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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Li, Yang Zhao, Lingchu Zhu, Jia Liu, Yonglin Ding, Jian Yang, Jia Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Hubei Yichang443002 China State Grid Yichang Electric Power Supply Company Hubei Yichang443002 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Covert communication refers to achieving high-level secure transmission by protecting communication behaviour. This paper primarily centres on the examination of covert communication in the scene of the power Internet of Things (PIoT). Specifically, we explore the scenario where power users (PU) transmit information to the master station (MS) while being exposed to potential eavesdropping. In addition, we assume that Willie has noise uncertainty when detecting communication behaviour, which makes it difficult for Willie to accurately detect communication behaviour, and significantly affects the detection error probability (DEP) of Willie and the overall covert performance of the proposed model. It is worth noting that the simulation results indicate that the presence of noise uncertainty can enable MS and PU to successfully achieve covert transmission. By leveraging this uncertainty, MS and PU can effectively protect their communication behaviour and minimize the risk detected by potential Willie, which provides a solution for high-level secure communication in the field of PIoT and effectively protects the privacy of PU. © 2024 SPIE.

关键词： Internet of things

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Liquid Material Identification based on RFID Passive Sensing and Machine Learning

Liquid Material Identification based on RFID Passive Sensing...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Zhao, Liang Xu, Jieru Yao, Yujun Huang, Shuai China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Hubei Yichang443002 China China Three Gorges University College of Computer and Information Technology Hubei Yichang443002 China

ISBN: (纸本)9798350312270

In order to further improve the identification accuracy of unknown liquid materials, this paper proposes a liquid material identification method based on RFID passive sensing and machine learning. Firstly, based on the principle that phase changes and received signal strength (RSS) changes are different when RFID signal passes through different liquids, the phase and attenuation constants of a specific liquid are derived and defined as its two characteristic values. Then, the weighted K-nearest neighbor algorithm (WKNN) is used to optimize the weights of training samples at different distances, while machine learning is performed on labeled liquid feature data. Finally, an experimental system based on Impinj R420 RFID reader is established and numerous experimental tests are carried out. The test results show that the proposed approach can effectively identify ten common liquids such as beer, coke, spirit and milk, with an average identification accuracy of 99.4%, which is 4.1% higher than the existing methods, implying its good theoretical significance and application value. © 2023 IEEE.

关键词： Machine learning

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Combined model electricity load forecasting based on NeuralProphet and Bi-LSTM-SA

Combined model electricity load forecasting based on NeuralP...

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2024 International Conference on Smart Grid and Artificial Intelligence, SGAI 2024

作者： Zhao, Dongpeng Xu, Shouzhi Sun, Haowen Li, Bitao Jiang, Mengying Tan, Shiyu Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang Hubei443002 China College of Computer and Information Technology China Three Gorges University Yichang Hubei443002 China

This study proposes an innovative method for forecasting electricity load that combines NeuralProphet's time series analysis capability with Bi-LSTM-SA's self-attention mechanism. The method improves prediction accuracy, reliability, and interpretability by analyzing trends, cycles, and holiday impacts, as well as considering climatic factors as key external variables. A peak interval weighted mean square error indicator is introduced to optimize the weights in the model combination strategy. This improves the prediction accuracy during peak times, making this method superior to any single sub-model in terms of prediction performance. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

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Heterogeneous data alignment for cross-media computing 7

Heterogeneous data alignment for cross-media computing

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7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015

作者： Wei, Shikui Wei, Yunchao Zhang, Lei Zhu, Zhenfeng Zhao, Yao Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Institute of Information Science Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781450335287

Massive data sets are generated everyday, and knowledge spreading trends to involve diverse media types and infor-mation sources. The new situation emerges some new chal-lenging research problems: (1) How to bridge the hetero-geneity and mine the shared information among cross-view representations? (2) How to build the semantic association among heterogeneous information objects. (3) How to fully explore the complementary information underlying hetero-geneous information objects to cooperatively make decision. In fact, the core of these problems is to align heteroge-neous data. In this paper, we first give a short introduction about heterogeneous data alignment. And then, two ongo-ing works in our group about heterogeneous data alignment are discussed, i.e., consistent pattern mining and modality-dependent cross-media retrieval. Finally, we conclude this paper and discuss some potential applications of heteroge-neous data alignment. © 2015 ACM.

关键词： Semantics

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UAVs-Assisted Edge Networks Optimization based on Constrained Particle Swarm Algorithm 23

UAVs-Assisted Edge Networks Optimization Based on Constraine...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Zhang, Lei Wen, Fangqing Li, Xudong Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China College of Computer and Information Technology China Three Gorges University Yichang China

ISBN: (纸本)9798350325959

The mobility of unmanned aerial vehicles (UA Vs) has attracted widespread attention in mobile edge computing (MEC). Due to limitations in UAVs communication capacity and onboard power, the performance of UA Vs-assisted MEC networks faces significant challenges. To address this, this paper constructs a multi-objective optimization model for a UAVs-assisted MEC networks and derives multi objective functions that characterize network performance, including capacity, energy consumption, and latency. Furthermore, a constraint particle swarm optimization algorithm with low computational complexity is proposed. This includes designing a multi-objective evaluation strategy that balances diversity and convergence, as well as an adaptive constraint handling mechanism for managing constraints. Finally, the proposed algorithm is used to simultaneously optimize the capacity and energy consumption while meeting the requirements of UAVs computational resources and user task latency. Experimental results demonstrate that our algorithm can significantly reduce energy consumption while enhancing capacity and transmission rate, without significantly increasing algorithm complexity. © 2023 IEEE.

关键词： mobile edge computing particle swarm optimization UAVs communication

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Bi-LSTM-based load forecasting for power grid

Bi-LSTM-based load forecasting for power grid

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2024 International Conference on Smart Grid and Artificial Intelligence, SGAI 2024

作者： Xu, Shouzhi Sun, Haowen Li, Bitao Zhao, Dongpeng Li, Hao Wang, Ke Liu, Jingwen Wang, Xiaoyu Hubei Key Laboratory of Intelligent Vision-based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang Hubei China Yichang Power Supply Company of State Grid Hubei Electric Power Co. Ltd. Yichang Hubei China

It is very important to optimize the power supply and alleviate the conflict of electric load demand, so an accurate and reasonable power load prediction technology has been the focus of research. An evolutional LSTM named Bi-LSTM processes two independent input sequences with two opposite time directions, which aims to improve the efficiency of load forecasting for the power grid. The four main processes are illustrated, which include preprocessing the historical electrical load data, setting the parameters of the model, training the model, and testing the model. Comprising with typical models of RNN, LSTM, and GRU, the performance is verified, and the comparison result shows that it is better in several typical evaluation metrics. The proposed Bi-LSTM model effectively leverages historical data to forecast the imminent state of the power grid, providing valuable insights for electrical load management. © Published under licence by IOP Publishing Ltd.

关键词： Electric power plant loads

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GeoNER:Geological Named Entity Recognition with Enriched Domain Pre-Training Model and Adversarial Training

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Acta Geologica Sinica(English Edition) 2024年第5期98卷 1404-1417页

作者： MA Kai HU Xinxin TIAN Miao TAN Yongjian ZHENG Shuai TAO Liufeng QIU Qinjun Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichangHubei 443002China College of Computer and Information Technology China Three Gorges UniversityYichangHubei 443002China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Key Laboratory of Quantitative Resource Evaluation and Information Engineering Ministry of Natural ResourcesChina University of GeosciencesWuhan 430074China

As important geological data,a geological report contains rich expert and geological knowledge,but the challenge facing current research into geological knowledge extraction and mining is how to render accurate understanding of geological reports guided by domain *** generic named entity recognition models/tools can be utilized for the processing of geoscience reports/documents,their effectiveness is hampered by a dearth of domain-specific knowledge,which in turn leads to a pronounced decline in recognition *** study summarizes six types of typical geological entities,with reference to the ontological system of geological domains and builds a high quality corpus for the task of geological named entity recognition(GNER).In addition,Geo Wo BERT-adv BGP(Geological Word-base BERTadversarial training Bi-directional Long Short-Term Memory Global Pointer)is proposed to address the issues of ambiguity,diversity and nested entities for the geological *** model first uses the fine-tuned word granularitybased pre-training model Geo Wo BERT(Geological Word-base BERT)and combines the text features that are extracted using the Bi LSTM(Bi-directional Long Short-Term Memory),followed by an adversarial training algorithm to improve the robustness of the model and enhance its resistance to interference,the decoding finally being performed using a global association pointer *** experimental results show that the proposed model for the constructed dataset achieves high performance and is capable of mining the rich geological information.

关键词： geological named entity recognition geological report adversarial training confrontation training global pointer pre-training model

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An Efficient Deep Multi-View Clustering Method of Power Users based on a Variational Automatic Encoder 5

An Efficient Deep Multi-View Clustering Method of Power User...

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5th International Conference on Clean Energy and Electric Power engineering, ICCEPE 2024

作者： Ke, Yang Xiao, Wei Chen, Meng Feng, Yi Wu, Ze Yichang Power Supply Company State Grid Hubei Electric Power Co. Ltd. Hubei Yichang China China Three Gorges University Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering Hubei Yichang443000 China

ISBN: (纸本)9798350390315

The electricity consumption behavior characteristics of electricity users are reflected in their electricity load data. Therefore, clustering the electricity users into groups and analyzing their characteristics can help power companies obtain a clear insight and a deeper understanding of the user groups and provide more targeted services accordingly. However, due to the large-scale and high-dimensional nature of power load data, traditional clustering methods face certain difficulties in feature extraction. In view of that, this article proposes a deep multi-view clustering method for power users based on a VAE (variational autoencoder). The proposed method first reduces the dimensionality of power load data by distributing the VAE network and then adopts a deep fusion module to achieve deep mining of the reduced data. Finally, it clusters power users using the clustering module. The experimental results demonstrate that the proposed clustering method can achieve optimal performance on both the Calinski Harabasz index and Silhouette score and a suboptimal result on the Davies Bouldin index. The results presented in this study indicate the practical effectiveness of the proposed approach and provide a solid data foundation for more accurate user behavior analysis for power companies. © 2024 IEEE.

关键词： Dual-scale similarity Electricity Load data Multi-view clustering Variational autoencoder

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AFCNNM: Accelerating Fully Connected Neural Network on Mesh-based Optical Network-on-Chip 22

AFCNNM: Accelerating Fully Connected Neural Network on Mesh-...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Yang, Wen Liao, Yunting China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443000 China China Three Gorges University College of Computer and Information Technology Yichang443002 China

ISBN: (纸本)9798331509712

Fully Connected Neural Network (FCNN) are widely used in image recognition and natural language processing. However, the time cost of training large datasets is high. Optical Network-on Chip (ONoC) has been proposed to accelerate the parallel computing of FCNN because of its advantages. Therefore, this paper proposes an accelerated FCNN model based on ONoC. We first design an FCNN-aware mapping strategy, and then propose a group-based inter-core communication scheme with low wavelength requirements according to the distribution of mapping cores. The optimal number of cores in each period is obtained by achieving the trade-off between the communication and computation time. The simulation results show that the proposed scheme has the advantages of low wavelength requirement, short training time and good scalability. © 2024 IEEE.

关键词： Large datasets

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Multi-objective Optimization Design of Reflective Dual-band Linear-circular Polarizer based on Surrogate Method

Multi-objective Optimization Design of Reflective Dual-band ...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Wu, S.X. Wang, Z.Y. Zhang, Q.H. China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang443002 China China Three Gorges University College of Computer and Information Technology Yichang443002 China

ISBN: (纸本)9798350375909

This paper proposes a scheme for a reflective dual-band line-to-circular polarizer (RLCP) that is more suitable for performance design based on expected requirements, in response to the application demands for high-frequency, high-efficiency, and polarization conversion in satellite communication. The scheme addresses the mutual conflict between the applicable bandwidth (BW) and the maximum angle of incidence (MAI) performance of polarization converters. It is based on a surrogate model (SM) to replace traditional full-wave simulations, and utilizes the axial ratio (AR), BW, and MAI as performance metrics for multi-objective optimization design. The two types of expected requirements, either leaning towards BW or MAI, are evaluated using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to assess the Pareto frontier (PF). Numerical and simulation analyses demonstrate that the designed RLCP can achieve dual-band and dual-polarization conversion functionality in the K/Ka-band satellite communication. When the weight is inclined towards the angle, the RLCP can achieve left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP) in the RX channel (17.8-20.5 GHz) and TX channel (28.2-31.0 GHz) respectively, within a MAI of 62°, meeting the requirement of AR less than 3 dB in the frequency range. When the weight is inclined towards the BW, a MAI of 50° is allowed, ensuring AR less than 3 dB in the LHCP band (18.3-21 GHz) and RHCP band (28.2-31 GHz). Compared with traditional solutions, the proposed design significantly improves design efficiency. According to the actual needs of BW and MAI, the scheme can return the most suitable structural parameters, so as to flexibly design RLCP. © 2024 IEEE.

关键词： Circular polarization

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