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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jinghao Zhang Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Department of Automation Tsinghua University Beijing P. R. China

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.

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fMRI-based Brain Disease Diagnosis: A Graph Network Approach

TechRxiv

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TechRxiv 2023年

作者： Chen, Qi-Qi Gao, Yubing Tong, Wei Li, Ping Wu, Edmond Q. The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Nanjing University of Science and Technology Jiangsu210008 China The School of Biological Science and Medical Engineering Beihang University Beijing100191 China The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As a non-invasive brain imaging technology, functional magnetic resonance imaging (fMRI) provides a basic tool for brain functional network modeling and brain disease diagnosis. Problems, such as large number of parameters, low training efficiency, and poor interpretability, are encountered in mainstream models because of the high complexity of fMRI and brain networks. To solve these problems, a novel structure feature combined graph neural network (SFC-GNN) with a low number of parameters is proposed. In particular, SFC-GNN is composed of 1) the graph convolution layer of the brain region perception and 2) the node pooling layer of the graph structure feature (GSF). It also receives the sparse brain graph modeled by each subject’s fMRI as input. Especially, the GSF layer can select brain regions that are important for classification, thereby localizing all active regions related to brain disease. Moreover, a group network is constructed according to the correlation among subjects, and SFC-GNN can be extended further to a node classification model to achieve better diagnosis performance. The proposed method has been validated on the ABIDE and ADNI datasets, thereby showing the effectiveness of our proposed method in various experiments. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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Chinese Automation Congress (CAC)

作者： Lu Kang Ding Liu Yali Wu Guozheng Ping School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (纸本)9781665426480

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm.

关键词： Adaptation models Heuristic algorithms Crystals Production Probability Silicon Scheduling

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A Distributed Algorithm for Economic Dispatch Problem in Multi-energy systems

A Distributed Algorithm for Economic Dispatch Problem in Mul...

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IEEE Conference on Decision and control

作者： Jialan Huang Zhibin Wang Qimin Xu Shanying Zhu Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665436601

The multi-energy system (MES) takes into account different forms of energy such as electricity, heat, and gas, and coordinate the different processes from the side of supply to demand. It truly realizes the mutual complementation of multiple energy resources and can greatly improve the flexibility and efficiency compared to traditional energy systems. In this paper, we study the economic dispatch (ED) problem in the MES aiming to minimize both generation and pollution cost. A distributed method via the symmetric alternating direction multiplier method (symmetric-ADMM) is proposed to deal with non-convex factors caused by coupling between different energy sources. Through the dynamic consensus mechanism and the variational inequality, we prove that the proposed algorithm has the convergence rate of $\mathcal{O}\left( {\fracsystem{t}} \right)$. The effectiveness of the proposed algorithm is verified by simulations on the IEEE 24-bus system.

关键词： Couplings Supply and demand Pollution Costs Energy resources Heuristic algorithms Propagation losses

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Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system

Optimized Dispatching of Seawater Pumping-Storing/Wind/Solar...

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第40届中国控制会议

作者： Yuyu Liu Ding Liu Lu Kang National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Different power sources in Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system supply power to the island *** order to ensure the reliability of the power supplying and improve the economical efficiency of the island micro-grid system at the same time,the output of each power source in the island micro-grid system needs to be optimized and dispatched according to historical wind power and photo-voltaic *** this paper,the mathematical model of Seawater Pumping-Storing/Wind/Solar Hybrid Power Generating system is established,and the one-time cost,*** the joint system are taken as the objective *** immune particle swarm algorithm is used to figure out the optimal output of each power source in the system after considering the restrictions including power balance of the system,the power constraints of each generator set and the online power of wind power and *** example shows that the optimization result of improved PSO algorithm is better than the original one,and it can also achieve the optimized dispatching of the joint system economically and effectively.

关键词： Seawater pumped-storage power station IPSO Optimized Dispatching

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A Low Delay AES Round Transform Architecture Using Constant Matrix Multiplications Merging Technologies

A Low Delay AES Round Transform Architecture Using Constant ...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xinxing Zheng Xiaoqiang Zhang Fan Yang Mingyu Xu Han Yan College of Information Engineering Wuhu Institute of Technology Wuhu China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu China Engineering Research Center of Vehicle-Mounted Display Integration System Wuhu China College of Electrical Engineering Anhui Polytechnic University Wuhu China

ISBN: (纸本)9781665409858

This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplications (CMM) merging technologies. To reduce the area cost in hardware implementations, S-box/InvS-box is usually implemented with composite field arithmetic (CFA) technologies. In this paper, CMM in CFA-based S-box/InvS-box are further merged with constant coefficient multiplications in MixColumns/ InvMixColumns, which can also be expressed as CMM forms. By the merging, the delay of the hardware implementation of encryption round transform is reduced at the cost of slight area cost increasing, and both delay and area cost are reduced in hardware implementations of decryption round transform. Hardware complexities analysis indicates that our designs have less delay compared with previous works.

关键词： Industrial electronics Costs Design methodology Merging Transforms Hardware Delays

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Hybrid integrated modeling of thermal field temperature in Cz silicon single crystal growth process based on VMD-CNN-LSTM-ELM

Hybrid integrated modeling of thermal field temperature in C...

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第40届中国控制会议

作者： Yin Wan Ding Liu Junchao Ren National & Local Engineering Research Center of Crystal Growth Equipment and System Integration Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Czochralski（Cz） silicon single crystal growth is a dynamic time-varying process with complex physical changes,multi-phase coupling of multiple fields, model uncertainty and large hysteresis and nonlinearity, while mechanistic models based on a large number of assumptions are difficult to be applied practically. In order to establish a stable and reliable model of crystal growth process, this paper proposes a variational modal decomposition--convolutional neural network-long and short-term memory network-extreme learning machine（VMD-CNN-LSTM-ELM） nonlinear hybrid integrated modeling method based on the idea of data-driven modeling to construct a model between heater power and thermal field temperature. The model consists of feature extraction, deep learning network, and optimization algorithm. First, VMD is used to decompose the original data into multiple Intrinsic Mode Function（IMF） from low to high frequencies, preserving the variation characteristics of the signal at different frequencies. Second, for each eigenmode function sequence, CNN feature extraction and LSTM prediction are performed in turn. Then, ELM is used to nonlinearly integrate the prediction results of multiple sequences to obtain the final prediction output. Finally, the simulation experimental results based on industrial data show that the proposed VMD-LSTMELM based thermal field temperature has excellent prediction performance and generalization ability.

关键词： Cz silicon single crystal growth Data-driven modeling Thermal field temperature Hybrid integrated modeling

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Application of Digital Twin Technology in the Teaching of engineering Drawing Course

Application of Digital Twin Technology in the Teaching of En...

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Information Science and Education (ICISE-IE), International Conference on

作者： Runfeng Zhang Yuxin Tang Zhenzhong Liu Yanfang Sun Jianguo Wu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China School of Language and Culture Tianjin University of Technology Tianjin China Tianjin Hanhailanfan Marine Technology Co. Ltd. Tianjin China

As an advanced technology that integrates virtuality and reality, digital twin technology has shown a wide range of application prospects in various fields. This paper aims to explore the potential application of digital twin technology in the teaching of engineering drawing courses, to improve students' theoretical knowledge and practical ability. By combining digital twin technology with traditional engineering drawing teaching, students can carry out practical operations and simulations in a virtual environment to better understand and apply drawing concepts and principles. Besides, taking the assembly drawing of thread connection as an actual teaching case, the specific application of digital twin technology in theoretical and experimental teaching is explained, respectively. The results show that digital twin technology is conducive to improving students' interest in engineering drawing courses and training students' spatial imagination ability, which is of great significance to reforming drawing courses.

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A Decentralized Algorithm for Coordination Between Economic Dispatch and Demand Response in Multi-Energy systems

A Decentralized Algorithm for Coordination Between Economic ...

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IEEE Conference on Decision and control

作者： Zishun Liu Shanying Zhu Jinming Xu Cailian Chen Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665436601

In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constraints are nonconvex. To deal with this issue, we propose a linearization method to transform the coordination problem to a convex social welfare optimization one. Then a decentralized algorithm based on parallel Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking protocol is developed, where each agent could only make decisions based on information from their neighbors. Moreover, under the assumption of convexity, we show convergence of the algorithm estimates to the global optimal solution. Finally, a case study on the modified IEEE 14-bus network verifies the feasibility and effectiveness of our algorithm.

关键词： Economics Protocols Heuristic algorithms Conferences Transforms Convex functions Demand response

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