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6th Euro-China Conference on intelligent data Analysis and Applications, ECC 2019

作者： Chang, Kuo-Chi Chu, Kai-Chun Lin, Yuh-Chung Nguyen, Trong-The Sung, Tien-Wen Zhou, Yu-Wen Pan, Jeng-Shyang School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Business Management Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9789811650352

The WSN uses wireless communication to realize its information transmission. WSN is a network data system composed of a large number of wireless data collectors with sensing functions. In the WSN-based sensing function structure, the size should be small, more power saving, low price, and can achieve design goals. This study first assumes that WSN uses routing algorithm assumptions in the protocol, and starts from the node position and direction information, and considers the dynamic evolution of the network. Each public node has random mobility, and the WSN node can help determine the location information of the public node, and then use the node location information to have effective routing performance and effective. However, the experimental results also strongly support the energy saving ability of this research. In the overall system energy saving and power consumption reduction, the actual electric energy hazard is greatly reduced, so in the spirit of ISD strategies, if the WSN system is installed in a high-risk and explosive atmosphere, it can significantly improve the safety level. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

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TFGDA: exploring topology and feature alignment in semi-supervised graph domain adaptation through robust clustering 24

TFGDA: exploring topology and feature alignment in semi-supe...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

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Analysis of parallel helix tension measurement based on frequency domain reflection method 5

Analysis of parallel helix tension measurement based on freq...

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5th International Conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Li, Chaopeng Li, Qing Chen, Huamin Xu, Xiajie Miao, Xinyu Zhang, Yangke College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China National and Local Joint Engineering Research Center for Disaster Monitoring Technology and Instruments China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability and Application Zhejiang Hangzhou310061 China Jinhua Geological Environment Monitoring Station Zhejiang Jinhua321000 China

ISBN: (纸本)9781510668621

Distributed measurement of underground rock and soil deformation is an important part of geological hazard monitoring. Using parallel helical transmission lines as sensors and combining frequency domain reflection (FDR) analysis enables continuous distributed measurement of larger deformation quantities. It is known that the characteristic impedance of the parallel helical transmission line increases with the increase of tensile force. Through FDR, the measurement and location of the tensile point can be determined. This paper explains the principle of FDR measurement of tensile deformation in parallel helical transmission lines. In response to practical application issues of FDR in parallel helical transmission lines, this paper analyzes the influence of sweep range, frequency, and number of sampling points on measurement results through experimental data. The paper also measures different incident signals under different circumstances, identifies and analyzes factors that may affect measurement results, and selects a better measurement scheme. The proposed method is used to simulate the actual use of parallel helical transmission lines and obtain actual measurement data, which is analyzed for better understanding. © 2023 SPIE.

关键词： Electric lines

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keyword-Oriented Multimodal Modeling for Euphemism Identification

arXiv

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arXiv 2025年

作者： Hu, Yuxue Li, Junsong Chen, Meixuan Su, Dongyu Wang, Tongguan Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

Euphemism identification deciphers the true meaning of euphemisms, such as linking "weed" (euphemism) to "marijuana" (target keyword) in illicit texts, aiding content moderation and combating underground markets. While existing methods are primarily text-based, the rise of social media highlights the need for multimodal analysis, incorporating text, images, and audio. However, the lack of multimodal datasets for euphemisms limits further research. To address this, we regard euphemisms and their corresponding target keywords as keywords and first introduce a keyword-oriented multimodal corpus of euphemisms (KOM-Euph), involving three datasets (Drug, Weapon, and Sexuality), including text, images, and speech. We further propose a keyword-oriented multimodal euphemism identification method (KOM-EI), which uses cross-modal feature alignment and dynamic fusion modules to explicitly utilize the visual and audio features of the keywords for efficient euphemism identification. Extensive experiments demonstrate that KOM-EI outperforms state-of-the-art models and large language models, and show the importance of our multimodal datasets 1 Copyright © 2025, The Authors. All rights reserved.

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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An anonymous authentication and key exchange protocol in smart grid

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第2期6卷 206-215页

作者： Luo, Yi Zheng, Wei-Min Chen, Yeh-Cheng Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350100 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science University of California DavisCA95616 United States

With the use of a large number of digital devices in smart grid, how to protect the security of communication in Advanced Metering Infrastructure (AMI) has become a difficult problem. AMI plays an important role in smart grid, which is used two-way communication between users and electricity companies. AMI includes smart meters(SM), data collectors, AMI Head-End(AHE), etc. In this paper, we present an authentication and key exchange protocol between the smart meter and the AMI Head-End in smart grid. The security of proposed scheme is proved by random oracle model and BAN logic. At the end of the paper, the performance analysis shows that the scheme is efficient and suitable for AMI. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Authentication

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EDSep: An Effective Diffusion-Based Method for Speech Source Separation

arXiv

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arXiv 2025年

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method’s superior performance over existing diffusion and discriminative models, validating its efficacy. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Analysis and Optimization of Ancient Building Lighting Based on DIALux 32

Analysis and Optimization of Ancient Building Lighting Based...

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第32届中国控制与决策会议

作者： Dou Jiaming Ma Hongyan Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

In order to provide a better interior visual experience for visitors in ancient buildings, insufficient illumination and non-uniformity need to be solved urgently. DIALux software is used to simulate an ancient building to find out its problems, like non-uniformity and north-east corner illumination lack. By the use of controlling variable method, six different cases were proposed to optimize the lighting environment. The results show that case 3 and case 4 are chosen as the best cases to solve the defects. It is verified that the overall environment has better illumination and uniformity.

关键词： Ancient Building Lighting Environment Optimization DIALux

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Chaotic Neural Network with Legendre Function Self-feedback and Applications

Chaotic Neural Network with Legendre Function Self-feedback ...

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作者： Zhang, Yu Wei, SiFan Wang, Yu Xu, Yaoqun School of Computer and Information Engineering Harbin University of Commerce Harbin150028 China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin150028 China Heilongjiang Cultural Big Data Theory Application Research Center Harbin150028 China Institute of System Engineering Harbin University of Commerce Harbin150028 China

A new transient chaotic neuron model which introduces Legendre function into the self-feedback term is constructed. The dynamic characteristics of the single neuron are analyzed by the time evolution graph and the inverted bifurcation graph of the largest Lyapunov exponent. The network parameters are set for settling the combinatorial optimization problem. The effectiveness of this model has been verified by the simulation results in nonlinear function optimization, traveling salesman problem (TSP). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traveling salesman problem

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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