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Extraction of Behavioral Features Based on Customers' Point Use History

Extraction of Behavioral Features Based on Customers' Point ...

2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Kenji TANAKA Kumada SHU Shen-ming GU Jing ZHANG Department of Systems Innovation The University of Tokyo Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University

To analyze features of customers' point use behavior, we define and classify a unit based on five feature values to extract the behavioral features. Our results will contribute to the construction of a customer-sp... 详细信息

关键词： ID-POS data Point Unit

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Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

arXiv

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arXiv 2021年

作者： Zheng, Yizhen Jin, Ming Pan, Shirui Li, Yuan-Fang Peng, Hao Li, Ming Li, Zhao Department of Data Science and AI Faculty of IT Monash University ClaytonVIC3800 Australia School of Information and Communication Technology Griffith University Australia SouthportQLD4222 Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province Zhejiang Normal University Jinhua321004 China Alibaba Group Hangzhou311100 China

Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information, which is normally expensive to obtain in the real world. Although some existing works aim to effectively learn graph representations in an unsupervised manner, they suffer from certain limitations, such as the heavy reliance on monotone contrastiveness and limited scalability. To overcome the aforementioned problems, in light of the recent advancements in graph contrastive learning, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme. Specifically, this mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales: micro (i.e., node-level), meso (i.e., neighborhood-level), and macro (i.e., subgraph-level). Firstly, we generate two augmented views of the input graph via two different graph augmentations. Then, we establish three different contrastiveness on the above three scales progressively, from node, neighboring, to subgraph level, where we maximize the agreement between graph representations across scales. While we can extract valuable clues from a given graph on the micro and macro perspectives, the neighboring-level contrastiveness offers G-Zoom the capability of a customizable option based on our adjusted zooming scheme to manually choose an optimal viewpoint that lies between the micro and macro perspectives to better understand the graph data. Additionally, to make our model scalable to large graphs, we employ a parallel graph diffusion approach to decouple model training from the graph size. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently. © 2021, CC BY.

关键词： Graph neural networks

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Underwater Mixed Spatial Attention Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Qiang Pan Yueting Yang Chuansheng Yang Chao Wang Anhui Tan School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China

High-quality underwater images play an important role in obtaining and understanding underwater information. However, raw underwater images have many problems, such as low contrast, chromatic aberration, blur and low light, which seriously restrict the progress of other underwater tasks. In this paper, we propose an Underwater Mixed Spatial Attention Network (UMSAN) for underwater image enhancement. The evaluation on the EUVP dataset and some other real underwater images demonstrates that our network is sufficient against several of the advanced models.

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Two-Stage Fusion Model for Heavy Rain Removal on Single Image

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Xiaomiao Pan Yueting Yang Chuansheng Yang Chao Wang Anhui Tan School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China

Current single image derain methods cannot solve the heavy rain situation well. In this paper, based on the physical model of a rainy image, we build a two-stage network, TSF-Net, which combines model-driven and data-driven methods. The first stage gets the rain streaks, atmospheric light, and transmission map to obtain the coarse rain-free image by the physical model. The second stage is a fully convolutional neural network with the structure of U-Net. The proposed Multi-Scale Projection Fusion Block (MSPFB) module, which can perceive the spatial information across the scale of images, is employed to remove the residual rain and fuzzy parts in the first stage and obtain a refined clean image. Extensive experiments show that our TSF-Net achieves better accuracy and visual improvements against state-of-the-art methods.

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Lump-type solutions of a generalized Kadomtsev–Petviashvili equation in(3+1)-dimensions

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Chinese Physics B 2019年第10期28卷 245-252页

作者： Xue-Ping Cheng Wen-Xiu Ma Yun-Qing Yang Physics MathematicsInformation College of Zhejiang Ocean UniversityZhoushan 316004China Key Laboratory of Oceanographic Big Data Mining&Application of Zhejiang Province Zhoushan 316022China Department of Mathematics and Statistics University of South FloridaTampaFL 33620-5700USA Department of Mathematics King Abdulaziz UniversityJeddahSaudi Arabia College of Mathematics and Systems Science Shandong University of Science and TechnologyQingdao 266590China International Institute for Symmetry Analysis and Mathematical Modelling Department of Mathematical SciencesNorth-West UniversityMafikeng CampusPrivate Bag X2046Mmabatho 2735South Africa

Through the Hirota bilinear formulation and the symbolic computation software Maple, we construct lump-type solutions for a generalized(3+1)-dimensional Kadomtsev-Petviashvili(KP) equation in three cases of the coefficients in the equation. Then the sufficient and necessary conditions to guarantee the analyticity of the resulting lump-type solutions(or the positivity of the corresponding quadratic solutions to the associated bilinear equation) are discussed. To illustrate the generality of the obtained solutions, two concrete lump-type solutions are explicitly presented, and to analyze the dynamic behaviors of the solutions specifically, the three-dimensional plots and contour profiles of these two lump-type solutions with particular choices of the involved free parameters are well displayed.

关键词： lump-type solution generalized(3+1)-dimensional Kadomtsev-Petviashvili equation Hirota bilinear form symbolic computation

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Adaptive Feature Normalization Network for Single Image Dehazing

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Guangrui Hu Yueting Yang Chuansheng Yang Chao Wang Anhui Tan School of Information Engineering Zhejiang Ocean University Zhoushan 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan 316022 China

In this paper, we design an end-to-end adaptive feature normalization network (AFN-Net) for single image dehazing. In order to fit the function that can recover haze-free images from haze images more effectively, an Adaptive Normalization module (AN) is designed, which can unify the input into the same feature space. A large number of experimental evaluations show that the proposed method is superior to the state-of-the-art (SOTA) method on the benchmark datasets.

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Multi-Stage Enhanced Rain Image Restoration Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Quanqing Wang Yuanshun Cheng Yueting Yang Chao Wang Chuansheng Yang School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China Tianfu College of SWUFE Mianyang Sichuan 621000 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China

The restoration task of rain images is beneficial to the further development of computer vision tasks. However, recent rain removal methods have difficulty coping with dense rain streaks. In this paper, we propose a Multi-stage Enhanced Rain Image Restoration Network (MERNet) for the task of rain removal. Our main idea is to design a three-stage network architecture, which makes full use of the enhanced information of the previous stage and complements the information of the next stage through transmission step by step. We give consideration to information enhancement at the same time of information transmission, fully improve the information utilization rate and the overall network effect.

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Attention-based Feature Enhanced Dehazing Network

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Journal of Physics: Conference Series 2021年第1期2035卷

作者： Yuanshun Cheng Anhui Tan Chuansheng Yang Yueting Yang Chao Wang Tianfu College of SWUFE Mianyang Sichuan 621000 China School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang 316022 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan Zhejiang 316022 China School of Mathematics and Statistics Beihua University Jilin Jilin 132013 P.R. China

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should enhance the feature information of the background while weakening the feature information of haze. In this paper, we propose an end-to-end attention-based feature enhanced dehazing network (AEDNet), which integrates enhancement strategy and attention mechanism, to achieve haze removal. The network is based on U-Net, which has the advantages of retaining information, obtaining multi-scale features and so on. In the training of the network, pixel loss and perceptual loss are used to preserve feature information and improve the overall quality of results. The extensive evaluation shows that the proposed model performs significantly better than previous dehazing methods on various benchmarks.

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Incremental multivariate Markov chain model

Incremental multivariate Markov chain model

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作者： Yang, Chuan-Sheng Zheng, Yu-Jia Wang, Chao School of Mathematics Physics and Information Science Zhejiang Ocean University Zhoushan Zhejiang316000 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhejiang Ocean University Zhoushan Zhejiang316022 China

In this study, the authors present an incremental multivariate Markov (IMM) chain model. Moreover, the estimation method of the parameters in IMM is proposed. Numerical experiments illustrate the effectiveness of IMM.... 详细信息

关键词： Markov processes

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Enhancing pseudocapacitive behavior of CoFe-LDH through MWCNTs conductive network: a strategy for ultra-stable supercapacitors

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Diamond and Related Materials 2025年 157卷

作者： Qihui Niu Suna Zhang Lijun Wang Yongmin Qiao Jianguang Xu Jing Li Wenqin Li Yong Zhu Yang Li Huaqing Xie School of Energy and Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai 201209 China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai 201209 China Shanghai Shanshan Technology Co. Ltd. Shanghai 201209 China Ningbo Dema Billion Intelligent Technology Co. Ltd. Cixi Ningbo Zhejiang Province 315300 China

Layered double hydroxide (LDH) holds a prospective position in the realm of electrode materials for supercapacitors, due to its distinctive layered structure. However, its inherent low conductivity hinders its possible utilization in supercapacitors. In this study, we synthesized CoFe-LDH nanosheets and introduced multi-walled carbon nanotubes (MWCNTs) to construct MWCNTs wrapped CoFe-LDH nanocomposites. The results show that the CoFe-LDH/MWCNTs nanocomposite has a specific capacitance of 752.5 F/g under a current density of 1 A/g. The potential electrochemical capability of the CoFe-LDH/MWCNTs nanocomposite is excited via the construction of the conductive network of MWCNTs. High energy density (71 Wh/kg) and power density (9800 W/kg) are generated in an asymmetric supercapacitor with CoFe-LDH/MWCNTs nanocomposite as electrochemical active material, showing excellent cycle stability of 88.9 % capacitance remaining even after 10,000 cycles. These results indicate that MWCNTs wrapped CoFe-LDH composites are promising candidates for high performance supercapacitors.

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