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2023 International Conference on computer, Vision and intelligent Technology, ICCVIT 2023

作者： Lin, Anping Shi, Yaoting Zhang, Wenfen Liu, Dong Lan, Yulin School of Physics and Electronic Electrical Engineering Xiangnan University Chenzhou423000 China School of Computer and Artificial Intelligence Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou China

ISBN: (纸本)9798400708701

Salp swarm algorithm (SSA) divides the population into leaders and followers, and employs two different search methods to achieve steady convergence. However, the search methods adopted by SSA are relatively simple, hence SSA always trapped by local optima on complex multimodal problems. To improve the exploration of SSA, this paper proposes a multi-food source differential salp swarm algorithm (MFD-SSA) by transferring multi-leader mechanism and differential mutation strategy into SSA. MFD-SSA regards a portion of high fitness individuals as food sources and employ them for updating the position of leaders, the differential evolution mutation and the chaotic crossover are adopted to enhance swarm diversity. Then, the differential mutation and crossover operator are employed for enlarging search range. The MFD-SSA is tested on CEC2017 test suite and compared with five SSA variants and five other meta-heuristics. The test results indicate that MFD-SSA bears strong exploration, it outperforms five SSA variants and five meta-heuristics. On Planetary gear train design problem, MFD-SSA performs better than other five SSA variants too. © 2023 ACM.

关键词： Evolutionary algorithms

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Behavioral Decision-Making of Mobile Robots Simulating the Functions of Cerebellum, Basal ganglia and Hippocampus

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1639-1650页

作者： Wang, Dongshu Liu, Qi Duan, Yihai School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou450001 China Innovation Center of Intelligent Systems Longmen Laboratory Henan Luoyang471000 China State Key Laboratory of Intelligent Agricultural Power Equipment Henan Luoyang471004 China China Information Technology Design and Consulting Institute Co. Ltd. Henan Zhengzhou450007 China

In unknown environments, behavioral decision-making of mobile robots is a crucial research topic in the field of robotics applications. To address the low learning ability and the difficulty of learning from the unknown environments for mobile robots, this work proposes a new learning model which integrates the supervised learning of the cerebellum, reinforcement learning of the basal ganglia, and memory consolidation of the hippocampus. Firstly, to reduce the impact of noise on inputs and enhance the network's efficiency, a multi-neuron winning strategy and the refinement of the top-k competition mechanism have been adopted. Secondly, to increase the network's learning speed, a negative learning mechanism has been designed, which allows the robot to avoid obstacles more quickly through weakening the synaptic connections between error neurons. Thirdly, to enhance the decision ability of cerebellar supervised learning, simulating the hippocampal memory consolidation mechanism, memory replay during the agent's offline state enables autonomous learning in the absence of real-time interactions. Finally, to better adjust the roles of cerebellar supervised learning and basal ganglia reinforcement learning in robot behavioral decision-making, a new similarity indicator has been designed. Simulation experiments and real-world experiments validate the effectiveness of the proposed model in this work. © 2020 IEEE.

关键词： Mobile robots

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A Deep Encoder-Decoder Based Primal-dual Proximal Network for Image Restoration 15

A Deep Encoder-Decoder Based Primal-dual Proximal Network fo...

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Wang, Siqi Jiu, Mingyuan Chen, Li Li, Shupan Xu, Mingliang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450002 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Zhengzhou450002 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450002 China National Supercomputing Center in Zhengzhou Zhengzhou450002 China

ISBN: (纸本)9781510675018

Image restoration is a popular and challenge task, which is regarded as a classical inverse problem. Condat-Vu primal-dual algorithm based on proximal operator is one of successful optimization methods. It is further reformulated as a primal-dual proximal network, where one iteration in the original algorithm corresponds to one layer in the network. The drawback of primal-dual network is that blur kernels should be given as prior information, however, it is usually very hard to be known in the real situation. In this work, we propose a deep encoder-decoder primal-dual proximal network, named ED-PDPNet. In each layer, the blur kernels and the projections between the primal and dual variables are designed as encoder-decoder modules, in this way, the network can be learned in an end-to-end way and all the parameters in the primal-dual algorithm are learned. The proposed method is applied on the MNIST and BSD68 datasets for image restoration. The preliminary results show that the proposed method by combining simple encoder-decoder modules obtained very promising and competitive performance compared to the state-of-the-art methods. In addition, the proposed network is shown to be a lightweight network with fewer learning parameters in comparison to the recent popular transformer-based method. © 2024 SPIE. All rights reserved.

关键词： Image reconstruction

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel Robot 11

Experimental Study on Motion Controller based on NN-ARX and ...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Jalali, Hasan Damavandi, Hossein Kalhor, Ahmad Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran University of Tehran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers. © 2023 IEEE.

关键词： Controllers

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Survey on Cryptoprocessors Advances and Technological Trends 3rd

Survey on Cryptoprocessors Advances and Technological Tren...

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3rd International Conference on Innovations in Computing Research, ICR 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Paphos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031655210

Cryptoprocessors play a pivotal role in enhancing the security of modern computing systems by accelerating cryptographic operations and fortifying data protection. This survey delves into the world of cryptoprocessors, encompassing their historical development, architectural features, and various types based on their applications. The paper explores the crucial security considerations surrounding cryptoprocessors, covering both physical and logical aspects, as well as potential threats and countermeasures. Furthermore, the survey navigates through the latest technological trends and advancements in cryptoprocessor design. This includes discussions on post-quantum cryptography, secure enclaves, and mitigating side-channel attacks, all of which are shaping the future of cryptoprocessors. Real-world use cases and applications in domains such as secure communications, the Internet of Things (IoT), and data centers are presented, showcasing the practical significance of cryptoprocessors. However, the paper also highlights the persistent challenges faced by cryptoprocessor designers and provides insights into future research directions aimed at addressing emerging security and performance issues. As the digital landscape continues to evolve, the role of cryptoprocessors becomes increasingly vital in safeguarding sensitive information and fortifying the foundations of cybersecurity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Context Mutual Evolution Network for Weakly Supervised Surface Defect Detection 27th

Context Mutual Evolution Network for Weakly Supervised Surf...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Xiaoheng Xiao, Penghui Yan, Feng Lu, Yang Jin, Shaohui Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou450001 China

ISBN: (纸本)9783031781919

Weakly supervised object detection methods have achieved great success in natural scenes. However, they confront challenges such as small and weak appearances when detecting surface defects in industrial scenes. To address this problem, we propose a global-local Context Mutual Evolution Network (CMENet) for weakly supervised surface defect detection. CMENet is a dual-branch network consisting of a CNN branch and a Transformer branch, in which a context mutual evolution (CME) module is introduced in multiple blocks to enhance the feature representation ability of the network through the global and local information interaction. The CME module helps the network focus on defective areas and suppress background interference via a Feature Cluster Attention (FCA) module. The FCA adaptively selects some meaningful tokens as keys and values by a learnable clustering module to calculate attention. Extensive experiments on the DAGM 2007, KolektorSDD2, and Magnetic Tile defect datasets demonstrate that our method achieves promising performance compared with other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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Recent Developments in Event-Triggered Control of Nonlinear systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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An Improved Method for Approximating the Infinite-Horizon Value Function of the Discrete-Time Switched LQR Problem

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Journal of systems Science & Complexity 2024年第1期37卷 22-39页

作者： HOU Tan LI Yuanlong LIN Zongli Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904-4743USA

This paper considers the problem of approximating the infinite-horizon value function of the discrete-time switched LQR *** particular,the authors propose a new value iteration method to generate a sequence of monotonically decreasing functions that converges exponentially to the value *** method facilitates us to use coarse approximations resulting from faster but less accurate algorithms for further value iteration,and thus,the proposed approach is capable of achieving a better approximation for a given computation time compared with the existing *** numerical examples are presented in this paper to illustrate the effectiveness of the proposed method.

关键词： Optimal control switched linear systems switched LQR problem.

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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