, with expected performance comparable to existing MILP methods. Additionally, the framework gains the benefits of optimizing for risk-sensitive measures, and identifies online decisions orders of magnitude faster than the most efficient optimization approaches. This promises to mitigate practical issues and ease in handling realizations of process uncertainty in the paradigm of online production scheduling. © 2022, CC BY.

关键词： Scheduling

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Distributed Event-Triggered Consensus control from Noisy Data Using Matrix Polytopes

Distributed Event-Triggered Consensus Control from Noisy Dat...

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IEEE Conference on Decision and control

作者： Yifei Li Wenjie Liu Jian Sun Gang Wang Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Department of Control Science and Engineering Tongji University Shanghai China

This paper presents a novel data-driven polytopic approach to event-triggered consensus control of unknown leader-following multi-agent systems (MASs). A distributed data-driven event-triggered consensus control protocol is proposed that utilizes noisy input-state data to enable all followers to track the leader while reducing communication and computational burden. Unlike previous research that relies on quadratic matrix inequalities to characterize system uncertainties, this paper devises a data-based polytopic representation for MASs, which enables addressing the consensus control problem without using explicit system matrices. Based on this representation, a data-based criterion is established, utilizing matrix polytopes to ensure the asymptotic stability of the closed-loop MAS. Moreover, a co-design method is presented for the distributed controller gain and the triggering matrix, using only data and expressed in terms of linear matrix inequalities. Finally, numerical simulations are conducted to demonstrate the validity and effectiveness of the proposed data-driven approach.

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Intelligent Technologies for Ecomonitoring Data Processing in Conditions of Technogenic Emergencies 4

Intelligent Technologies for Ecomonitoring Data Processing i...

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4th IEEE International Conference on Modern Electrical and Energy System, MEES 2022

作者： Bakharev, Volodymyr Perekrest, Andrii Chenchevoi, Volodymyr Chencheva, Olga Malyk, Svitlana Andrii, Domanetskyi Kremenchuk Mykhailo Ostrohradskyi National University Department of Civil and Labour Safety Geodesy and Land Management Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Computer Engineering and Electronics Kremenchuk Ukraine Kremenchuk Mykhailo Ostrohradskyi National University Department of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine Kremenchug Medical College Named after V.I. Lytvynenko Kremenchuk Ukraine

ISBN: (纸本)9798350346831

The paper considers the importance of expanding the possibilities of environmental monitoring of environmental air parameters in the event of man-made emergencies. Software and hardware solutions have been developed for visualization and analysis of data from public environmental monitoring systems in normal and emergency situations using Power BI and Vaisala devices. The integration of various subsystems for measuring atmospheric air and microclimate indicators in a single computer system with a single software interface was carried out. © 2022 IEEE.

关键词： Data visualization

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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Chinese Automation Congress (CAC)

作者： Yanxing Bai Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene.

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Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach

arXiv

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arXiv 2022年

作者： Nie, Tong Qin, Guoyang Wang, Yunpeng Sun, Jian Department of Traffic Engineering Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University Shanghai201804 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Traffic speed is central to characterizing the fluidity of the road network. Many transportation applications rely on it, such as real-time navigation, dynamic route planning, and congestion management. Rapid advances in sensing and communication techniques make traffic speed detection easier than ever. However, due to sparse deployment of static sensors or low penetration of mobile sensors, speeds detected are incomplete and far from network-wide use. In addition, sensors are prone to error or missing data due to various kinds of reasons, speeds from these sensors can become highly noisy. These drawbacks call for effective techniques to recover credible estimates from the incomplete data. In this work, we first identify the issue as a spatiotemporal kriging problem and propose a Laplacian enhanced low-rank tensor completion (LETC) framework featuring both low-rankness and multi-dimensional correlations for large-scale traffic speed kriging under limited observations. To be specific, three types of speed correlation including temporal continuity, temporal periodicity, and spatial proximity are carefully chosen and simultaneously modeled by three different forms of graph Laplacian, named temporal graph Fourier transform, generalized temporal consistency regularization, and diffusion graph regularization. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging. By performing experiments on two public million-level traffic speed datasets, we finally draw the conclusion and find our proposed LETC achieves the state-of-the-art kriging performance even under low observation rates, while at the same time saving more than half computing time compared with baseline methods. Some insights into spatiotemporal traffic data modeling and kriging at the network level are provided as well. Copyright © 2022, The Authors. All rights reserved.

关键词： Kriging

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