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Pinning boundary sampled-data synchronization of coupled reaction–diffusion neural networks

Neurocomputing 2025年 648卷

作者： Zi-Peng Wang Bo-Ming Chen Junfei Qiao Biao Luo Huai-Ning Wu Tingwen Huang Guangwei Chen School of Information Science and Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System and Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing 100124 China School of Automation Central South University Changsha 410083 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen 518107 China

In this paper, the pinning boundary sampled-data (SD) synchronization problem of coupled reaction–diffusion neural networks (CRDNNs) with SD-based couplings is investigated under boundary and distributed SD measurements. First, a pinning boundary SD controller is designed to effectively address the synchronization problem of CRDNNs, utilizing boundary and distributed SD measurements and requiring only a small number of sensors and actuators. Then, linear matrix inequalities provide adequate conditions that ensure exponential stability of the synchronization error system by using the designed pinning boundary SD controller, derived through inequality techniques and Lyapunov functionals. Finally, we present a numerical example to showcase the practical applicability and effectiveness of the proposed pinning boundary SD synchronization method.

关键词： Lyapunov functions

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Optimization and economic assessment of renewable energy sources and their combinations for Central Europe

Optimization and economic assessment of renewable energy sou...

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23rd International Conference on Circuits, Systems, Communications and Computers, CSCC 2019

作者： Skovajsa, Jan Zálešák, Martin Tomas Bata University in Zlin Faculty of Applied Informatics Department of Automation and Control Engineering Nad Stranemi 4511 Zlin Czech Republic

The article deals with the economic evaluation of investment and optimization of the renewable energy sources for family houses. For example, from the point of view of solar systems, the optimal solution is based on the specific application of it. The design is dependent on the location of the panels and ration between active aperture area and real daytime consumption. Common calculations according to actual standards often give overstated results, which also reflected in the value of the investments. The article shows the research of optimal parameters of the thermal solar system for preparing of domestic hot water. A combination of related standards and software TRNSYS are used to find optimal parameters. Thanks to created and verified simulation models, it is possible to design parameters so as to avoid under-dimensioning or over-dimensioning of the each system. Energy price is another factor affects the payback period of investments. This is affected by the used energy sources and their combination. For example, buildings that use electricity to heat water or heating have different energy charges than a building that uses natural gas. So, the aim is to find optimal solution of the combination of renewable energy sources. © 2019 The Authors, published by EDP Sciences.

关键词： Investments

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Energy management strategy for HEV based on KFCM and neural network

Energy management strategy for HEV based on KFCM and neural ...

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作者： Wang, Yeqin Wu, Zhen Xia, Aoyun Guo, Chang Chen, Yuyan Yang, Yan Tang, Zhongyi Faculty of Automation Huaiyin Institute of Technology Huaian China School of Mechanical Engineering Southeast University Nanjing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

Aiming at the deficiency of optimal control energy management strategy, a model of energy management controller for hybrid electric vehicle (HEV) is constructed based on Kernel Fuzzy C-means Clustering (KFCM) and multi-neural network. Using energy management control strategy based on PMP, the operational parameters of the four driving modes for HEV is extracted;the data cluster corresponding to the driving mode is generated by clustering through the KFCM method and is used as the training samples for the feedforward neural network. Taking the battery SOC, needed power and speed as the inputs of neural network, and taking engine power as the output of neural network, four sub-neural network models are established. Taking the vehicle driving needed power at the current moment and the engine output power at the previous moment as characteristic parameters, the corresponding sub-neural network model is selected for output prediction according to the proportional relationship between the driving demand torque and the engine output power. The simulation results show that, compared with the energy management strategy based on PMP, the calculation time is greatly shortened using the proposed control strategy, and the real-time performance is better. The fuel economy is a little decreased under the condition of meeting the requirements, but better dynamic performance can be obtained. © 2018 John Wiley & Sons, Ltd.

关键词： Energy management

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DTFFNet: A dual-branch time–frequency feature fusion network for remaining useful life prediction of mechanical equipment

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Mechanical Systems and Signal Processing 2025年 236卷

作者： Jiangyan Zhu Jun Ma Jiande Wu Lekang Fan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming 650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming 650500 China School of Engineering Yunnan University Kunming 650500 China School of Electronics and Information Technology (School of Microelectronics) Sun Yat-sen University Guangzhou 510006 China

With its exceptional feature extraction and modeling capabilities, deep learning has emerged as a cornerstone technology in the field of remaining useful life (RUL) prediction for mechanical equipment. However, time-domain-based deep learning methods face significant limitations in capturing global features, hindering their ability to fully model the complexities of equipment degradation processes. To address these challenges, we propose DTFFNet, a dual-branch time–frequency interactive integration framework that combines time-domain and frequency-domain signal analysis for efficient feature interaction and fusion. Specifically, the time-domain branch employs a CNN-Transformer structure to extract local features and model long-term dependencies within sequences. The frequency-domain branch utilizes a frequency-domain Multilayer Perceptron (MLP) to extract global dependencies from the real and imaginary components of Fourier-transformed signals. Additionally, a multi-head cross-attention mechanism is introduced to construct a time–frequency cross-domain interactive fusion module, enabling information exchange and complementary representation learning across time and frequency domains, enhancing feature fusion depth and effectiveness. To meet the specific demands of RUL prediction, a joint learning strategy is proposed that combines weighted and constrained MSE losses, enabling targeted optimization during critical degradation phases and along specific error directions to enhance prediction accuracy. Finally, experimental analysis on widely used turbofan engine dataset and PHM 2012 bearing dataset demonstrates that DTFFNet not only significantly improves prediction accuracy compared to mainstream RUL prediction methods but also exhibits enhanced robustness in noisy environments. These results establish DTFFNet as a reliable and effective solution for equipment health management in complex industrial settings.

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Region-reaching control for multiple underactuated Euler-Lagrange systems based on energy-shaping framework

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Communications in Nonlinear Science and Numerical Simulation 2025年 151卷

作者： Bin Zheng Jinchen Ji Runlong Peng Zhonghua Miao Jin Zhou School of Information Engineering Fujian Business University Fuzhou 350506 China Shanghai Institute of Applied Mathematics and Mechanics and Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai University Shanghai 200072 China Faculty of Engineering and IT University of Technology Sydney NSW 2007 Australia School of Mechatronic Engineering and Automation Shanghai University Shanghai 200072 China Shanghai Institute of Aircraft Mechanics and Control Tongji University Shanghai 200092 China

This paper presents a first attempt to address the distributed region-reaching control problems of multiple underactuated Euler-Lagrange systems (MUELSs) within an energy-shaping framework. An adaptive gain technique incorporating the region potential function is suitably introduced to design a distributed region-reaching control scheme by the best use of the passivity-based control (PBC) framework with damping injection. Additionally, the underactuated Euler-Lagrange (EL) dynamics are utilized to systematically integrate the energy of the system model and adaptive controller dynamics. Consequently, two independent region-reaching algorithms for MUELSs in the presence or absence of communication delays are analytically derived using appropriate Lyapunov functions. The overall region-reaching tracking cooperative performance, including stability, adaptability, and robustness, is validated through analysis and comparison of simulation examples.

关键词： Communication delays Energy-shaping framework Multiple underactuated Euler-Lagrange systems (MUELSs) Region-reaching control

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Weak Scratch Detection of Optical Components Using Attention Fusion Network

Weak Scratch Detection of Optical Components Using Attention...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Xian Tao Dapeng Zhang Avinash K Singh Mukesh Prasad Chin-Teng Lin De Xu Chinese Academy of Sciences Research Center of Precision Sensing and Control Institute of Automation Beijing China faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Scratches on the optical surface can directly affect the reliability of the optical system. Machine vision-based methods have been widely applied in various industrial surface defect inspection scenarios. Since weak scratches imaging in the dark field has an ambiguous edge and low contrast, which brings difficulty in automatic defect detection. Recently, many existing visual inspection methods based on deep learning cannot effectively inspect weak scratches due to the lack of attention-aware features. To address the problems arising from industry-specific characteristics, this paper proposes “Attention Fusion Network;”, a convolutional neural network using attention mechanism built by hard and soft attention modules to generate attention-aware features. The hard attention module is implemented by integrating the brightness adjustment operation in the network, and the soft attention module is composed of scale attention and channel attention. The proposed model is trained on a real-world industrial scratch dataset and compared with other defect inspection methods. The proposed method can achieve the best performance to detect the weak scratch inspection of optical components compared to the traditional scratch detection methods and other deep learning-based methods.

关键词： Conferences automation Computer aided software engineering

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Assess-Ranging-Fuse: Obstacle Detection Based on Stereo Vision Threat Assessment and Ultrasonic Ranging

Assess-Ranging-Fuse: Obstacle Detection Based on Stereo Visi...

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Chinese automation Congress (CAC)

作者： Yanhui Zhang Tehuan Chen Song Chen Zhisheng Wang College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, we propose a stability and reliability obstacle avoidance detection system (OADS) for the aggressive flight of autonomous quadrotors. We name our system as assess-ranging-fuse: ARF. In particular, we use the sliding window on the global depth map to determine the nearest obstacle information, instead of directly using the closest depth information, to avoid misjudgment of the environment due to aircraft shaking. Our ARF uses Federal Kalman Filter (FKF) to fusion threat assessment and ultrasound ranging in OADS. Among them, threat assessment by a stereo vision composed of two RGB cameras, the global sliding window to evaluate the threat distance and give the largest object's visual threat distance. Our system is integrated into the quadrotor for indoor/outdoor and dynamic/static experimental tests, and results show that this system can achieve stable global obstacle avoidance.

关键词： Voltage control

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MODELING AND FUZZY control OF PERMANENT MAGNET SYNCHRONOUS MOTORS

MODELING AND FUZZY CONTROL OF PERMANENT MAGNET SYNCHRONOUS M...

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The 10th International Conference on Power Electronics, Machines and Drives (PEMD 2020)

作者： D.-K. Jukić T. Varga T. Benšić V. J. Štil M. Barukčić Automation and Control Institute TU Wien Vienna Austria Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

This paper deals with Permanent Magnet Synchronous Motor modeling and control. The derived nonlinear dq model is a combination of Finite Element Method simulation data with the classical dq model. The so derived model keeps the precision of the Finite Element Method simulations and also the fast execution times of the dq model. The Finite Element Method data is included in the dq model using Artificial Neural Networks. The derived model is also used for controller design. The chosen controller is a PI like Fuzzy Logic controller, which is tuned using Genetic Algorithm optimization. The Permanent Magnet Synchronous Motor with Fuzzy Logic controller operation is tested using Real-Time (Model-in-the-Loop) simulations.

关键词： fuzzy control finite element analysis genetic algorithms neural nets permanent magnet motors machine control synchronous motors nonlinear control systems PI control electric machine analysis computing

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Super-k: A piecewise linear classifier based on Voronoi tessellations

arXiv

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arXiv 2020年

作者： Zengin, Rahman Salim Sezer, Volkan Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Autonomous Mobility Group Electrical and Electronics Engineering Faculty Istanbul Technical University Istanbul Turkey

Voronoi tessellations are used to partition the Euclidean space into polyhedral regions, which are called Voronoi cells. Labeling the Voronoi cells with the class information, we can map any classification problem into a Voronoi tessellation. In this way, the classification problem changes into a query of just finding the enclosing Voronoi cell. In order to accomplish this task, we have developed a new algorithm which generates a labeled Voronoi tessellation that partitions the training data into polyhedral regions and obtains interclass boundaries as an indirect result. It is called Supervised k-Voxels or in short Super-k. We are introducing Super-k as a foundational new algorithm and opening the possibility of a new family of algorithms. In this paper, it is shown via comparisons on certain datasets that the Super-k algorithm has the potential of providing comparable performance of the well-known SVM family of algorithms with less complexity. Copyright © 2020, The Authors. All rights reserved.

关键词： Piecewise linear techniques

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