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IEEE International Conference on data mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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Electric Vehicle Charging Control Based on Smart Grid Big data

Electric Vehicle Charging Control Based on Smart Grid Big Da...

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Ze Xu Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou North Borneo University College Sabah Malaysia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

At the same time, the burning of a large amount of fossil fuels has aggravated environmental pollution and the greenhouse effect. In order to solve the problems of energy shortage and environmental pollution, electric vehicle technology has been highly valued by many countries. Electric vehicles are the most promising new energy vehicles. Therefore, on the basis of fully considering the charging load characteristics of electric vehicles, how to better establish charging facilities and adjust the impact of a large number of random loads on the grid. Therefore, it is of great significance and application value to study the charging strategy and control method of electric vehicles to improve the stable operation level of the power grid and the ability to accept new energy. Here, the frequency modulation control strategy of electric vehicles considering the restriction of electricity price is studied to ensure the regulation ability and charging demand of electric vehicles.

关键词： Industries Pollution Frequency modulation Greenhouse effect Electric vehicles Regulation Fossil fuels

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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Design of Primary System for 110kV Smart Substation

Design of Primary System for 110kV Smart Substation

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International Conference on Intelligent Computing, Automation and Systems (ICICAS)

作者： Kuo-Chi Chang Hongkai Chen Kai-Chun Chu Fu-Hsiang Chang Hsiao-Chuan Wang Tsui-Lien Hsu Yuh-Chung Lin Yuwen Zhou North Borneo University College Sabah Malaysia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Fujian University of Technology Fuzhou China Shih-Hsin University Taipei Taiwan Institute of Environmental Engineering National Taiwan University Taipei Taiwan Institute of Construction Engineering and Management National Central University Taoyuan Taiwan

Substation is the bridge between power plant and load, and it is the place where high-quality energy is transported. And China's power technology has been in the world's top level, because of the complex terrain in China, so the design of power station should consider the impact of terrain factors, economic factors, and future development factors and so on. Therefore, smart substation design is worthy of our research and innovation. Installation of lightning arrester can effectively reduce the threat of lightning.

关键词： Wiring Arresters Substations Short-circuit currents Lightning Stability analysis Circuit stability

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A 1D Plug-and-Play Synthetic data Deep Learning For Undersampled Magnetic Resonance Image Reconstruction

A 1D Plug-and-Play Synthetic Data Deep Learning For Undersam...

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IEEE International Conference on Image Processing

作者： Min Xiao Zi Wang Jiefeng Guo Di Guo Xiaobo Qu Institute of Artificial Intelligence Xiamen University Xiamen China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen China Department of Microelectronics and Integrated Circuit Xiamen University Xiamen China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Magnetic resonance imaging (MRI) plays a pivotal role in modern medical diagnosis yet is often hindered by the long imaging time. MRI imaging can be accelerated through undersampling, but the introduced aliasing artifacts should be removed during image reconstruction. While deep learning reconstruction methods excel at image de-aliasing, they may yield suboptimal results when training sampling settings differ from those at the time of reconstruction. To decouple from specific sampling settings, we propose using synthetic data to generate a substantial training dataset and pre-train a 1D deep denoiser. We then integrate the trained deep denoiser into the iterative reconstruction process as a replacement for the approximation operator within the deep plug-and-play framework. In vivo results indicate that the proposed method exhibits robust and visually appealing image reconstruction when there is a mismatch between the training and reconstruction undersampling settings, such as different undersampling patterns and sampling rates.

关键词： Training Deep learning In vivo Magnetic resonance imaging Magnetic resonance Training data Trajectory

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Identification of misleading product description in e-commerce website

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Ruan Jian Xue Bao/Journal of Software 2014年 25卷 127-135页

作者： Long, Yin Liu, Hong-Yan He, Jun Hu, He Du, Xiao-Yong Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China Beijing100872 China School of Economics and Management Tsinghua University Beijing100084 China

Online shopping has been accepted by more and more consumers. C2C websites provide thousands of offers for consumers as a mainstream e-commerce platform. When customers search products in C2C website, some returned offers have misleading description. Misleading description means that the description does not convey the actual price of products, but usually claiming much lower price for the purpose of attracting more consumers. The misleading offers affect consumers' judgments and bring bad influences on the websites' reputation. This paper proposes an approach that combines statistical model HMM with statistical outlier detection method to detect misleading offers. HMM model is built to determine the product that an offer description really designates, providing an efficient solution to eliminate the ambiguity of the offer description caused by description irregularities. The statistical outlier detection method is effective to deal with limited product offer information. The paper further conducts experiments on real data set of electric business websites and the results demonstrate the effectiveness of the proposed approach. ©2014 ISCAS.

关键词： Websites

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Capability enhanced trust evaluation model for Web services

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第8期31卷 1471-1477页

作者： Li, Hai-Hua Du, Xiao-Yong Tian, Xuan Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Information Science and Technology Beijing Forestry University Beijing 100083 China

In open environment, an effective trust mechanism should be built to ensure transaction safety. But current trust evaluation approaches solely based on the certificate or feedback are inaccurate and ineffective. Thus, referring to the social trust models, this paper presents a capability enhanced method to improve the accuracy of evaluating trustworthiness. Based on it, this paper also designs a trust evaluation model ServTrust that naturally provides a solution to the trust problem. Compared with current feedback based approaches, simulation experimental results show the effectiveness of the proposed model in trust evaluating.

关键词： Web services

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Generative AI for Advanced UAV Networking

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IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

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Ship Detection Using the Surface Scattering Similarity and Scattering Power

Ship Detection Using the Surface Scattering Similarity and S...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tao Zhang Zhen Y ang Bo Mao Jian Y ang Yifang Ban Huilin Xiong Shanghai Key Lab. of Intelligent Sensing and Recognition Shanghai Jiao Tong University Shanghai China Jiangxi Science and Technology Normal University Nanchang China Jiangsu Key Laboratory of Grain Big Data Mining and Application Nanjing University of Finance & Economics Nanjing China Department of Electronic Engineering Tsinghua University Beijing China Division of Geoinformatics KTH Royal Institute of Technology Stockholm Sweden

Sea surface and ship have different backscattering mechanisms, in which surface scattering is predominant for sea surface in the low sea state case. Based on this fact, many ship detectors have been developed by suppressing the surface scattering resulted from sea surface. Actually, small ship may also have strong surface scattering sometimes. In such a case, the methods of avoiding using surface scattering features may easily miss the detection of small ships. To verify this point, in this paper, we first analyze the shortcomings of An's method which is based on surface scattering similarity and the power maximization synthesis detector (PMS), and then improve it for detecting small ships more effectively. In order to demonstrate the performance of the proposed method, AIRSAR L-Band Polarimetric SAR dataset is exploited. Comparing to other methods, the new method shows a better ship detection performance.

关键词： Marine vehicles Scattering Sea surface Covariance matrices Detectors Synthetic aperture radar Backscatter

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An automatic colonies counting based on piecewise circle fitting

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Journal of Information Hiding and Multimedia Signal Processing 2017年第3期8卷 660-669页

作者： Luo, San-Ding Zou, Zheng Tian, Guang-Ya Pan, Jeng-Shyang Liu, Shi-Jian School of Information Science and Engineering Central South University No.932 Lushannan Road ChangshaHunan410005 China School of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China The Key Laboratory of Big Data Mining and Applications of Fujian Province Fujian University of Technology No.3 Xueyuan Road University Town Minhou FuzhouFujian350118 China

And counting the total number of bacterial colonies on agar plates can offer essential indicator for microorganism survival rates. However, manual counting is time-consuming while for computer-aided colony counting, the adhesive colonies usually make the colonies counting challenging. To improve the accuracy and efficiency, we pro- pose a fully automatic counting method. Firstly, a color identification process is used for distinguishing the colonies with different color. Secondly, according to the curvature characteristic along the colony boundary, a segments extraction strategy is used for finding the proper split points. Thirdly, piecewise fitting algorithm ranks candidate circles by confidence, and removes the redundant candidate circles to separate touching colonies. Once the circles are determined, all the colonies are marked by corresponding circles. Our method demonstrates a new way of using circle fitting for accurately separating the touching colonies. The experimental results demonstrated that our method is better than two other most cited methods in accuracy and consuming time. © 2017.

关键词： Circle fitting Clustering Colonies counting Colony segmentation Petri dish

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