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Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

Frontiers of information Technology & Electronic Engineering 2023年第7期24卷 1062-1079页

作者： Zhenhui FENG Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and *** is a typical extended vehicle routing problem(VRP)with both time and space *** consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)*** solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is *** algorithm can be divided into two ***,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem ***,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling *** analysis is carried out based on existing data sets and simulated urban *** with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark *** VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related ***,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

关键词： Vehicle routing problem with constraints(VRPC) Spatiotemporal distance function Labor division strategy Ant colony algorithm(ACO)

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Radiation Pattern Decoupling of Low-Profile Substrate Integrated Dielectric Resonator Antennas 9

Radiation Pattern Decoupling of Low-Profile Substrate Integr...

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9th IEEE MTT-S International Wireless Symposium, IWS 2022

作者： Zhao, Xin Tong, Changwu Yang, Nan Lu, Kai Wu, Zehai Long, Yunliang School of Electronic and Information Technology Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China Guangdong Broadradio Communication Technology Co.Ltd Guangzhou510730 China

ISBN: (数字)9781665481977

ISBN: (纸本)9781665481977

A decoupled 1 times 2 cylindrical substrate integrated di-electric resonator antennas (DRAs) is presented using metallic vias and rectangular strips. The two externally tangent cylindrical DRAs are implemented in a printed circuit board (PCB) with two different substrate layers, where the upper and the lower sub-strates are used for antenna structure and feed network, respectively. By inserting vertical metallic vias into the upper substrate and attaching horizontal strips at both sides of the intersection, reduced isolation and distorted radiation pattern caused by mu-tual coupling is corrected, resulting in symmetric radiation pat-terns and maximum radiation along z-axis. To verify the idea, a design working at 24.00-24.25 GHz is designed. It can be found that the isolation between the two ports are higher than 15 dB, the envelope correlation coefficient (ECC) is below 0.002, and the maximum radiation is along z-axis, which makes the antenna suit-able for multiple-input and multiple output systems. © 2022 IEEE.

关键词： Printed circuit boards

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Semi-Supervised Learning for Low-Light Image Enhancement by Pseudo Low-Light Image

Semi-Supervised Learning for Low-Light Image Enhancement by ...

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International Congress on Image and Signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shuo Xie Yingjie Ma Wei Xu Song Qiu Yifei Sun School of Communication and Electronic Engineering East China Normal University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai

Building paired datasets in low-light enhancement entails significant cost and time, making such datasets precious commodities. Many researchers focus on how to enable models to learn more information from limited datasets. A prevalent strategy involves employing semi-supervised learning techniques to enhance model performance through additional unpaired images. However, one of the main challenges faced is the scarcity of a vast number of unpaired images from the same domain as the original low-light images. Consequently, we introduce a semi-supervised image enhancement method using pseudo-low-light images. Initially, we generate pseudo low-light images with less noise compared with the source domain image by the Signal-to-Noise Ratio prior and diffusion models. We then employ the Mean-Teacher network and the feature constraints of the pseudo-low-light images to realize low-light image enhancement. Comprehensive experimental results validate the efficacy of our approach on real-world datasets.

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End-To-End Autonomous Exploration for Mobile Robots in Unknown Environments through Deep Reinforcement Learning

End-To-End Autonomous Exploration for Mobile Robots in Unkno...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Li, Zhi Xin, Jinghao Li, Ning 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 Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Autonomous exploration in unknown environments is a significant capability for mobile robots. In this paper, we present an end-To-end autonomous exploration model based on deep reinforcement learning (DRL), which takes the sensor data and a novel exploration map as inputs, and directly outputs the motion control commands of the robot. In contrast to the existing DRL-based exploration methods, the proposed model has no requirements to be combined with the traditional exploration or navigation algorithms, resulting in lower computational complexity. We directly transfer the DRL-based model trained in the training map to four test maps with different sizes and layouts, and the results show that the robot can rapidly adapt to unknown scenes. Besides, a comparison study with RRT-exploration algorithm indicates that the proposed model can reach a higher map exploration rate within less distance and time. Furthermore, we also conduct experiments on the real physical robot to demonstrate the transferability of learned policy from simulation to reality. A video of our experiments in the Gazebo simulator and real world can be found here1 © 2022 IEEE.

关键词： Reinforcement learning

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Robust Stability of Linear Time-Invariant systems with Hybrid Uncertainties

Robust Stability of Linear Time-Invariant Systems with Hybri...

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Automation, control and Electronics Engineering (CACEE), International Conference on

作者： Yangxuan Li Junguo Lu Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai People’s Republic of China

This paper presents a study on the robust stability analysis of linear time-invariant systems with parameter uncertainties and norm-bounded uncertainties. By utilizing the structured singular value, necessary and sufficient conditions for robust stability are derived. Based on the stability condition, the stability margin of the uncertain system is obtained from the skewed structured singular value. Additionally, numerical simulation results are provided to validate the effectiveness of the proposed methods.

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High-Gain Millimeter-Wave Reflectarray Antenna

High-Gain Millimeter-Wave Reflectarray Antenna

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Xie, Shi-Kang Deng, Yu-Zhuo Yang, Nan Lu, Kai Wu, Ze-Hai Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510006 China Guangdong Broadradio Communication Technology Co.Ltd Guangzhou510730 China

ISBN: (纸本)9781665452366

A high-gain reflectarray(RA) antenna is proposed in this paper for millimeter wave applications. The unit cell of the RA is etched on the top surface of a substrate, with a circular patch and a concentric circular ring. It can be found that this unit cell can cover a whole 360°, with an insertion loss lower than 0.3 dB. To verify the design idea, a 24×24 unit RA antenna was designed, fabricated, and measured. Its measured realized gain can be up to 28.1 dBi, with satisfactory cross-polar and sidelobe levels. © 2022 IEEE.

关键词： Millimeter waves

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SGDA: A Saliency-Guided Domain Adaptation Network for Nighttime Semantic Segmentation

SGDA: A Saliency-Guided Domain Adaptation Network for Nightt...

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Industrial Cyber-Physical systems (ICPS)

作者： Yijia Duan Jingzheng Tu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China

Nighttime semantic segmentation has attracted considerable attention due to its crucial status in the smart city. However, it is challenging to handle poor illumination and indiscernible information. To tackle these problems, a saliency-guided domain adaptation network, SGDA, is proposed via adapting daytime models to nighttime scenes. Firstly, a saliency guidance branch is attached to the segmentation network to enrich the spatial features and guide the model to better perceive detail information. Secondly, to embed the saliency guidance to the segmentation network, a pyramid attention architecture is designed to fuse the features from the two branches. Thirdly, an illumination adaptation module is constructed to close the intensity distributions via adversarial learning, with an elaborately designed loss function to improve the performance. Extensive experiments on Dark Zurich dataset and Nighttime Driving dataset validate the effectiveness of SGDA, and indicate that our method improves the accuracy on small object categories,

关键词： Adaptation models Fuses Smart cities Semantic segmentation Semantics Lighting Cyber-physical systems

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Breakdown of effective-medium theory by a photonic spin Hall effect

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Science China(Physics,Mechanics & Astronomy) 2023年第11期66卷 145-154页

作者： Shuaijie Yuan Xinxing Zhou Yu Chen Yuhan Zhong Lijuan Sheng Hao Hu Hongsheng Chen Ido Kaminer Xiao Lin Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Synergetic Innovation Center for Quantum Effects and ApplicationsSchool of Physics and ElectronicsHunan Normal UniversityChangsha410081China International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong ProvinceInstitute of Microscale OptoelectronicsShenzhen UniversityShenzhen518060China Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and InstrumentationZJU-Hangzhou Global Scientific and Technological Innovation CenterZhejiang UniversityHangzhou310027China International Joint Innovation Center Electromagnetics Academy at Zhejiang UniversityZhejiang UniversityHaining314400China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore639798Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of EducationCollege of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing211106China Key Laboratory of Advanced Micro/Nano Electronic Devices&Smart Systems of Zhejiang Jinhua Institute of Zhejiang UniversityZhejiang UniversityJinhua321099China Shaoxing Institute of Zhejiang University Zhejiang UniversityShaoxing312000China Department of Electrical Engineering Technion-Israel Institute of TechnologyHaifa32000Israel

Effective-medium theory pertains to the theoretical modelling of homogenization,which aims to replace an inhomogeneous structure of subwavelength-scale constituents with a homogeneous effective *** effective-medium theory is fundamental to various realms,including electromagnetics and material science,since it can largely decrease the complexity in the exploration of light-matter interactions by providing simple acceptable ***,the effective-medium theory is thought to be applicable to any all-dielectric system with deep-subwavelength constituents,under the condition that the effective medium does not have a critical angle,at which the total internal reflection *** we reveal a fundamental breakdown of the effective-medium theory that can be applied in very general conditions:showing it for deep-subwavelength all-dielectric multilayers even without a critical *** finding relies on an exotic photonic spin Hall effect,which is shown to be ultrasensitive to the stacking order of deep-subwavelength dielectric layers,since the spin-orbit interaction of light is dependent on slight phase accumulations during the wave *** results indicate that the photonic spin Hall effect could provide a promising and powerful tool for measuring structural defects for all-dielectric systems even in the extreme nanometer scale.

关键词： photonic spin Hall effect effective-medium theory spin-orbit interaction of light

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Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen Injected PEMFC systems

Safe Deep Learning-Based Offset-Free MPC for Green Hydrogen ...

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Chinese Automation Congress (CAC)

作者： Han Wang Longyan Li Chao Ning Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Safe and stable operation of a proton exchange membrane fuel cell (PEMFC) system requires stringent control of oxygen excess ratio (OER). However, the OER regulation in PEMFC is challenging due to frequent fluctuations of current, various modeling nonlinearities, constrained manipulated variable, and real-time requirements. Offset-free model predictive control (MPC) provides a useful means for controlling systems with disturbances and constraints, but suffers from the heavy computational burden of repeatedly solving an optimization problem in real time. Such computational issue precludes the possibility of meeting the real-time requirements of PEMFC. In this paper, a PEMFC cathode gas supply model is firstly established. Next, we develop a safe deep learning-based offset-free MPC algorithm. Based on the nominal offset-free MPC, the proposed MPC not only reserves the ability of disturbance rejection, but also leverages deep neural networks for approximating the explicit solution to the MPC problem to greatly reduce online computational time. Furthermore, a gauge map is used to guarantee the satisfaction of safe constraints regarding compressor voltage. The simulation results show that the proposed MPC is an order of magnitude faster than the nominal offset-free MPC.

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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