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IEEE Conference on Electromagnetic Field Computation

作者： Xuanda Hou Kazuya Kawai Hiroshi Dozono Kazuhiro Muramatsu Norihiro Ogishima Nguyen Gia Minh Thao Keisuke Fujisaki Yanhui Gao Weimin Guan Cuihua Tian Jiaxin Yuan Baichao Chen Department of Electrical and Electronic Engineering Saga University Saga Japan Electromagnetic Energy System Laboratory Toyota Technological Institute Nagoya Japan Department of Innovative Engineering Oita University Oita Japan School of Electrical Engineering and Automation Wuhan University Wuhan P. R. China

ISBN: (数字)9798350348958

ISBN: (纸本)9798350348965

In the inductors excited by the inverter using Gallium Nitride power devices, the ringing phenomenon, which occurs due to the multi-resonances during the short rise and fall time, increases noise and loss. The multi-resonances of the ring core inductor at high frequency (HF) can be represented by using the electromagnetic field finite element analysis (FEA) considering the stray capacitance of the winding in the frequency domain. However, it requires fine subdivision and expensive computation cost to consider the real structure of the winding in the FEA. To reduce the computation cost, a homogenized-winding model of the ring core inductor, in which one turn of wire with gap is represented by one element with equivalent conductivity and permittivity, is proposed and verified in this paper. It is shown that the HF characteristics of the ring core inductor obtained from the proposed homogenized-winding model are in good agreement with those obtained from the real-winding model with significantly reduced computation cost.

关键词： Analytical models Costs Computational modeling Windings Capacitance Finite element analysis High frequency

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Reinforcement Learning-Based Adaptive Search Strategy Combination Algorithm

Reinforcement Learning-Based Adaptive Search Strategy Combin...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiaotong Liu Zhiqiang Zeng Sicheng Wan Jiajie Tian Tianlei Wang Zhiyong Hong School of Mechanical and Automation Engineering Wuyi University Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China School of Electronic and Information Engineering Wuyi University Jiangmen China Guangdong Kejie Technology Co. Ltd. Jiangmen China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Integrating multiple search operators to leverage the characteristics of different search operators is one of the common methods to enhance the performance of evolutionary algorithms. Most of these combination algorithms adopt a fixed framework, which makes it difficult to adaptively select the appropriate search operator during the algorithm iteration process, resulting in poor performance on certain problems. To overcome the aforementioned shortcomings of combination algorithms, an adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search (RLASCA) is proposed. During the algorithm iteration process, a reinforcement learning-based adaptive search operator selection method (RLAS) is designed, enabling the algorithm to adaptively select the appropriate search operator based on the state of the individual. Furthermore, to address the issue of premature convergence, a neighborhood search strategy based on differential evolution (NSDE) is designed to increase the diversity of the population. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite and two constrained engineering design problems. The algorithm was compared with four basic algorithms that compose RLASCA and four classical combination algorithms. The experimental results indicate that RLASCA outperforms the other eight algorithms in terms of convergence speed and accuracy.

关键词： Distributed processing Accuracy Reinforcement learning Evolutionary computation Search problems Optimization Convergence Testing

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Precise LiDAR SLAM in Structured Scene Using Finite Plane and Prior Constraint

Precise LiDAR SLAM in Structured Scene Using Finite Plane an...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xie, Yuhui Zhao, Wentao Wang, Jiahao Wang, Jingchuan Chen, Weidong Institute of Medical Robotics and Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China School of Mechanical Engineering Shanghai Jiao Tong University Ministry of Education Shanghai200240 China

ISBN: (纸本)9781665481090

In this paper, we propose a precise LiDAR SLAM in optimization framework using plane-like object as landmark. Compared to general methods, finite plane feature is used to represent landmark and a new residual model is designed, making constraint from edge of landmark can be used to limit the parallel position between LiDAR and landmark, leading to a more accurate result. Moreover, floor plan is used to provide global pose for reduction of drift, and additionally feature orientation prior is used to prevent map distortion when updating the inaccurate part of floor plan. Experiments are conducted using data collected in real environment. The result qualitatively shows that proposed method can build a corrected map from floor plan without distortion, and quantitatively verifies that proposed method can outperform other baseline methods in accuracy. © 2022 IEEE.

关键词： Optical radar

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Robust controller Design for an Active Hexapod Platform with Prescribed Performance

Robust Controller Design for an Active Hexapod Platform with...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Rongji Yin Yi Shi Wei Xie Jinhui Lu Yu Wen Weixing Cheng Weidong Zhang Department of Automation Shanghai Jiao Tong University Shanghai China National Engineering Research Center of Ship & Shipping Control System School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

In this paper, we use a robust controller with prescribed performance for the Stewart-Gangway system, achieving motion compensation under unknown time-varying disturbances. The designed controller is able to drive the Stewart top platform towards an arbitrarily small neighborhood of a desired trajectory, with convergence speed no less than a prespecified value, and a maximum overshoot less than a sufficiently small preseted constant. To demonstrate the effictiveness of the designed control strategy, simulation results are given.

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Group Cooperation in Intergroup Conflicting Networks: An Evolutionary Game Approach

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IFAC-PapersOnLine 2023年第2期56卷 3459-3464页

作者： Aixin Liu Lin Wang Guanrong Chen Jing Wu Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Department of Electrical Engineering City University of Hong Kong Hong Kong China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China

Most intergroup conflicts arise from rivalry over limited resources, malicious disturbance, or hostile attitudes; therefore, it is critical to investigate individuals’ behaviors involved in intergroup conflicting contexts. Focusing on cooperation issues among individuals, in this study we establish an evolutionary game framework for analyzing cooperation and conflicts that arise within and inter-groups of intergroup conflicting networks. We first model the intergroup conflicting networked evolutionary games (ICNEGs). Then, we analyze the ICNEGs and prove that the evolution of ICNEGs can be expressed as a logical dynamic system. Finally, we apply the obtained results to a simplified Israeli-Palestinian conflict scenario. Our case study demonstrates that only by adopting suitable initial strategy profiles can a certain scale of group cooperation be continuously generated without suffering casualties.

关键词： Coupled networks intergroup conflicting cooperation evolutionary game approach

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Entity Representation Learning with Multimodal Neighbors for Link Prediction in Knowledge Graph 7

Entity Representation Learning with Multimodal Neighbors for...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Liu, Wenxuan Duan, Hao Li, Zeng Liu, Jingdong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management China

ISBN: (纸本)9781665409506

Entity representation learning is both fundamental and crucial for link prediction in knowledge graph. Existing entity representation learning approaches mainly focus on learning from knowledge triples with single modal text data which could only describe the things or concepts in words. However, multimodal data commonly exists in the real worlds and may describe things or concepts from different aspects. Therefore, multimodal entities have emerged in knowledge graph in recent years, which greatly enrich the knowledge graph by different information. This paper focuses on how to learn more robust and comprehensive representations of multimodal entities for better link prediction. A new representation learning method for multimodal entities is proposed, called Graph Attention with Bi-pooling Representation Learning (GBRL), where multimodal fusion and knowledge embedding are combined for learning representations of an entity with consideration of its multimodal neighbors. Firstly, feature vectors are obtained by pre-trained models for each modality. Then, the bilinear pooling is utilized for combining feature vectors of multimodal entities into multimodal feature vectors. A two-layer graph attention module is followed to generate multimodal representations of an entity from its multimodal neighbors. Finally, the inception decoder uses multimodal knowledge embedding to calculate scores for link prediction. The experimental results have shown that the proposed method performs well on link prediction by consideration of multimodal entities comparing with traditional methods that using only single modal text data. © 2021 IEEE.

关键词： Knowledge graph

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Arbitrary State Transition of Open Qubit system Based on Switching control

arXiv

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arXiv 2024年

作者： Wu, Guangpu Xue, Shibei Ma, Shan Kuang, Sen Dong, Daoyi Petersen, Ian R. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Automation Central South University Changsha410083 China Department of Automation University of Science and Technology of China Hefei230027 China School of Engineering Australian National University CanberraACT2601 Australia

We present a switching control strategy based on Lyapunov control for arbitrary state transitions in open qubit systems. With coherent vector representation, we propose a switching control strategy, which can prevent the state of the qubit from entering invariant sets and singular value sets, effectively driving the system ultimately to a sufficiently small neighborhood of target states. In comparison to existing works, this control strategy relaxes the strict constraints on system models imposed by special target states. Furthermore, we identify conditions under which the open qubit system achieves finite-time stability (FTS) and finite-time contractive stability (FTCS), respectively. This represents a critical improvement in quantum state transitions, especially considering the asymptotic stability of arbitrary target states is unattainable in open quantum systems. The effectiveness of our proposed method is convincingly demonstrated through its application in a qubit system affected by various types of decoherence, including amplitude, dephasing and polarization decoherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Asymptotic stability

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Optimal control Synthesis of Markov Decision Processes for Efficiency with Surveillance Tasks

arXiv

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arXiv 2024年

作者： Chen, Yu Yin, Xuanyuan Li, Shaoyuan Yin, Xiang Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University 62 Nanyang Drive Singapore

We investigate the problem of optimal control synthesis for Markov Decision Processes (MDPs), addressing both qualitative and quantitative objectives. Specifically, we require the system to fulfill a qualitative surveillance task in the sense that a specific region of interest can be visited infinitely often with probability one. Furthermore, to quantify the performance of the system, we consider the concept of efficiency, which is defined as the ratio between rewards and costs. This measure is more general than the standard long-run average reward metric as it aims to maximize the reward obtained per unit cost. Our objective is to synthesize a control policy that ensures the surveillance task while maximizes the efficiency. We provide an effective approach to synthesize a stationary control policy achieving ϵ-optimality by integrating state classifications of MDPs and perturbation analysis in a novel manner. Our results generalize existing works on efficiency-optimal control synthesis for MDP by incorporating qualitative surveillance tasks. A robot motion planning case study is provided to illustrate the proposed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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