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Spectral2Spectral: Image-spectral Similarity Assisted Deep Spectral CT Reconstruction without Reference

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arXiv 2022年

作者： Guo, Xiaodong Li, Yonghui Chang, Dingyue He, Peng Feng, Peng Yu, Hengyong Wu, Weiwen The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China Chongqing University Chongqing China The China Academy of Engineering Physics Institute of Materials Mianyang China The Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA01854 United States The School of Biomedical Engineering Sun Yat-Sen University Guangdong Shenzhen China The Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods. © 2022, CC0.

关键词： Image reconstruction

Reconfigurable Intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

Disturbance Attenuation via Nonlinear Feedback for Chemical Reaction Networks

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IFAC-PapersOnLine 2020年第2期53卷 11497-11502页

作者： Lőrinc Márton Katalin M. Hangos Gábor Szederkényi Department of Electrical Engineering Sapientia Hungarian University of Transylvania Tirgu Mures Romania Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary Faculty of Information Technology and Bionics Pázmány Péter Catholic University Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary

This paper deals with the setpoint control of Chemical Reaction Networks (CRNs) in the presence of disturbances. The addressed control problem is traced back to the output feedback-based stabilization of passive systems. First, the passivity properties of complex balanced CRNs are discussed. Second, it is shown that, by combining the kinetic feedback based control with the passivity theory-based output feedback approach, arbitrary disturbance attenuation in controlled CRNs can be achieved. A case study is provided to demonstrate the applicability of the proposed control design approach.

关键词： Chemical Reaction Networks Passivity Setpoint control Disturbance Attenuation

Comparative study of Hermitian and non-Hermitian topological dielectric photonic crystals

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arXiv 2021年

作者： Chen, Menglin L.N. Jiang, Li Jun Zhang, Shuang Zhao, Ran Lan, Zhihao Sha, Wei E.I. Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Department of Electronic and Electrical Engineering University College London United Kingdom Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

The effects of gain and loss on the band structures of a bulk topological dielectric photonic crystal (PC) with C6v symmetry and the PC-air-PC interface are studied based on first-principle calculation. To illustrate the importance of parity-time (PT) symmetry, three systems are considered, namely the PT-symmetric, PT-asymmetric, and lossy systems. We find that the system with gain and loss distributed in a PT symmetric manner exhibits a phase transition from a PT exact phase to a PT broken phase as the strength of the gain and loss increases, while for the PT-asymmetric and lossy systems, no such phase transition occurs. Furthermore, based on the Wilson loop calculation, the topology of the PT-symmetric system in the PT exact phase is demonstrated to keep unchanged as the Hermitian system. At last, different kinds of edge states in Hermitian systems under the influences of gain and loss are studied and we find that while the eigenfrequencies of nontrivial edge states become complex conjugate pairs, they keep real for the trivial defect states. Copyright © 2021, The Authors. All rights reserved.

关键词： Photonic crystals

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

Parameter estimation and control of an unmanned aircraft-based transportation system for variable-mass payloads

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Parameter estimation and control of an unmanned aircraft-bas...

作者： Alatorre, Armando Espinoza, Eduardo S. Sánchez, Bonifacio Ordaz, Patricio Muñoz, Filiberto García Carrillo, Luis Rodolfo French-Mexican Laboratory of Informatics and Automatics Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Automatic Control Department Center of Research and Advanced Studies of the National Polytechnic Institute México City Mexico Research Center of Information Technologies and Systems Autonomous University of the State of Hidalgo Hidalgo Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States

This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust controllers. © 2021 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： controllers

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

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arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

A Constructive Semi-global Differential Game Design Method for Multi-agent systems under Input Constraint and Collision Avoidance

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A Constructive Semi-global Differential Game Design Method f...

2021 China Automation Congress, CAC 2021

作者： Wu, Xiongjun Zhou, Jialing Li, Yuanlong Chen, Chunfang Li, Kai The 802 Institute of Shanghai Academy of Space Flight Technology Eighth Academy of China Aerospace Science and Technology Corporation National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering Shanghai201109 China School of Automation Nanjing University Science and Technology Nanjing210094 China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The 802 Institute of Shanghai Academy of Space Flight Shanghai210094 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665426473

The constructive semi-global differential game concept is introduced into the multi-agent's cooperative controller designing of typical autonomous unmanned systems subject to controller input constraint as well as the collision avoidance requirements simultaneously. The problem of driving each agent which is assume to subject to input constraint nonlinearity from the initial position to a desired one and at the same time avoiding collisions (referring to both static obstacles and other agents) is modeled and transformed into multi-agent's collision avoidance problem. In view of the requirement that different agents working together might possibly have individual but conflicting purpose (such as in the multi-missile cooperative detection requiring slowly and continuing actions vs fast formation control scenario, and multi-agents' fast arriving vs avoiding different kinds of obstacles). The differential game theory provided a mechanism or framework for investigating different kinds of problems in which several players seek to achieve individual purposes or goals, which might be competing rather than cooperation. In view of the input constraints, the problem is reduced as a semi-global differential game. Conditions are established to guarantee input constraints are satisfied, and mainly by using the Hamilton-Jacobi-Isaacs (HJI for short) PDIs and as well as Ε-Nash equilibria. The semi-global property of the states are also guaranteed. It provides a description in terms of the invariant set for the overall differential game system under constraint controller input. Illustrative examples are carried out to verify the effectiveness of the method, and also the feasibility of the suggested control protocols. © 2021 IEEE

关键词： Multi agent systems