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第37届中国控制会议

作者： Xiufen YE Xiubo Wang Department of Automation Harbin Engineering University

The problem that the underwater robot is winded by fishing nets is the most concerned. At present, there are still no effective methods which can detect fishing nets in real time to avoid obstacles. As fishing net images taken with traditional detection technique are blurry and have low definition, it is hard to identify fishing nets. In view of this phenomenon, we proposed a new underwater detection method in this paper and used underwater laser scanning system to collect clear fishing net images. Due to the high cost of data acquisition and the single amount of experiment data, we took advantage of a deep generative network to amplify original data. In fishing nets detection network, we used a residual network as new base net to deepen the network layers, then combined the method of regression with some region suggestion to detect fishing nets, which can improve the accuracy of fishing nets detection without affecting the real-time performance. Finally, the contrast experiment of laser fishing net images is carried out to verify the effectiveness of the proposed method.

关键词： Fishing net detection Laser scanning system generative network Regression network Data amplification

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Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1563-1575页

作者： Wenlong Liao Birgitte Bak-Jensen Jayakrishnan Radhakrishna Pillai Zhe Yang Yusen Wang Kuangpu Liu AAU Energy Aalborg UniversityAalborgDenmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSweden

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

关键词： Renewable energy source scenario generation implicit maximum likelihood estimation(IMLE) deep learning generative network

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Fundamental Limitation of Semantic Communications:Neural Estimation for Rate-Distortion

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Journal of Communications and Information networks 2023年第4期8卷 303-318页

作者： Dongxu Li Jianhao Huang Chuan Huang Xiaoqi Qin Han Zhang Ping Zhang School of Science and Engineering and the Fu-ture network of Inelligence Institute the Chinese University of Hong KongShenzhen 518172China Department of Electrical and Electronic Engineering the University of Hong KongHong Kong 999077China School of Information and Communication Engineering and the State Key Laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China

This paper studies the fundamental limit of semantic communications over the discrete memoryless channel.We consider the scenario to send a semantic source consisting of an observation state and its corresponding semantic state,both of which are recovered at the receiver.To derive the performance limitation,we adopt the semantic rate-distortion function(SRDF)to study the relationship among the minimum compression rate,observation distortion,semantic distortion,and channel capacity.For the case with unknown semantic source distribution,while only a set of the source samples is available,we propose a neural-network-based method by leveraging the generative networks to learn the semantic source distribution.Furthermore,for a special case where the semantic state is a deterministic function of the observation,we design a cascade neural network to estimate the SRDF.For the case with perfectly known semantic source distribution,we propose a general Blahut-Arimoto(BA)algorithm to effectively compute the SRDE.Finally,experimental results validate our proposed algorithms for the scenarios with ideal Gaussian semantic source and some practical datasets.

关键词： Semantic communications semantic ratedistortion generative network Blahut-Arimoto algorithm

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