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3rd International Conference on Electronic information Engineering and Computer, EIECT 2023

作者： Huang, Yuesheng Chen, Sida Wu, Qiling Xiao, Jiapeng Ren, Yongqi Wang, Leijun Chen, Rongjun Li, Jiawen Guangdong Polytechnic Normal University School of Computer Science Guangzhou China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9798350357707

With the growing global energy demand and requirement for environmental protection, renewable energy is attracting attention as a vital development direction. Particularly, wind power is rapidly developing as a clean and sustainable form of energy. Nonetheless, wind speed uncertainty and system dynamics make wind power prediction and optimization complex and challenging. Accurate wind power prediction is essential to improve system reliability and performance. In this regard, traditional forecasting methods have limitations in some cases. In this study, based on XGBoost and decision tree with Bagging methods in ensemble learning, wind power data is predicted. The results indicated that better performances than the traditional linear regression and ordinary decision tree models are achieved, revealing the efficiency of the proposed approach. © 2023 IEEE.

关键词： Decision trees

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Design and realization of precision agriculture information system based on 5S

Design and realization of precision agriculture information ...

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International Conference on Geoinformatics

作者： Wang, Xiaoshan Qi, Qingwen Geomatics College Shandong University of Science and Technology Qingdao China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Nature Resources Research Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781612848488

The brief of this paper is to discuss the design and realization of precision agriculture information system based on 5S. The precision agriculture is representing the direction of agriculture development, is also the focal point of agriculture research. In this paper, the concept of precision agriculture and the features of 5S technology are briefly introduced. Then an application of 5S technology and database technology to the developing precision agriculture, and an establishment of precision agriculture information system based on 5S technology integration are proposed. The total structure, hardware and software environment, database design and function module design, realization methods of this system are also introduced. © 2011 IEEE.

关键词： Precision agriculture

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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 *** 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 *** 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 *** 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 ***,for a special case where the semantic state is a deterministic function of the observation,we design a cascade neural network to estimate the *** the case with perfectly known semantic source distribution,we propose a general Blahut-Arimoto(BA)algorithm to effectively compute the ***,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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Experimental Results of Federated Learning in Unmanned Aerial Vehicle Swarm

Experimental Results of Federated Learning in Unmanned Aeria...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Shen, Yun Dong, Chao Qu, Yuben Ren, Weiqing Zhu, Xiaojun Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

Unmanned aerial vehicles (UAVs) need to deploy machine learning (ML) models to execute complex tasks such as target tracking. For traditional centralized ML, UAVs need to transmit the raw data to the ground central server for model training. However, the transmission of raw data not only causes a large delay but also may incur sensitive information leakage. Federated learning (FL) as a distributed machine learning enables multiple UAVs to form a swarm for distributed model training. Nevertheless, most of current researches on FL of UAV swarm are based on theoretical architecture and simulations. In this paper, we focus on verifying the feasibility of deploying FL in a UAV swarm through practical experiments. Moreover, we further explore the impact of the distance change between the training node UAVs and the parameter server UAV on the training accuracy. The experimental results show that the UAV swarm can perform 15 rounds of global training in 5 minutes, with a training accuracy of 84.28%. © 2021 IEEE.

关键词： Antennas

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ding, Yi Zhao, Lu Yuan, Liming Wen, Xianbin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin300384 China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China

ISBN: (纸本)9781665468190

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability. © 2022 IEEE.

关键词： Aggregates

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Building Energy Optimization Based on Biased ReLU Neural network

Building Energy Optimization Based on Biased ReLU Neural Net...

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第40届中国控制会议

作者： Hongyi Li Xinglong Liang Jun Xu Harbin Institute of Technology Key Laboratory of System Control and Information Processing Ministry of Education

This paper proposes a building energy optimization strategy based on artifical intelligence technology modeling ***,the data set generated by Energy Plus energy consumption simulation software is used as the training set and test set of the Biased ReLU neural network（BRNN）.Secondly,the building energy consumption prediction model and indoor temperature prediction model are built based on the Biased ReLU neural ***,model predictive control（MPC） is uesd to achieve energy saving by controlling the set temperature of the building’s Heating,Ventilation and Air Conditioning（HVAC） ***,the joint simulation of MATLAB and EnergyPlus is realized by introducing the building control virtual test bed（BCVTB）.The results show that our method can effectively reduce building energy consumption.

关键词： Biased ReLU neural network MPC building energy consumption HVAC system

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A minimum entropy approach for multiple-model estimation

A minimum entropy approach for multiple-model estimation

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15th International Conference on information Fusion, FUSION 2012

作者： Han, Shen-Tu Xue, An Ke Peng, Dong Liang State Key Laboratory of Industrial Control Technology Institute of Cyber-System and Control Zhejiang University Hang Zhou 310027 China Institution of Information and Control Hangzhou Dianzi University Hang Zhou 310018 China

ISBN: (纸本)9780982443859

Multiple-model (MM) methods are effective in handling mode uncertainties and the variable structure multiple-model (VSMM) approach is one technique of the state of art. However, designing a better model set adaptive (MSA) mechanism is still a challenging problem both in theory and practice. In this paper, we present new theoretical analysis to evaluate the quality of model sequence sets in a nested structure which implies a principle - to find effective model sequence sets with the smallest size if the risk of missing real modes is small enough. A minimum entropy multiple-model (MEMM) approach is proposed to calculate the model sets with minimum Shannon entropy through feed back the online information into the fusion center. Sub-optimal MEMM algorithms are also designed with a particle filter. An example of maneuvering target tracking is considered in simulations. The proposed algorithms are compared to several existing VSMM algorithms and the results show that the MEMM algorithms are robust and effective in both estimation precision and converging rate. © 2012 ISIF (Intl Society of information Fusi).

关键词： Target tracking

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Performance Analysis of Velocity Perturbation Control in Mixed Platoons with Connected Autonomous and Human-Driven Vehicles

Performance Analysis of Velocity Perturbation Control in Mix...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Duan, Xuting Zhao, Yihan Xia, Haiying Zhou, Jianshan Zhang, Long Zhao, Dezong School of Transportation Science and Engineering Beihang University Beijing China Key Laboratory of Operation Safety Technology On Transport Vehicles Ministry of Transport Beijing China Institute of Systems Engineering National Key Laboratory of Science and Technology On Information System Security Beijing China James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

As the Internet of vehicles and autonomous driving are not widely popularized, it is of practical significance to investigate the mixed platoon composed of connected autonomous and human-driven vehicles. This paper focuses on the velocity perturbation control of the mixed platoon. We first use an intelligent driver model (IDM) to build a general scene of an individual connected autonomous vehicle (CAV) guiding multiple human-driven vehicles (HDVs). Using this model, the paper theoretically derives the difficulty calculation of CAV controlling the velocity perturbation at a certain position in the whole platoon. The results show the relationship between the control difficulty and the control time of velocity perturbation occurring at different positions. Finally, the numerical simulation results verify that CAV has the potential to guide the HDVs behind it and eliminate the velocity perturbation. Interestingly, when the velocity perturbation occurs in a HDV far away from the CAV, the velocity perturbation will continue to exist for a long time. © 2022 IEEE.

关键词： Autonomous vehicles

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Image super-resolution reconstruction based on low-rank matrix and dictionary learning

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2016年第4期53卷 884-891页

作者： Yang, Shuaifeng Zhao, Ruizhen Institute of Information Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing100044 China

Super-resolution (SR) reconstruction based on sparse representation and dictionary learning algorithm does not decompose the image at first. It reconstructs the image with its whole information based on sparse representation and dictionary learning algorithm directly. It is said that images can be decomposed into low-rank part and sparse part by low-rank matrix theory. Using different methods according to the characteristics of the different parts can be more effective to use the characteristics of the image. This paper proposes a super-resolution reconstruction method based on low-rank matrix and dictionary learning. The method obtains the low-rank part and sparse part of the original image via low-rank decomposition at first. The low-rank part retains most of the information of the image. The algorithm reconstructs the image based on dictionary learning method only for the low-rank part. The sparse part of the image reconstruction is not involved in the learning method, instead its reconstruction is based on linear interpolation method directly. Experimental results show that it can not only enhance the quality of the image reconstruction but also reduce the time of the reconstruction. Compared with existing algorithms, our method obtains better results in the visual effects, the peak signal to noise ratio and the running speed of the algorithm. © 2016, Science Press. All right reserved.

关键词： Image reconstruction

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Pre-training Fine-tuning data Enhancement method based on active learning

Pre-training Fine-tuning data Enhancement method based on ac...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Deqi Cao Zhaoyun Ding Fei Wang Haoyang Ma Key Laboratory of Information System Engineering National University of Defense Technology Changsha China

With the development of Internet technology, the number of Internet users increases rapidly, and the amount of data generated on the Internet is very large every day. At the same time, with the development of storage technology and query technology, it is very easy to collect massive data, but the information value contained in these data is uneven, and most of them are unmarked. However, traditional supervised learning has a great demand for labeled samples. Faced with a large number of unlabeled samples, there is a problem of the lack of effective automatic labeling methods, and manual labeling costs are high. If the strategy of simple random sampling is used for annotation, it may lead to the selection of noisy information and waste of resources, and low-quality training data could also have an influence on the prediction accuracy of the model. Meanwhile, the training effect of traditional deep learning methods is very limited for small sample labeled training *** paper takes the text emotion analysis task in natural language processing as the background, selects IMDB film review data as the training set and test set, starts with the design of active learning algorithm based on clustering analysis, combined with the appropriate pre-training fine-tuning model, constructs a data enhancement method based on active learning. In the experiment, it is found that when the labeled training set is reduced by 90%, the prediction accuracy of the pre-training model is reduced by no more than 2%, which verifies the effectiveness of the data enhancement method combining active learning with the pre-training model.

关键词： Training Analytical models Costs Annotations Training data Predictive models Data models

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