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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Lai, Xiaochen Chen, Hui Zhang, Liyong School of Software Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350335972

Missing values of real-world datasets bring challenges to data mining. Popular methods for handling missing values are to replace missing values in a dataset using predicted values, called imputation, which is a fundamental task in many research fields. In this paper, we propose an auto-associative neural network with local neighborhood information for missing values imputation. Specifically, we introduce local neighborhood information into auto-associative neural network to make the spatial relationships among samples work. With the spread of neighbor information, the similarity of samples is used to supplement the missing attributes of incomplete samples, which facilitates network analysis of attributes associations. Besides, we propose a training method to iteratively update estimation by treating missing values as variables, in which the imputation error and the incomplete model inputs are continuously optimized during training. The training method allows incomplete samples to participate in the network training as well, enabling all known attributes to be useful. Experiments based on several public datasets validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Data mining

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Image Classification Algorithm for Graphite Ore Carbon Grade Based on Multi-scale Feature Fusion 13th

Image Classification Algorithm for Graphite Ore Carbon Grade...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Huang, Xueyu Shi, Haoyu Liu, Yaokun Lu, Haoran School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Key Laboratory of Virtual Digital Factory and Cultural Communication Jiangxi University of Science and Technology Nanchang330013 China

ISBN: (纸本)9783031554704

Based on the tedious process of using a carbon-sulfur analyzer to detect the carbon grade of graphite in graphite mining production, this paper proposes a graphite carbon grade image recognition and classification method based on multi-scale feature fusion. The experiment preprocesses the images and constructs a residual network model that combines pyramid convolution (PyConv) and spatial attention mechanism (SAM). This model enhances the extraction of both global and local feature information from graphite images. Transfer learning is introduced by using pre-trained weights to accelerate the convergence of the model, achieving efficient and accurate recognition and classification of graphite carbon grade with an accuracy of 92.5%, surpassing traditional machine learning methods using single features. The experimental results demonstrate that the neural network model constructed in this paper effectively extracts texture and color features from graphite images, improving the accuracy of graphite image classification and recognition. The model exhibits good robustness and provides valuable insights for practical graphite mining production. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Graphite

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Analysis of china photovoltaic market development trend 4

Analysis of china photovoltaic market development trend

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4th International Conference on Laser, Optics, and Optoelectronic technology, LOPET 2024

作者： Yuan, Quanhong Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Depart. Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan523083 China

ISBN: (纸本)9781510681903

In recent years, China is the world's largest PV producer, the most complete PV industry chain, the largest PV market and the largest PV power generation capacity. Under the dual-carbon target, the future development trend of China's PV market is very important. Based on the sample data of China's annual cumulative PV installations from 2001 to 2022, its regression equations were obtained by MATLAB software using cubic polynomials, exponential curves, Peel's growth curves, linderuo's growth curves, quadratic exponential smoothing, and BP neural network training models, respectively. After comprehensive analysis, the cubic polynomial curve and exponential curve fitting are better in terms of continuity, growth, reliability and rapidity, but the exponential method accelerates the error with the increase of extrapolation year, so it is recommended to use the cubic polynomial curve. Finally, the cubic polynomial is used to forecast the annual cumulative installed capacity of PV (including distributed and centralized) in China from 2023 to 2030. Comparing with the results predicted by the China Photovoltaic Industry Association (CPIA), the prediction results of this paper are on the small side, which is mainly due to the neglect of the effects of favorable policies, technological breakthroughs, and increasing domestic and foreign market demand. © 2024 SPIE.

关键词： MATLAB

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Machine learning-assisted multi-stage highly sensitive electronic skin tactile sensing tracking platform

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Science China Materials 2025年

作者： Shun Liu Ziqi Wang Duo Pan Hu Liu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment National Engineering Research Center for Advanced Polymer Processing Technology Zhengzhou University

Human skin sensory system, featuring a sophisticated threedimensional(3D) distribution of mechanoreceptors within the skin, possesses an exceptional ability to perceive a diverse range of external mechanical stimuli a...

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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25th International Conference on Web Information Systems engineering, WISE 2024

作者： Tu, Longchuan Meng, Shunmei Liu, Xiao Liu, Guanfeng Beheshti, Amin Zhang, Xuyun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjin China State Key Laboratory for Novel Software Technology Nanjing University Nanjin China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)9789819605699

Recently, graph neural networks(GNNs) have played a key crucial in many recommendation situations. In particular, contrastive learning-based hypergraph neural networks (HGNNs) are gradually becoming a research focus for addressing issues of data sparsity and noise. Despite many studies proving their outstanding performance, there are still some shortcomings: i) Most contrastive learning-based HGNNs primarily rely on cross-view contrastive learning, while neglecting contrastive learning on the interaction graph. ii) Utilizing node embedding for hypergraph structure learning is susceptible to the influence of low-quality representations, thereby constraining the learning capability of HGNNs. To address these issues, we offer a Semantic Similarity-based Graph Contrastive Learning framework (SSGCL), which aims to jointly learn representations with rich semantic information through within-view and cross-view contrastive learning. Specifically, we first introduce consistency contrastive learning, which captures self-supervised signals through semantic similarity between nodes and their neighborhoods. It better captures the unique features of nodes through the connections between nodes and their neighborhoods. Then, we utilize interaction graph learning hypergraph structure, promoting it to extract potential node connections and thus improving its ability to describe homogeneous node relationships. Experimental evaluations on three actual datasets show that SSGCL performs much better than the current baseline models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Concept Drift Tolerant Abnormal Detection Method for Network Traffic 15

A Concept Drift Tolerant Abnormal Detection Method for Netwo...

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15th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

作者： Wang, Xu Han, Li Sun, Shimin Xu, Guowei Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin China Tiangong University School of Software Tianjin China

ISBN: (纸本)9798350308693

we propose a cutting-edge solution that leverages passive adaptive methods based on ensemble learning to effectively detect anomalous traffic in data streams. Our approach tackles the issue of concept drift by integrating various deep learning models, each offering a unique perspective to learn data features. By doing so, our solution can accurately capture the distribution of data concepts and propose weight layers to make the model adaptable to changes caused by concept drift. In a series of experiments conducted on public datasets, our model demonstrated remarkable effectiveness in detecting anomalies, achieving accuracy rates of over 97% after adapting to concept drift. © 2023 IEEE.

关键词： Learning systems

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A quantum federated learning framework for classical clients

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Science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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Enrich Code Search Query Semantics with Raw Descriptions 19th

Enrich Code Search Query Semantics with Raw Descriptions

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Liu, Xiangzheng Liu, Jianxun Hu, Haize Liu, Yi School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search can recommend relevant source code according to the development intention (query statement) of the demander, thereby improving the efficiency of software development. In the research of deep code search model, code description is used to replace query sentences for training. However, the heterogeneity existing between the query statement and the code description will seriously affect the accuracy of the code search model. In order to make up for the shortcomings of code search, this paper proposes a sentence-integrated query expansion method—SIQE. Unlike previous query expansion methods that focus on word-level expansion, SIQE uses the entire code description fragment as the source of query expansion. And by learning the mapping relationship between the query statement and the code description, the heterogeneity problem between them is compensated. In order to verify the effect of the proposed model in code search tasks, the article conducts code search experiments and analyzes on two languages: python and java. Experimental results show that, compared with the baseline model, SIQE has higher code search results. Therefore, the SIQE model can effectively improve the search effect of query statements, improve the accuracy of code search, and further improve the development of software engineering. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep learning

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Hybrid Heterogeneous Wireless Chargers Placement 18th

Hybrid Heterogeneous Wireless Chargers Placement

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Hou, Sujin Deng, Peng Ren, Meixuan Xu, Yun Zhu, Yuzhang Dai, Haipeng Wu, Libing School of Cyber Science and Engineering Wuhan University Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9783031714665

This paper presents the first study on the problem of Hybrid heterogeneous wireless chARgers Placement (HARP), i.e., given a number of heterogeneous deployed rechargeable devices, determining deployment positions for multiple heterogeneous stationary chargers with a given cardinality of each type and partial selected devices with energy allocation for multiple mobile chargers to maximize the overall charging utility. The HARP problem is essentially NP-hard and non-submodular, which poses huge challenges. We first propose an approach to approximate the nonlinear charging power and partition the whole area into multi-power constant subareas, after that, we present a charging discretization method and construct a mapping function over the selected device set to transform the problem of deploying a single mobile charger into maximizing a monotone submodular function subject to a general routing constraint. It allows us to propose a benefit-cost ratio algorithm to solve it. Finally, we propose an efficient approximation algorithm that achieves 1-1/e5-1/e-approximation ratio to address HARP. We conducted extensive simulations and field experiments verifying that our algorithm outperforms the comparison algorithms by at least 17.49% on average. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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