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General theory for designing phonon transport in alloyed/doped materials

Science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 133-140页

作者： Chenhan Liu Chao Wu Wei Liu Yunshan Zhao Gang Zhang Hongmin Yang Yunfei Chen Micro-and Nano-scale Thermal Measurement and Thermal Management Laboratory Key Laboratory of Numerical Simulation for Large Complex SystemsMinistry of EducationSchool of Energy and Mechanical EngineeringNanjing Normal UniversityNanjing210023China School of Electrical and Automation Engineering Nanjing Normal UniversityNanjing210023China Phonon Engineering Research Center of Jiangsu Province Center for Quantum Transport and Thermal Energy ScienceInstitute of Physics Frontiers and Interdisciplinary SciencesSchool of Physics and TechnologyNanjing Normal UniversityNanjing210023China Agency for Science Technology and ResearchInstitute of High-Performance ComputingSingapore138632Singapore Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments School of Mechanical EngineeringSoutheast UniversityNanjing211100China

Alloying/doping is a widely used technique for improving the electrical,mechanical,and optical properties of ***,this technology induces significant distortions in the lattice structure,mass distribution,and potential field,greatly enhancing phonon ***,we introduce the concept of alloying/doping path and employ crystal symmetry,lattice deformation,and electron distribution to characterize *** on this new concept,the phonon thermal transport behavior in alloyed/doped materials can be well designed,and along different alloying/doping paths,the difference in thermal conductivity can be up to 45 *** one hand,strategic alloying/doping that combines high crystal symmetry,large lattice contraction,and the same electron distribution suppresses phonon-phonon scattering phase space,induces phonon stiffening,and bolsters electronic structure symmetry,*** synergistic effects significantly improve thermal *** the other hand,random alloying/doping has a low symmetry,leading to the typical“U”shape of alloying/doping level-dependent thermal *** theory is corroborated in three-dimensional(3D)Si,2D MoS_(2),and quasi-1D TiS_(3),affirming its efficacy and broad applicability in controlling phonon transport.

关键词： phonon transport alloyed/doped materials crystal symmetry lattice contraction electronic structure

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Research on Evolutionary Guiding Strategy for Self-organized Aggregates of Massive Flexible Resources 4

Research on Evolutionary Guiding Strategy for Self-organized...

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4th International Conference on Advanced Electrical and Energy systems, AEES 2023

作者： Mao, Tingfeng Zhang, Kaifeng Yin, Changwei Pan, Xiaohui Ma, Nan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Nanjing Power Supply Company of State Grid Jiangsu Electric Power Co. Ltd. Nanjing210019 China

ISBN: (纸本)9798350342659

With the improvement of communication technology and the management of demand-side, more and more researches focus on the aggregation technology of flexible resources on the demand side. This paper proposes to build a self-organizing response framework in the scenario of massive resources, and resources can independently choose to join or exit the aggregates to participate in the regulation of grid active power balance. However, self-organizing aggregates cannot generate aggregation scenarios that accurately match the dynamic needs of the power grid during autonomous operation, so the grid needs to further guide the evolution of the aggregates. Firstly, the process and principle of realizing self-organization and evolution of resources in cyberspace are outlined. Then, the evolutionary driving sources of the self-organizing aggregates are analyzed to construct the evolutionary dynamics model framework from both the steady and the dynamic dimensions, and the data-driven modeling is carried out through the back propagation neural network to obtain the evolution law of resource self-organization. Finally, an iterative evolutionary guiding strategy based on price feedback is designed. The optimal guiding value of the market price cap is obtained through iteration under the expected aggregation scale target of the power grid, and also the dynamic evolution time of the aggregates is predicted to determine the timing of the release of the guiding strategy. The proposed strategy is constructed and tested on the NETLOGO resource self-organization simulation platform. The results show that the self-organizing aggregates can evolve to the expected state of power grid demand with high accuracy. © 2023 IEEE.

关键词： Aggregates

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Distributed solver for linear matrix inequalities: an optimization perspective

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control Theory and Technology 2021年第4期19卷 507-515页

作者： Weijian Li Wen Deng Xianlin Zeng Yiguang Hong Department of Automation University of Science and Technology of ChinaHefei 230027AnhuiChina Key Lab of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

In this paper,we develop a distributed solver for a group of strict(non-strict)linear matrix inequalities over a multi-agent network,where each agent only knows one inequality,and all agents co-operate to reach a consensus solution in the intersection of all the feasible *** formulation is transformed into a distributed optimization problem by introducing slack variables and consensus ***,by the primal–dual methods,a distributed algorithm is proposed with the help of projection operators and derivative ***,the convergence of the algorithm is analyzed,followed by illustrative simulations.

关键词： Distributed computation Distributed optimization Linear matrix inequalities Primal-dual method

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A Lightweight Object Detection Network for Industrial Robot Based YOLOv5

A Lightweight Object Detection Network for Industrial Robot ...

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2023 China automation Congress, CAC 2023

作者： Zhong, Tao Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

With the rapid development of deep learning, it has been widely applied in fields such as computer vision, natural language processing, and robotics. Despite the superior performance of deep learning in object detection, most industrial vision robots still rely on traditional object detection methods due to computational constraints of robot controllers. In order to improve the performance of object detection for vision robots, a lightweight 3D object detection network based on You Only Look Once version 5 (YOLOv5) is proposed for satisfying industrial production. YOLOv5 work to efficiently object detection using deep convolutional networks. In terms of model deployment, we adopt a novel OpenVINO-based model deployment approach. The OpenVINO framework significantly enhances the inference speed of models by model optimization and compression. Our model achieves a 70% reduction in inference time compared to the baseline model on CPU. The effectiveness of the proposed method is demonstrated through experiments. © 2023 IEEE.

关键词： Object detection

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Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration

Graph-based Selection-Activation Reinforcement Learning for ...

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第43届中国控制会议

作者： Hao-Xiang Chen Xi-Wen Zhang Jun-Nan Shen State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning(GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages.

关键词： Multi-Agent System Hierarchical Graph Convolutional Network Heterogenous Multi-Agent Collaboration

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An Improved YOLOv5 Detection Algorithm with Pruning and OpenVINO Quantization

An Improved YOLOv5 Detection Algorithm with Pruning and Open...

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2023 China automation Congress, CAC 2023

作者： Chen, Cheng Gan, Yahui Han, Zao Gao, Huan Li, Ang School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education NanJing China Nanjing YingQi Intelligent Technology Co. Ltd. Intel Joint Research Institute on Intelligent Edge Computing NanJing China

ISBN: (纸本)9798350303759

In order to address the problem of current object detection models being too large to be deployed on robot controllers, this paper proposes improvements to YOLOv5 for real-time detection. The YOLOv5s model is pruned at the Batch Normalization (BN) layer, and further optimized using the OpenVINO tool for quantization. The results demonstrate that our improvements effectively improve the model's inference speed while maintaining its accuracy. Compared to the original YOLOv5 model, the pruned model achieves the same accuracy without degradation, with a 36.6% reduction in parameter count and a 35% reduction in weight storage file size. Furthermore, after optimization with OpenVINO, the final model achieves a FPS of 56. © 2023 IEEE.

关键词： Object detection

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Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Gao, Yuan Guo, Yu Zhu, Fenghua Wang, Fei-Yue The School of Navigation The State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan430063 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer the poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which has more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoder-decoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with the state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS. The source code is available at https://***/QuJX/DDNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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A bi-level optimization approach for joint rack sequencing and storage assignment in robotic mobile fulfillment systems

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Science China(Information Sciences) 2023年第11期66卷 127-149页

作者： Xiang SHI Fang DENG Sai LU Yunfeng FAN Lin MA Jie CHEN Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing Institute of Technology Chongqing Innovation Center Zhejiang Cainiao Supply Chain Management Co. Ltd. Shanghai Research Institute for Intelligent Autonomous Systems

This paper studies a novel rack scheduling problem with multiple types of multiple storage locations(RS-MTMS), which can decide the retrieval sequence of racks and assign each rack a storage location after visiting a picking station. A major challenge in RS-MTMS is that the storage assignment problem and the retrieval sequence decision are closely coupled. If the RS-MTMS is solved directly, the storage assignment scheme and the retrieval sequence of racks are generally generated separately, thus resulting in poor performance. To overcome this difficulty, we propose a bi-level optimization approach for jointly optimizing the storage assignment and retrieval sequence(Bi JSR). In Bi JSR, the storage assignment problem is solved by variable neighborhood search(VNS) in the upper-level optimization. Effective candidate modes are incorporated into VNS to improve solution quality and computational efficiency. The sequencing optimization is obtained in the lower-level according to the given storage location set. A transformation strategy with sufficient problem-specific knowledge is developed to identify the lower-level optimization as the traveling salesman problem and its variants. Then these identified problems are solved using the loop-based *** results show that the proposed Bi JSR is more effective and efficient than the representative algorithms in solving the RS-MTMS problem.

关键词： rack scheduling sequence decision storage assignment bi-level optimization robotic mobile fulfillment system

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AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

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International Conference on Intelligent Transportation

作者： Fei Lin Yonglin Tian Yunzhe Wang Tengchao Zhang Xinyuan Zhang Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China Capital University of Economics and Business Beijing China Department of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks.

关键词： Three-dimensional displays Large language models Urban areas Land transportation Autonomous aerial vehicles Cognition Space exploration Planning

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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Chinese control Conference (CCC)

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China

Deforestation is the primary source of global warming; traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its power. Various technologies provide connectivity and a fast-updating system to eliminate the paper-based approach. LoRa is one of the contenders to design the system for electronic shelf labels (ESLs). In this paper, LoRa has been used to minify data losses and guarantee the successful decoding of the carrier signals. The data parallelism at the network server (NS) is used to distribute the data packets among the gateways (GWs) for concurrent transmissions to the end devices (EDs). The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and collision. The data rate (DR) and spreading factors (SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated tags. The orthogonality principles follow industrial, scientific, and medical regulations (ISM) to avoid data traffic congestion. GWs under different duty cycles (DC) and bandwidth (BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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