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2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Wu, Wei Bao, Yuangui Zhang, Xiaofeng School of Mathematics Liaoning Technical University Fuxin China School of Information and Electronics Beijing Institute of Technology Beijing China Guangdong Province Key Laboratory of Intelligent Detection in Complex Environment of Aerospace Land and Sea Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (纸本)9798350384185

The primary goal of the distributed economic dispatch challenge is to satisfy meet demand and various operational constraints while minimizing the overall generation cost of all generators. Traditional methods can be ineffective due to unknown cost functions and coupling constraints among different generators. This article introduces a Q-learning algorithm that solves the economic dispatch problem in a distributed manner, efficiently managing multiple coupling constraints. Our apptoach addresses the challenge of dynamic economic dispatch, where both the cost function and resource transfer function are unknown. Numerical simulations validate the algorithm's effectiveness, showcasing its capability to optimize power generation schedules under complex and dynamic conditions. © 2024 IEEE.

关键词： Cost functions

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Underwater Image Enhancement Based on Color Different Attenuation 4

Underwater Image Enhancement Based on Color Different Attenu...

引用

4th International Conference on Robotics, intelligent Control and Artificial Intelligence, RICAI 2022

作者： Liu, Chao Wang, Lili Wang, Tenghui Tang, Jialin Zhao, Zengfeng Lai, Yunting Beijing Institute of Technology Zhuhai China Key Laboratory of Intelligent Detection in Complex Environment of Aerospace Land and Sea Beijing Institute of Technology Zhuhai China City University of Macau China

ISBN: (纸本)9781450398343

Underwater images play a very important role in projects related to the underwater environment. But in the underwater environment, the R channel attenuation of the RGB image and other reasons make the quality of the image significantly different from that on land in terms of color and clarity. In order to correct the chromatic aberration of the underwater image and improve the clarity, we propose a DAC method based on the compensation of difference. First, we perform contrast stretching and image denoising on the image. Then extract the details layer and the base layer of the RGB channel of the image respectively, and use the detail layer of the G, B channels with more information to compensate for the detail layer of the R channel. Finally, we optimize the details of the picture through the grayscale world algorithm to obtain the true color of the picture. We compared with various methods in the public underwater dataset UIEB, and it turns out that our method can well solve the problems of chromatic aberration and blur. © 2022 ACM.

关键词： Image enhancement

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Underwater Image Enhancement Based on Color Different Attenuation 22

Underwater Image Enhancement Based on Color Different Attenu...

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Proceedings of the 2022 4th International Conference on Robotics, intelligent Control and Artificial Intelligence

作者： Chao Liu Lili Wang Tenghui Wang Jialin Tang Zengfeng Zhao Yunting Lai Beijing Institute of Technology China Key Laboratory of Intelligent Detection in Complex Environment of Aerospace Land and Sea Beijing Institute of Technology China City University of Macau China

ISBN: (纸本)9781450398343

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Q-Learning Algorithm Design for Distributed Economic Dispatch Problem with Multiple Coupling Constraints

Q-Learning Algorithm Design for Distributed Economic Dispatc...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Wei Wu Yuangui Bao Xiaofeng Zhang School of Mathematics Liaoning Technical University Fuxin China School of Information and Electronics Beijing Institute of Technology Beijing China Guangdong Province Key Laboratory of Intelligent Detection in Complex Environment of Aerospace Land and Sea Beijing Institute of Technology Zhuhai Zhuhai China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

关键词： Couplings Schedules Q-learning Costs Heuristic algorithms Transfer functions Cost function Numerical simulation Dynamic scheduling Generators

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Simultaneous source separation of unknown numbers of single-channel underwater acoustic signals based on deep neural networks with separator-decoder structure

arXiv

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arXiv 2022年

作者： Sun, Qinggang Wang, Kejun College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Detection in Complex Environment of Aerospace Land and Sea Zhuhai519088 China School of Information Technology Beijing Institute of Technology Zhuhai Zhuhai519088 China

The separation of single-channel underwater acoustic signals is a challenging problem with practical significance. Few existing studies focus on the source separation problem with unknown numbers of signals, and how to evaluate the performance of the systems is not yet clear. In this paper, a deep learning-based simultaneous separating solution with a fixed number of output channels equal to the maximum number of possible targets is proposed to address these two problems. This solution avoids the dimensional disaster caused by the permutation problem induced by the alignment of outputs to targets. Specifically, we propose a two-step learning-based separation model with a separator-decoder structure. A performance evaluation method with two quantitative metrics of the separation system for situations with mute channels in the output channels that do not contain target signals is also proposed. Experiments conducted on simulated mixtures of radiated ship noise show that the proposed solution can achieve similar separation performance to that attained with a known number of signals. The proposed separation model with separator-decoder structure achieved competitive performance as two models developed for known numbers of signals, which is highly explainable and extensible and gets the state of the art under this *** Codes 68T07, 94A12, 76Q05, 68U99 Copyright © 2022, The Authors. All rights reserved.

关键词： Source separation

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Automation 5.0: The key to Systems Intelligence and Industry 5.0

引用

IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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A study of the limits of imaging capability due to water scattering effects in underwater ghost imaging

arXiv

引用

arXiv 2023年

作者： Yuliang, L.I. Chen, Mingliang Jinquan, Q.I. Deng, Chenjin Longkun, D.U. Zunwang, B.O. Chang, H.A.N. Zhihua, M.A.O. Yan, H.E. Shao, Xuehui Han, Shensheng Key Laboratory of Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou310012 China Key Laboratory of Space Laser Communication and Detection Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China National Laboratory of Aerospace Intelligent Control Technology Beijing100089 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

Underwater ghost imaging is an effective means of underwater detection. In this paper, a theoretical and experimental study of underwater ghost imaging is carried out by combining the description of underwater optical field transmission with the inherent optical parameters of the water body. This paper utilizes the Wells model and the approximate S-S scattering phase function to create a model for optical transmission underwater. The second-order Glauber function of the optical field is then employed to analyze the scattering field's degradation during the transmission process. This analysis is used to evaluate the impact of the water body on ghost imaging. The simulation and experimental results verify that the proposed underwater ghost imaging model can better describe the degradation effect of water bodies on ghost imaging. A series of experiments comparing underwater ghost imaging at different detection distances are also carried out in this paper. In the experiments, cooperative targets can be imaged up to 65.2m (9.3AL, at attenuation coefficient c=0.1426m-1 and the scattering coefficient b=0.052m-1) and non-cooperative targets up to 41.2m (6.4AL, at c=0.1569m-1 and b=0.081m-1) . By equating the experimental maximum imaged attenuation length for cooperative targets to Jerlov-I water (b=0.002m-1 and a=0.046m-1), the system will have a maximum imaging distance of 193m. Underwater ghost imaging is expected to achieve longer-range imaging by optimizing the system emission energy and detection sensitivity. © 2023, CC BY-NC-SA.

关键词： Light transmission

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