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arXiv 2023年

作者： Wang, Peilan Mei, Weidong Fang, Jun Zhang, Rui The National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen518172 China The Department of Electrical and Computer Engineering National Uiversity of Singapore Singapore117583 Singapore

Intelligent reflecting surface (IRS) has been widely recognized as an efficient technique to reconfigure the electromagnetic environment in favor of wireless communication performance. In this paper, we propose a new application of IRS for device-free target sensing via joint location and orientation estimation. In particular, different from the existing works that use IRS as an additional anchor node for localization/sensing, we consider mounting IRS on the sensing target, whereby estimating the IRS’s location and orientation as that of the target by leveraging IRS’s controllable signal reflection. To this end, we first propose a tensor-based method to acquire essential angle information between the IRS and the sensing transmitter as well as a set of distributed sensing receivers. Next, based on the estimated angle information, we formulate two optimization problems to estimate the location and orientation of the IRS/target, respectively, and obtain the locally optimal solutions to them by invoking two iterative algorithms, namely, gradient descent method and manifold optimization. In particular, we show that the orientation estimation problem admits a closed-form solution in a special case that usually holds in practice. Furthermore, theoretical analysis is conducted to draw essential insights into the proposed sensing system design and performance. Simulation results verify our theoretical analysis and demonstrate that the proposed methods can achieve high estimation accuracy which is close to the theoretical bound. Copyright © 2023, The Authors. All rights reserved.

关键词： Location

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Abnormal traffic detection system in SDN based on deep learning hybrid models

arXiv

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arXiv 2023年

作者： Wang, Kun Fu, Yu Duan, Xueyuan Liu, Taotao Xu, Jianqiao Department of Information Security Naval University of Engineering Hubei Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Henan Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Henan Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

Software defined network (SDN) provides technical support for network construction in smart cities, However, the openness of SDN is also prone to more network attacks. Traditional abnormal traffic detection methods have complex algorithms and find it difficult to detect abnormalities in the network promptly, which cannot meet the demand for abnormal detection in the SDN environment. Therefore, we propose an abnormal traffic detection system based on deep learning hybrid model. The system adopts a hierarchical detection technique, which first achieves rough detection of abnormal traffic based on port information. Then it uses wavelet transform and deep learning techniques for fine detection of all traffic data flowing through suspicious switches. The experimental results show that the proposed detection method based on port information can quickly complete the approximate localization of the source of abnormal traffic. the accuracy, precision, and recall of the fine detection are significantly improved compared with the traditional method of abnormal traffic detection in SDN. © 2023, CC BY-NC-SA.

关键词： Wavelet transforms

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Adaptive Weighting Strategy based Multi-sensor data Fusion Method for Condition Monitoring of Reciprocating Pump

Adaptive Weighting Strategy based Multi-sensor Data Fusion M...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Yinghao Zhao Xu Yang Xia Wu Jian Huang Kaixiang Peng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665401166

Driven by the increasing needs for condition monitoring and maintaining stable operation using process data, a multi-sensor data fusion method based on adaptive weighting strategy using analytic hierarchy process (AHP) algorithm and cross-correlation function fusion (CCFF) algorithm is proposed in this paper. On this basis, a health index (HI) is developed based on the distance using the time-domain characteristic value of the fused vibration signals. By considering the location of sensors and data relevance, the weight coefficients could be more reasonable. The effectiveness of the proposed condition monitoring method is demonstrated by a case study from a three-plunger reciprocating pump in the industrial field, and the test results show its advantage in operation monitoring and abnormal detection

关键词： Condition monitoring Vibrations Fault detection data integration Analytic hierarchy process Sensor phenomena and characterization Safety

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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IEEE International Symposium on Biomedical Imaging

作者： Lidan Fu Binchun Lu Lingbing Li Xiaojing Shi Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese PLA General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000–1300 nm). NIR-IIb (1500–1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice.

关键词： Training Image resolution Explainable AI Statistical analysis Noise Imaging Fluorescence Generative adversarial networks Real-time systems Probes

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy

Application Research of Vertical Federated Learning Technolo...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yong Luo Zhi Lu Xiaofei Yin Songfeng Lu Yiting Weng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound.

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Competition between Tcp and Bcc Clusters During Relaxation of Undercooled Liquid Zirconium

SSRN

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SSRN 2022年

作者： Tian, Zean Mo, Yunfei Zhou, Lili Liang, Yongchao Zhang, Xiefu Zhang, Haitao Tan, Zhiguang Wang, Shengjie Luo, Jinhua Zou, Yingchang Liu, Rangsu School of Electronic Information and Electrical Engineering Hunan Province Key Laboratory of Applied Environmental Photocatalysis Changsha University Changsha410022 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Medical and Information Engineering Gannan Medical University Ganzhou341000 China School of Big Data and Information Engineering Guizhou University Guiyang550025 China

AbstractionThe details of dynamic competition between topologically closepacked (TCP) clusters and metastable body-centered cubic (BCC) clusters during the isothermal relaxation for undercooled liquid zirconium (Zr) have been investigated by molecular dynamics (MD) simulations. The structural evolution of the simulated system has been analyzed by using the system energy, the pair distribution function and the largest standard cluster analysis. It is found that the TCP clusters rather than icosahedral ones dominate in number in undercooled liquid. The TCP clusters with T5=n5/(n4+n5+n6)>1/3 stabilize the undercooled liquid and prevent crystallization;while that with T51/3 decreases and the average degree of interconnect among them weakens. © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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ML/MAP Estimation of Frequency Offset and Linear Phase Noise in Coherent Optical Communications

ML/MAP Estimation of Frequency Offset and Linear Phase Noise...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Xinwei Du Qian Wang Pooi-Yuen Kam Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Science and Engineering Chinese University of Hong Kong Shenzhen Shenzhen China

We propose a joint maximum likelihood (ML)/ maximum a posteriori probability (MAP) frequency offset (FO) and linear phase noise (LPN) estimation scheme for coherent optical communications. By designing the pilot symbols as unmodulated signal with known amplitude, the joint estimation can be simplified to the estimation of frequency and phase of a complex single sinusoid. We then utilize an ML Mth power approach to iteratively estimate the phase on those unknown transmitted symbols. The Cramér-Rao lower bounds are derived and the optimum estimation performance of the proposed algorithms are verified through simulations.

关键词： Phase noise Maximum likelihood estimation Maximum likelihood detection Symbols Frequency estimation Optical fiber communication Optical noise

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DAGCL: Diversified Recommendation with Graph-Augmented Contrastive Learning

DAGCL: Diversified Recommendation with Graph-Augmented Contr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Haozhi Xia Zhenyu Yang Ziyu Chen Xiaoyang Wang Xueli Chang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In personalized recommendation systems, Graph Neural Networks (GNNs) have gained attention for their ability to effectively model complex user-item interactions. The core concept of GNNs is to leverage ample and high-quality training data, recursively performing message passing along user-item interaction edges to progressively refine embedding representations. However, the highly imbalanced nature of user interaction data often leads traditional GNN-based recommendation systems to experience information redundancy, concentrating recommendations on a few popular items. Additionally, the long-tail problem further limits the visibility of less popular items, reducing recommendation diversity and impairing the user experience. In this paper, we propose a novel Diversity Augmented Graph Contrastive Learning method (DAGCL), which aims to increase recommendation diversity by improving the embedding generation process, while maintaining a balance between accuracy and diversity. Specifically, we design two graph augmentation methods, including a diversified subset selection module based on Deterministic Point Process (DPP) and a long-tail item interaction augmentation module, to generate diversified embeddings. Moreover, DAGCL incorporates contrastive learning, leveraging augmented views to optimize the recommendation process, alleviate data sparsity issue and capture potential user-item relationships. Experiments on two real-world datasets demonstrate that DAGCL significantly improves diversity while preserving recommendation accuracy, achieving an effective balance between these two aspects and validating its effectiveness. The code is available on https://***/XiaHaoZhi/DAGCL.

关键词： Accuracy Heavily-tailed distribution Message passing Redundancy Training data Contrastive learning User experience Graph neural networks Recommender systems Optimization

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Constructing the 3d Cloud Amount from Advanced Geostationary Imager Observations

SSRN

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SSRN 2024年

作者： Lin, Han Li, Jun Min, Min Zhang, Feng Wang, keyue Wu, Qunyong Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China National Satellite Meteorological Centre China Meteorological Administration Beijing100081 China School of Atmospheric Sciences Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University Southern Laboratory of Ocean Science and Engineering Zhuhai519082 China Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences Fudan University Shanghai200438 China

Accurate information on cloud amount vertical structure is crucial for weather monitoring and understanding climate systems. Active sensors from satellites can provide three-dimensional (3D) cloud structure but with limited geographical coverage, passive sensors from satellites have expanded observation coverage but with limited capability on profiling the clouds. Combing active and passive observations from satellites, together with atmospheric reanalysis data, this study proposes a machine learning approach (CLANN, CLoud Amount Neural Network) to construct three-dimensional (3D) cloud amounts at passive observational coverage. Independent validation is conducted for cloud amount estimates derived from the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4A and ERA5 using CLANN against CALIPSO/CALIOP lidar product, results indicate significant correlations (Pearson's r = 0.73). The cloud-amount-weighted height showed a high consistency in terms of height positioning between CLANN estimations and CALIOP data, with an RMSE of 1.89 km and a Pearson's r of 0.92. key features such as water vapor band brightness temperature and upper-layer temperature significantly enhanced model accuracy, as revealed by permutation importance analysis. Sensitivity tests highlighted the critical role of the 1.375 µm band in cirrus altitude detection, justifying the model's reliance on daytime observations. Additionally, CLANN's application to tropical cyclone (TC) dynamics was exemplified through Typhoon Lekima's rapid intensification process, emphasizing its potential in forecasting TC behavior based on cloud structure analysis. And the 3D statistical results from CLANN in 2019 reveal the seasonal variation details of cloud distribution, further demonstrating its application value in climate analysis. © 2024, The Authors. All rights reserved.

关键词： Tropical cyclone

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Structural Equivariance Self-Supervised Learning for Facial Pose Estimation

Structural Equivariance Self-Supervised Learning for Facial ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yaoxing Wang Heng Zhou Zhendong Li Xian Mo Hao Liu School of Information Engineering Ningxia University Yinchuan China Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education Yinchuan China Key Laboratory of the Internet of Water and Digital Water Governance of the Yellow River in Ningxia Yinchuan China

In this paper, we propose a self-supervised learning method for robust facial pose estimation. Conventional methods usually split the coherent head motion into discrete and finite outputs, likely leading to bias prediction because the performance of head pose estimation highly relies on structural facial appearance. To address this issue, our model achieves structural equivariance to poses through a self-supervised learning strategy from extrinsic attributes of face neighbors and underlying local associations. Specifically, we construct a complete neighbor graph to capture the extrinsic properties of face neighbors, where different latent semantic attributes are assigned to each subgraph. Accordingly, we design a set of proxy tasks based on different attribute subgraphs, where the model is encouraged to learn the underlying relation of local features under pose variation. Extensive experimental results on the challenging, widely evaluated datasets indicate the effectiveness of our model compared with the state of the arts.

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