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IEEE Conference on Decision and control

作者： Ryota Kokubo Mitsuaki Matsubara Rui Kato Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Yokohama Japan Laboratoire d’Automatique de Génie des Procédés et de Génie Pharmaceutique (LAGEPP) Université Claude Bernard Lyon 1 Villeurbanne France Department of Information Physics and Computing The University of Tokyo Tokyo Japan

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods.

关键词： Neurological diseases Hands Pathology Design methodology Neurons Pacemakers Convex functions Feedback control Synchronization Oscillators

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Auction-Based Job Scheduling for Smart Manufacturing

Auction-Based Job Scheduling for Smart Manufacturing

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Emil Gatial Zoltán Balogh Sepideh Hassankhani Dolatabadi Hatem Ghorbel Stefano Carrino Jonathan Dreyer Vicente Rodríguez Montequín Adrian Gligor Laszlo Barna Iantovics Parallel and distributed computing Institute of Informatics SAV Bratislava Slovakia Modelling and Control of Discrete Processes Institute of Informatics SAV Bratislava Slovakia Haute École Arc Ingénierie HES-SO St-Imier Switzerland Project Engineering Area University of Oviedo Oviedo Spain Electrical Engineering and Information Technology “George Emil Palade” University of Medicine Pharmacy Science and Technology of Targu Mures Targu Mures Romania

The paper describes a real-time job scheduling method designed for production of goods with different characteristics on machines with different processing parameters. The objective is to maximize the global reward of the factory as a sum of the rewards for each machine job. Traditionally, the task assignment problems deal with the assignment of m-tasks to n-agents and are calculated by analytical methods or heuristics. The proposed method is based on online auctions that distributes the tasks to the machines using the software agents. The method is implemented using the ERTS (Erlang Real-Time System) that allows adopting the features of fault-tolerance and real-time processing. The paper starts with introduction and review of related state-of-the-art. The following chapter briefly describes the problem and the specific requirements. The following chapter describes the software architecture of the online auction system, the use-case and the motivation for developing this method. The proposed method auction-based task assignment and its implementation are described in the next chapters. At the end of the work, the results of the method are presented in comparison with the optimal solution and the performance characteristics are also described. In the conclusion, possible advances and future work are proposed.

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Optimizing Temperature Distributions for Training Neural Quantum States using Parallel Tempering

arXiv

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arXiv 2024年

作者： Smith, Conor Campbell, Quinn T. Albash, Tameem Center for Quantum Information and Control University of New Mexico University of New Mexico AlbuquerqueNM United States Department of Electrical and Computer Engineering University of New Mexico AlbuquerqueNM United States Center for Computational Quantum Physics Flatiron Institute New YorkNY10010 United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM87185 United States

Parameterized artificial neural networks (ANNs) can be very expressive ansatzes for variational algorithms, reaching state-of-the-art energies on many quantum many-body Hamiltonians. Nevertheless, the training of the ANN can be slow and stymied by the presence of local minima in the parameter landscape. One approach to mitigate this issue is to use parallel tempering methods, and in this work we focus on the role played by the temperature distribution of the parallel tempering replicas. Using an adaptive method that adjusts the temperatures in order to equate the exchange probability between neighboring replicas, we show that this temperature optimization can significantly increase the success rate of the variational algorithm with negligible computational cost by eliminating bottlenecks in the replicas’ random walk. We demonstrate this using two different neural networks, a restricted Boltzmann machine and a feedforward network, which we use to study a toy problem based on a permutation invariant Hamiltonian with a pernicious local minimum and the J1-J2 model on a rectangular lattice. © 2024, CC BY.

关键词： Hamiltonians

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Electrical Charge Polarization Induced by Alternating Temperature Gradient to Promote Z-Scheme Photocatalytic Hydrogen Evolution and Imine Production

SSRN

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SSRN 2024年

作者： He, Zuming Yi, Jun Wong, Ngie Hing Sunarso, Jaka Xia, Yongmei Fu, Xiaofei Huang, Zhengyi Chen, Guihua Yang, Hanpei Zhou, Wei School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China Huaide School Changzhou University Jingjiang214500 China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia School of Materials and Engineering Jiangsu University of Technology Changzhou213001 China School of Pharmaceutical and Chemical Engineering Taizhou University Jiaojiang318000 China Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes Ministry of Education College of Environment Hohai University Nanjing210098 China Shandong Provincial Key Laboratory of Molecular Engineering School of Chemistry and Chemical Engineering Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

Here, reported photothermal-pyroelectric-Fe0.9Ni0.1S2/ZnSnO3 photocatalytic system via hydrothermal and annealing methods. Fe0.9Ni0.1S2 nanoparticles (NPs) could convert most of near-infrared (NIR) light energy into heat energy to drive the pyroelectric effect of ZnSnO3, causing natural polarization and thermoelectric effects under the condensed water circulation. As a result, the as-designed Fe0.9Ni0.1S2/ZnSnO3-2 composites could yield up to 7.194 mmol g-1 h-1) with exceptional photocatalytic performance under simulated sunlight. This result was 5.41 and 4.92-fold those of Fe0.9Ni0.1S2 and ZnSnO3, respectively, with 16.66% apparent quantum yield under 365 nm monochromatic light. At the same time, the composites could also oxidize BA and yield up to 6.640 mmol g-1 h-1) n-benzylidene benzylamine with a 90.2% selectivity. Due to the synergistic effect of Z-scheme's built-in electric field and photothermal-pyroelectric of Fe0.9Ni0.1S2/ZnSnO3, the surface charges released on them could direct the charge transfer pathways and restrain their recombination with accelerated electron transfer kinetics, thus extending the carrier lifetime. © 2024, The Authors. All rights reserved.

关键词： Thermal gradients

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FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network

FPGA-Based Convolution Accelerator and Memristor IP Core for...

引用

Computer Science, Electronic Information engineering and Intelligent control Technology (CEI), IEEE International Conference on

作者： Aolong Tan Yucheng Yang Shukai Duan Lidan Wang College of Artificial Intelligence Southwest University Chongqing China State Key Laboratory of Intelligent Vehicle Safety Technology National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing China Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education Southwest University Chongqing PR China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Object detection technology is widely used in areas such as autonomous driving, drone reconnaissance, smart surveillance and industrial automation, especially in remote or hazardous scenarios where it can improve efficiency and safety. Therefore, the development of a high-precision, low-power acceleration system integrated with low-power hardware is crucial. This paper proposes a collaborative acceleration scheme for the YOLO network based on an FPGA convolution accelerator and simulated memristors, which successfully implements and accelerates the YOLOv4-Tiny model. First, a reconfigurable single-core accelerator operator is designed for various computational operations in the model. Second, taking advantage of the digital device characteristics of memristors and the hardware resources of the FPGA, a memristor simulation IP core is built to exploit its memory characteristics for convolution operations. Through the synergistic effect of the convolution accelerator and the simulated IP core, a hybrid acceleration of the detection model is achieved. The power consumption of the convolution accelerator is only 2.75W, and in testing, the detection time for a single image is 823ms with an average accuracy of over 0.95, achieving high detection performance. This provides an opportunity to explore and research the efficient acceleration of object detection networks with integrated storage and computation.

关键词： YOLO Performance evaluation Power demand Accuracy Convolution Computational modeling Memristors Hardware IP networks Field programmable gate arrays

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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Xu, Zekun Zhou, Yipeng Wen, Shiting Jing, Weipeng Northeast Forestry University College of Computer and Control Engineering Harbin150040 China Macquarie University School of Computing Sydney Australia NingboTech University School of Computer and Data Engineering Ningbo315100 China

Unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in pine wilt disease (PWD) detection. UAV-collected PWD images are characterized by complex backgrounds, small target sizes, and uneven spatial distribution. State-of-the-art generic detection methods typically use Transformers or Mamba-based backbones to mitigate background interference by enhancing the global receptive field. However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. To overcome these challenges, a novel PWD detection method called feature interaction and dependency capturing-YOLO (FIDCYOLO) is proposed in this article. Firstly, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network (SELAN) is proposed to promote information interaction between features, improving the model's learning capability. Then, to address the interference of complex backgrounds, the bidirectional extension of Mamba (Hydra) is improved by employing the partial dependency capture (PDC) module, which captures both global and local dependencies for suppressing background information. Additionally, the feature aggregation-spatial pyramid pooling fast (FA-SPPF) module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. Finally, we use ADown as the downsampling module to avoid the loss of target information. Extensive experiments on our self-constructed PWD dataset and the public DIOR dataset demonstrate the excellent performance of FIDC-YOLO. © 2008-2012 IEEE.

关键词： Drones

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

引用

arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation

arXiv

引用

arXiv 2023年

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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Design and Development of Cyber Physical System for Real-Time Web-based Visualization and control of Robot Arm

Design and Development of Cyber Physical System for Real-Tim...

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IEEE International Conference on control and Robotics engineering (ICCRE)

作者： Suwanan Khrueangsakun Santi Nuratch Panuthat Boonpramuk Embedded Computing and Control Laboratory Faculty of Engineering KMUTT Bangkok Thailand

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

Since Cyber-Physical System or CPS currently takes part in industrial, there is a lot of research for developing this system. From the survey and the trend of CPS research, a human user interface for control and monitor was highlighted as Digital Twins where VR and AR are introduced to improve the visualization for easier system interpretation. This paper introduces the proposed design of real-time control web-based monitor and control of 4-DoF robotic arm system where the robotic arm represents a physical part and web-based visualization as a cyber-part. The 4-DoF robotic arm was utilized from MeArm robot and was controlled via ESP32 WROOM with Wi-Fi module in order to pursue wireless data transmission. WebSocket protocol will be exploited to connect between the physical and cyber-part where the virtual model was displayed and controlled via web-browser, the virtual model of the robot was created with 3D blender and operated with WebGL-based 3D application which can be integrated and run on web-browser without extension. The proposed design was evaluated with operational time and error between the controlled virtual model and actual physical angle, the result achieved 278.772 ms for average operational time and 0.32% in error of angle between virtual and actual. As a result, the proposed design provided two-way communication, real-time control, and monitor which can be applied in industrial applications.

关键词： Manipulators Solid modeling Three-dimensional displays Real-time systems Servers Service robots

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