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arXiv 2022年

作者： Zhu, Dafeng Yang, Bo Liu, Yuxiang Wang, Zhaojian Ma, Kai Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China

Owing to large industrial energy consumption, industrial production has brought a huge burden to the grid in terms of renewable energy access and power supply. Due to the coupling of multiple energy sources and the uncertainty of renewable energy and demand, centralized methods require large calculation and coordination overhead. Thus, this paper proposes a multi-energy management framework achieved by decentralized execution and centralized training for an industrial park. The energy management problem is formulated as a partially-observable Markov decision process, which is intractable by dynamic programming due to the lack of the prior knowledge of the underlying stochastic process. The objective is to minimize long-term energy costs while ensuring the demand of users. To solve this issue and improve the calculation speed, a novel multi-agent deep reinforcement learning algorithm is proposed, which contains the following key points: counterfactual baseline for facilitating contributing agents to learn better policies, soft actor-critic for improving robustness and exploring optimal solutions. A novel reward is designed by Lagrange multiplier method to ensure the capacity constraints of energy storage. In addition, considering that the increase in the number of agents leads to performance degradation due to large observation spaces, an attention mechanism is introduced to enhance the stability of policy and enable agents to focus on important energy-related information, which improves the exploration efficiency of soft actor-critic. Numerical results based on actual data verify the performance of the proposed algorithm with high scalability, indicating that the industrial park can minimize energy costs under different demands. © 2022, CC BY-NC-ND.

关键词： Multi agent systems

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A Machine Vision Based Automated Quality Control system for Product Dimensional Analysis

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Procedia computer Science 2021年 185卷 127-134页

作者： Aditya Akundi Mark Reyna Department of Manufacturing and Indsutrial Engineering The University of Texas Rio Grande Valley Texas 78520 United States Complex Engineering System Laboratory The University of Texas Rio Grande Valley Texas 78520 United States Department of Electrical and Computer Engineering The University of Texas Rio Grande Valley Texas 78520 United States

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and relatively low cost. This paper presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples, including additive manufacturing products. The algorithm used performs dimensional inspection on a base product considered to have acceptable dimensions. The perimeter, area, rectangularity, and circularity of the base product are determined using blob analysis on a calibrated camera. These parameters are then used as the standard with which to judge additional products. Each product following is similarly inspected and compared to the base product parameters. A likeness score is calculated for each product, which provides a single value tracking all parameter differences. Finally, the likeness score is considered on whether it is within a threshold, and the product is considered to be acceptable or defective. The proposed MV system has achieved satisfactory results, as discussed in the results section, that would allow it to serve as a dependable and accurate QC inspection system in industrial settings.

关键词： Machine Vision Quality Control Dimensional Analysis digital Quality Rectangularity Circularity Production Manufacturing

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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

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Proceedings of the 2021 5th International Conference on Video and Image Processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

RCGA-Net: An Improved Multi-hybrid Attention Mechanism Netwo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xiao, Feng Shen, Cong Chen, Yu Yang, Tian Chen, Shengyong Liao, Zhijun Tang, Jijun Tianjin University of Technology School of Computer Science and Engineering Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China School of Basic Medical Sciences Fujian Medical University Fujian Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Systems Bioengineering Ministry of Education Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States

ISBN: (纸本)9781665401265

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score. © 2021 IEEE.

关键词： Diagnosis

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Using Simulation and digital Twins to Innovate: Are we Getting Smarter?

Using Simulation and Digital Twins to Innovate: Are we Getti...

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2021 Winter Simulation Conference, WSC 2021

作者： Taylor, Simon J E Johansson, Bjorn Jeon, Sumin Lee, Loo Hay Lendermann, Peter Shao, Guodong Brunel University London Modelling Simulation Group Department of Computer Science Uxbridge United Kingdom Chalmers University of Technology Department of Industrial and Materials Science Gothenburg41296 Sweden Digital Industries Customer Services Siemens Pte Ltd Singapore National University of Singapore Department of Industrial Systems Engineering Management Singapore D-SIMLAB Technologies Pte Ltd Singapore National Institute of Standards and Technology Engineering Laboratory GaithersburgMD20899 United States

ISBN: (纸本)9781665433112

digital Twins have recently emerged as a major new area of innovation. digital Twins are often found at the core of 'smart' solutions that have also emerged as major areas of innovation. Modeling and Simulation (MS) approaches create a model of a real-world system that is linked to data sources and is used to simulate and predict the behavior of its real-world counterpart. On the face of it digital Twins and MS appear to be similar, if not the same. Is this actually the case? Are the two fields really separate or is digital Twin research re-inventing the 'MS wheel'? To investigate these relationships, in this panel we will explore some contemporary innovations with digital Twins and discuss whether or not digital Twins is a contemporary 'refresh' or 'rebranding' of MS or if there are exciting new synergies. © 2021 IEEE.

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Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks

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Science Bulletin 2021年第16期66卷 1624-1633,M0003页

作者： Xumeng Zhang Jian Lu Zhongrui Wang Rui Wang Jinsong Wei Tuo Shi Chunmeng Dou Zuheng Wu Jiaxue Zhu Dashan Shang Guozhong Xing Mansun Chan Qi Liu Ming Liu Frontier Institute of Chip and System Fudan UniversityShanghai 200433China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical and Electronic Engineering the University of Hong KongHong KongChina Zhejiang Laboratory Hangzhou 311122China Department of Electronic and Computer Engineering the Hong Kong University of Science and TechnologyHong KongChina

Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic ***,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological ***,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very ***,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron *** hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic ***,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this *** work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.

关键词： Memristor Hybrid neuron In-situ learning Fully hardware Spiking neural network

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Intelligent cognitive spectrum collaboration:Convergence of spectrum sensing,spectrum access,and coding technology

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Intelligent and Converged Networks 2020年第1期1卷 79-98页

作者： Peixiang Cai Yu Zhang Beijing National Research Center for Information Science and Technology (BNRist)Beijing 100084China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in ShenzhenShenzhen 518057China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

For a future scenario where everything is connected,cognitive technology can be used for spectrum sensing and access,and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection *** learning technologies are being increasingly used in the implementation of smart *** this paper,after an overview of several key technologies in the cognitive spectrum collaboration,a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning,and the effectiveness of the algorithm is verified by simulations,thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

关键词： cognitive radio spectrum sensing dynamic spectrum access coding technology reinforcement learning joint optimization

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Afnet: Local Aggregation and Context Fusion Network for Plant Point Cloud Part Segmentation

SSRN

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SSRN 2024年

作者： Qi, Yanyu Guo, Ruohao Li, Zhenbo Qu, Liao Niu, Dantong College of Information and Electrical Engineering China Agricultural University Beijing100083 China National Innovation Center for Digital Fishery Beijing100083 China School of Intelligence Science and Technology Peking University Beijing100871 China Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock Ministry of Agriculture and Rural Affairs Beijing100083 China Beijing Engineering and Technology Research Center for Internet of Things in Agriculture Beijing100083 China Electrical and Computer Engineering department Carnegie Mellon University PittsburghPA15213 United States BAIR Lab University of California BerkeleyCA94709 United States

Convolution-like models have emerged in recent years to address diverse point cloud processing tasks. However, a notable challenge persists in effectively capturing subtle spatial relationships and contextual semantics essential for achieving a thorough comprehension of point clouds. In this work, we propose AFNet, a local aggregation and context fusion network, to capture complicated shape structures, global semantics and contextual representations. Initially, a novel plug-and-play convolution operator is designed to model local points, leveraging both low-level positional relations and high-level feature correlations. This operator employs element-wise multiplication to aggregate transformed positions and features, followed by a reduction function for information abstraction from neighbors. By utilizing the aforementioned convolution as a foundational operator, a deep residual architecture is constructed, wherein each layer adopts one-shot connection strategy. Consequently, global semantics and complicated shapes can be fully obtained in an elegant manner. Furthermore, this work presents PlantSeg3D, a point cloud dataset of 3,600 3D plant models for plant phenotyping, to alleviate the scarcity of plant datasets in the community. Extensive experiments showcase that AFNet performs well on several public benchmarks across different tasks, surpassing previous state-of-the-art approaches with an additional increase of +4.1 mIoU. The impressive performance on both part segmentation and classification tasks demonstrates noteworthy generalization capability. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Spatiotemporal Hologram

arXiv

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arXiv 2024年

作者： Cao, Qian Zhang, Nianjia Chong, Andy Zhan, Qiwen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory Shanghai China University of Shanghai for Science and Technology Shanghai Key Laboratory of Modern Optical System Shanghai China Department of Physics Pusan National University Busan46241 Korea Republic of Institute for Future Earth Pusan National University Busan46241 Korea Republic of Westlake Institute for Optoelectronics Hangzhou Fuyang311421 China

Spatiotemporal structured light has opened up new avenues for optics and photonics. Current spatiotemporal manipulation of light mostly relies on phase-only devices such as liquid crystal spatial light modulator to generate spatiotemporal optical fields with unique photonic properties. However, simultaneous manipulation of both amplitude and phase of the complex field for the spatiotemporal light is still lacking, limiting the diversity and richness of achievable photonic properties. In this work, a simple and versatile spatiotemporal holographic method that can arbitrarily sculpture the spatiotemporal light is presented. The capabilities of this simple yet powerful method are demonstrated through the generation of fundamental and higher-order spatiotemporal Bessel wavepacket, spatiotemporal crystal-like and quasi-crystal-like structures, and spatiotemporal flat-top wavepackets. Fully customizable spatiotemporal wavepackets will find broader application in investigating the dynamics of spatiotemporal fields and interactions between ultrafast spatiotemporal pulses and matters, unveiling previously hidden light-matter interactions and unlocking breakthroughs in photonics and beyond. © 2024, CC BY.

关键词： Holograms

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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