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共 359 条记录，以下是301-310 订阅

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Deep Metric learning with Impulsive Neural Network 13

Deep Metric Learning with Impulsive Neural Network

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Hong-Zhou Hu, Bin Chen, Long Guan, Zhi-Hong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Future Technology South China University of Technology Guangzhou510641 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9798350361674

Neural networks with siamese and triplet structures can discriminate between samples by explicitly learning the distance measure. But the dominant metric learning model based on deep neural networks (DNN) has issues with low energy efficiency and slow on-chip operating speed. Motivated by this, we put forth novel impulsive neural networks (INN) based deep metric learning approachs. the loss function is proposed based on the Euclidean distance between the membrane potentials of the neurons in the output layer of the sub-networks composed by Leaky integrate-and-fire (LIF) neurons. Furthermore, the back-propagation through time (BPTT) technique can be effectively implemented and utilized for network optimization through the surrogate functions. Experiments on multiple datasets show that the proposed model can learn the difference between images with performance very close to DNN-based models. At the same time, we conducted an intuitive energy consumption analysis of our model. the results show that compared with DNN-based models, the proposed model has higher energy efficiency. Besides, it only needs an extremely short time step T=10 to complete inference process, which makes the low-latency on-chip operation possible. © 2024 ieee.

关键词： Energy Efficiency Image Processing Impulsive Neural Network Metric learning

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Observer-Based Fault Tolerant Formation control of Multi-Agent systems via Adaptive Dynamic Programming 13

Observer-Based Fault Tolerant Formation Control of Multi-Age...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Yongwei Zhao, Bo Liu, Derong College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Systems Science Beijing Normal University Beijing100875 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798350361674

this paper develops a fault tolerant formation control scheme for multi-agent systems (MASs) by using adaptive dynamic programming. In MASs, there exist one leader and multiple followers, where the leader is a generated ideal trajectory and the followers catch up with the leader using appropriate designed control inputs based on their own and neighbors' information. To obtain accurate fault information, a fault observer is established for each follower. Subsequently, a discounted cost function, which includes the estimated fault, the formation error, and the control laws, is developed for each following agent. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each follower, a critic-only structure is built to obtain the approximate fault tolerant formation control law. Furthermore, it is proved that the uniform ultimate boundedness of the formation error of each following agent is guaranteed under the developed observer-based fault tolerant formation control law. It means that all following agents are able to maintain a specified formation and catch up with the leader. Finally, a simulation example is employed to validate the effectiveness of the present control approach. © 2024 ieee.

关键词： Cost functions

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Fault-Tolerant control for Electromagnetic Suspension System of Maglev Train: Comprehensive Research and Design 13

Fault-Tolerant Control for Electromagnetic Suspension System...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Liu, Junhao Dai, Chunhui Sun, Shangrongxi Wang, Hao Li, Xiaolong College of Intelligence Science and Technology National University of Defense Technology Hunan Changsha410073 China Hunan Provincial Key Laboratory of Electromagnetic Levitation and Propulsion Technology Hunan Changsha410073 China Northwest Institute of Electromechanical Technology Shanxi Xian712099 China

ISBN: (纸本)9798350361674

Maglev technology, as an advanced transit technology, have garnered global attention due to their remarkable high-speed performance, energy efficiency, environmental friendliness, and comfortable ride experience. they have been actively promoted and put into practical operation in numerous countries and regions worldwide. Given the complexity of magnetic levitation train systems and their sensitivity to external disturbances, enhancing their reliability and robust stability is of paramount importance. this paper takes the conventional electromagnetic suspension (EMS) maglev train developed by the National University of Defense Technology as a reference model, and systematically investigates the fault-tolerant control techniques for magnetic levitation trains based on accurate sensor fault detection and localization. the study encompasses the implementation of active fault tolerance through dynamic switching control strategies and sensor failures, as well as simultaneous passive fault-tolerant control schemes that exploit the inherent redundancy characteristics of the joining-structure within the magnetic levitation train system. Furthermore, the effectiveness and practicability of these fault-tolerant control methods were validated using a Digital Signal Processor (DSP) digital controller. the experimental results demonstrated that the summarized fault-tolerant control system meets the predefined performance criteria, thereby substantiating the validity and practicality of the relevant fault-tolerance control technologies. © 2024 ieee.

关键词： Magnetic levitation

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A Multi-stage Optimized Fault Diagnosis Model for Imbalanced Fault data in Manufacturing Process 11

A Multi-stage Optimized Fault Diagnosis Model for Imbalanced...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Lai, Zhouhao Dong, Yan Ren, Hongru Lu, Renquan Guangdong University of Technology School of Computer Science and Technology and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Fault diagnosis of equipment in the manufacturing process is particularly important, and it is an indispensable link in intelligent manufacturing. the fault data generated by the equipment during the manufacturing process is often imbalanced. However, modeling and training on imbalanced dataset will result in a very high rate of misclassification. In order to solve this problem, and improve the accuracy of fault diagnosis in the manufacturing process. this paper proposes a multi-stage optimizatied fault diagnosis model based on bayesian optimization, synthetic minority oversampling technique(SMOTE) and stagewise additive modeling using a multi-class exponential loss function(SAMME), namely the BSS model. the multi-stage optimized fault diagnosis model proposed in this paper improves the diagnosis accuracy of imbalanced fault dataset from the aspects of dataset processing, model building and model training. Finally, the results of ultrasonic flowmeter failure diagnosis show that among the various evaluation indicators, the multi-stage optimized fault diagnosis model proposed in this paper is better than the traditional single machine learning model. © 2022 ieee.

关键词： Failure analysis

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A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming 11

A Quality Prediction Hybrid Model of Manufacturing Process B...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Peng, Chong Cheng, Zhijian Ren, Hongru Lu, Renquan Guangdong University of Technology School of Computer Science and Technology and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the design of manufacturing parameters in the initial stage is backed by quality prediction to realise intelligent manufacturing. Accurate prediction translates to better quality, lower costs and more flexibility. However, the real production is a complicated and variable process, most of which involved multiple parameters simultaneously. the data on the basis of feature construction can filter the impurities of data, accuracy of predictive model can be satisfied. Existing approaches to provide results are useless when the insufficient mining of the relationship between the data or the some case without adequate manufacturing data and expertise. In this paper, a two-stage hybrid approach with genetic programming is proposed for quality prediction. the feature construction is realized by genetic programming in the first stage, and the new features are utilized as additives to subsequent stage of the extreme gradient boosting. the comparison experiments indicate that the two-stage hybrid model outperforms the existing methods in overall performance. © 2022 ieee.

关键词： Genetic programming

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Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction 13

Anti-Windup Active Power Control for Wind Turbines Consideri...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Guozhong Luo, Hao Jiao, Xuguo Yang, Zhibo Zhao, Xinxin School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. this control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement. © 2024 ieee.

关键词： Wind turbines

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data-driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And data Acquisition (SCADA) system. then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 ieee.

关键词： Cost functions

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data-driven Based Optimal Output-Feedback control of Continuous-Time systems 10

Data-Driven Based Optimal Output-Feedback Control of Continu...

引用

10th International conference on Modelling, Identification and control (ICMIC)

作者： Li, Zican Wu, Tao Na, Jing Zhao, Jun Gao, Guanbin Herrmann, Guido Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Yunnan Peoples R China Univ Bristol Dept Mech Engn Bristol BS8 1TR Avon England

ISBN: (纸本)9781538654163

In this paper, we propose a novel method to solve the optimal output-feedback control problem of continuous-time (CT) linear systems based on a data-driven based reinforcement learning (RL). An output-feedback Riccati equation is first derived by further tailoring its counterpart of state-feedback optimal control. then, based on this modified Riccati equation, we further derive an output Lyapunov function, where only the system output rather than the unknown state is involved. this allows to obtain the optimal output-feedback gain based on the measured output only. then, an online data-driven based policy iteration is suggested to obtain the feedback gain K and matrix P. Finally, a simulation example is given to prove the effectiveness of the proposed algorithm.

关键词： Optimal control Output-feedback control data-driven Policy iteration Riccati equation

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An Adaptive Machine learning-based threat Detection Framework for Industrial Communication Networks 10

An Adaptive Machine Learning-based Threat Detection Framewor...

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10th ieee International conference on Communication systems and Network Technologies, CSNT 2021

作者： Bebortta, Sujit Singh, Sumanta Kumar Department of Computer Science and Engineering College of Engineering and Technology Bhubaneswar751003 India

ISBN: (纸本)9780738105239

the development in sophisticated sensory devices has largely revolutionized industrial control systems (ICS). With the popularity of the fourth industrial revolution, which is also referred to as Industry 4.0, more large-scale industrial communication systems are being interconnected by leveraging information communication technology (ICT) paradigms. these new paradigms have resulted in a sustainable improvement in the conventional production systems by facilitating timely and enhanced production rates. the ICS embeds service oriented architecture (SOA), which comprise of supervisory control and data acquisition (SCADA) systems as its backbone. However, such systems have often fallen prey to cyber-attacks leading to temporary failures or complete physical damage to the ICS. In this perspective, this work suggests the adoption of particle swarm optimization (PSO) method and artificial neural network (ANN) model for identifying cyber-attacks associated with ICS. this work considers the gas pipeline control system data to assess the efficacy of the proposed approach in conjunction with other models. It is observed that the proposed approach provides a prediction accuracy of 98.87 % and respective precision and recall values of 97.80 % and 95.79 %. © 2021 ieee.

关键词： Machine learning

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Towards Safe Machine learning for CPS Infer Uncertainty from Training data 19

Towards Safe Machine Learning for CPS Infer Uncertainty from...

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10th ACM/ieee International conference on Cyber-Physical systems (ICCPS)

作者： Gu, Xiaozhe Easwaran, Arvind Nanyang Technol Univ Energy Res Inst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Entineering Singapore Singapore

ISBN: (纸本)9781450362856

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant benefits brought by ML techniques, they also raise additional safety issues because 1) most expressive and powerful ML models are not transparent and behave as a black box and 2) the training data which plays a crucial role in ML safety is usually incomplete. An important technique to achieve safety for ML models is "Safe Fail", i.e., a model selects a reject option and applies the backup solution, a traditional controller or a human operator for example, when it has low confidence in a prediction. data-driven models produced by ML algorithms learn from training data, and hence they are only as good as the examples they have learnt. As pointed in [17], ML models work well in the "training space" (i.e., feature space with sufficient training data), but they could not extrapolate beyond the training space. As observed in many previous studies, a feature space that lacks training data generally has a much higher error rate than the one that contains sufficient training samples [31]. therefore, it is essential to identify the training space and avoid extrapolating beyond the training space. In this paper, we propose an efficient Feature Space Partitioning Tree (FSPT) to address this problem. Using experiments, we also show that, a strong relationship exists between model performance and FSPT score.

关键词： Machine learning Safety Safe Fail

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