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A Dynamic Access Control Model Based on Attributes and Intro VAE

A Dynamic Access Control Model Based on Attributes and Intro...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xiaoyan Hu Yuelin Hu Guang Cheng Hua Wu Yifei Qin Jian Gong School of Cyber Science & Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Provincial Engineering Research Center of Security for Ubiquitous Network Nanjing China Research Base of International Cyberspace Governance (Southeast University) Nanjing China Jiangsu Enlink network technology Co. Ltd.

ISBN: (纸本)9781665435413

Affected by the COVID-19 pandemic, teleworking is becoming more popular, with the exposed attack surface of the internal network expanding. Once outsiders personate accounts or insiders conduct illegal operations, the data security in teleworking with traditional border protection will be broken. Therefore, it is necessary to implement fine-grained and dynamic access control to protect data from malicious access. Attribute-based access control (ABAC) is ideal, where authorization is performed through attributes and rules. On this basis, risk assessment, context awareness, and machine learning are supplemented for dynamic access control. However, these methods have their limitations due to the requirement of sufficient prior knowledge and massive label-classified data. Moreover, it is challenging to obtain the samples of attack behaviors, and the attack behaviors may change frequently to evade detection. In contrast, the normal behaviors are relatively stable except for the update of network services. We propose a dynamic access control model, ABAC-IntroVAE, to address the above issues. ABAC-IntroVAE judges users' requests through rule matching and behavior analysis based on the attributes of the requests. It first filters out requests against the rules by rule matching. Then, the introspective variational autoencoder (IntroVAE) is used for behavior analysis to realize dynamic access decisions. Requests classified as normal can be authorized for access. ABAC-IntroVAE only needs samples of normal requests for training, avoiding the difficult task of collecting massive and frequently changing samples of attack requests. Meanwhile, the IntroVAE model is updated through continual learning to adapt to new-style normal behaviors due to the update of network services. Our experiment study suggests that our proposed ABAC-IntroVAE can effectively perform dynamic access control. It achieves an accuracy of 97.2% in abnormal detection and maintains an accuracy of over 97% throug

关键词： Access control Training Matched filters Pandemics Teleworking Machine learning Behavioral sciences

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LQR Control Method based on Improved Antlion Algorithm

LQR Control Method based on Improved Antlion Algorithm

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第42届中国控制会议

作者： Zhe Sun Zhi Wen Lei Xu Guangfu Gong Xiangpeng Xie Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and Telecommunications

In the face of the increasing demand of crane transportation speed and stability,an improved Ant-lion algorithm is proposed in this *** the original basis,the quasi-reverse learning method is used to improve the stability of the algorithm and make the initial data distributed ***,an elite ant-lion growth mechanism is introduced to accelerate the convergence speed of the simulation and improve the simulation ***,the proposed algorithm is verified in the double pendulum system of crane based on LQR controller,and the feasibility and effectiveness of the proposed algorithm are proved by experiments.

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Optimization design of a new type of Halbach array motor with magnetic axis offset

Optimization design of a new type of Halbach array motor wit...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Muyan Jiang Yihan Liu Zhanping Huang Xiuju Du Jielu Zhang Fanglin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper proposes a novel Halbach array structure based on the traditional radial and tangential magnetized Halbach array permanent magnet motor. This structure enables the magnetic field lines to be concentrated, thereby enhancing the motor torque. Concurrently, the magnetic axis offset is designed on this basis by combining ferrite with low magnetic conductivity and NdFeB with high magnetic conductivity. This design effectively reduces the gap between the electromagnetic torque and the peak current angle of the reluctance torque, and fully utilizes both the electromagnetic and reluctance torques to further improve the total output torque of the motor. The newly proposed motor, known as the Magnetic Axis Offset Halbach Motor (MAOHM), is a result of this design. Furthermore, this paper also employs a finite element method to study and compare the proposed motor with the traditional motor, serving to verify the rationality and effectiveness of the design.

关键词： Torque Shape Rotors Random access memory Conductivity Motors Permanent magnets

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Layered Compression Scheme for Efficient Data Collection of sensory Data

Layered Compression Scheme for Efficient Data Collection of ...

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International Conference on Computer Communication and networks

作者： Siguang Chen Shujun Zhang Bin Xu Jing Hu Vijayakumar Varadarajan Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education Nanjing University of Posts and Telecommunications Nanjing China Information Center Jiangsu China Tobacco Industry Co. Ltd. Nanjing China School of Computing Science and Engineering VIT University Chennai India

Although the existing compressed sensing (CS) based spatio-temporal data compression schemes can significantly decrease communication consumption for data collection, they ignore the data correlation among different clusters over spatial dimension. Actually, the discovery and utilization of spatial correlation among different clusters can further increase the compression rate (or improve the recovery effect). In this paper, we propose a layered compression scheme for efficient data collection of sensory data (LCS-EDC). In the proposed scheme, first, we design a multi-layer network architecture to support the exploration of spatio-temporal correlations, especially for exploring the spatial correlation among different clusters. And then, we construct the specific projection methods respectively for exploring the temporal correlation in sensory nodes, spatial correlation (intra-cluster) in cluster heads and spatial correlation (inter-cluster) in processing nodes. Meanwhile, the detailed solving method is developed to recover original data and achieve the approximate data collection in sink node. Finally, simulation results indicate that the proposed layered compression scheme has better recovery performance as compared with traditional clustered compression schemes (i.e., achieving efficient data collection with high quality).

关键词： Compressed sensing data correlation multi-layer architecture data collection recovery error

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Data-consistent Unsupervised Diffusion Model for Metal Artifact Reduction

Data-consistent Unsupervised Diffusion Model for Metal Artif...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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The short-term photovoltaic power generation forecasting based on Fruit Fly Optimization Algorithm optimized BP neural network

The short-term photovoltaic power generation forecasting bas...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Shaoru Zhang Haoxuan Zhu Likun Yang Zhanping Huang Xiuju Du Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Accurately predicting photovoltaic power generation is crucial for ensuring the safe operation of power grids and advancing solar energy development and utilization. For the issue of large errors in current prediction methods, this paper introduces a forecasting method for photovoltaic power generation. The method utilizes Fruit Fly Optimization Algorithm (FOA) to optimize Back Propagation (BP) neural network. This involves analyzing the correlations among factors impacting photovoltaic power generation and selecting relevant meteorological data via the Pearson correlation coefficient method. Fruit Fly Algorithm demonstrates rapid convergence, a minimal parameter set and ease of adjustment, rendering it applicable across various domains. Employing Fruit Fly Algorithm to optimize weights and thresholds within BP neural network leads to the final prediction outcomes. Simulation results confirm the superior prediction accuracy of the FOA-BP model for photovoltaic power generation, particularly during spring, autumn and winter, showcasing its practical utility.

关键词： Photovoltaic systems Correlation coefficient Atmospheric modeling Electricity Neural networks Predictive models Prediction algorithms

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Crop-picking Robot Grasping System Based on Deep Learning

Crop-picking Robot Grasping System Based on Deep Learning

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Qike Wu Yihan Liu Rui Ma Shaoru Zhang Sai Gao Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

The crop-picking system plays a key role in automatic picking. In this study, a set of crop-picking robot grasping system based on convolutional neural network (CNN) was designed by using deep learning technology. Firstly, a variety of preprocessing methods are used to expand and process the image data to ensure the diversity and integrity of the sample data. Then, CNN and gradient descent (GD) algorithm were combined to optimize the training model to identify the types of crops accurately. Then, the lidar used ToF (Time of Flight) technology and AGglomerative NESting(AGNES) algorithm to obtain the key positioning points. After excluding invalid cluster centers, the identification results were accurately located. The experimental results show that compared with the 3D pose estimation method of the manipulator based on multi-view, the calculation error of the 3D coordinates of crops is smaller, and the reasoning speed for a single image is only 0.000316 seconds. Finally, the comprehensive accuracy of the system reaches 97.58%, which provides practical technical support for automatic crop picking.

关键词： Training Three-dimensional displays Accuracy Laser radar Robot kinematics Pose estimation Crops

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Intra/Inter-frame Joint WPP Coding Algorithm on Multicore Platform

Intra/Inter-frame Joint WPP Coding Algorithm on Multicore Pl...

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2018 3rd International Conference on Electrical, Automation and Mechanical engineering(EAME 2018)

作者： Chuanwei Yin Dong Hu Tao Gu Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications

Although wavefront parallel processing(WPP) proposed in the HEVC standard and various inter frame WPP algorithms can achieved comparatively high parallelism, their scalability for its parallelism is still very limited due to various dependencies introduced in spatial and temporal prediction in HEVC. In this paper, through pixel correlation analysis, establishment of CTU node model, multi-core resource allocation strategy, we proposed an intra/inter-frame joint WPP coding algorithm for multi-core platform which can significantly improve the parallelism, while achieved good results in bit rate, PSNR, and acceleration ratio. Experiments on standard HD test sequence show that the proposed algorithm can lead to up to 2x, 1.3x speed up compared with the original WPP and IWF parallelism.

关键词： video coding HEVC multicore platform high parallelism WPP

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Deep Learning-based Visual Recognition System for Fruit-Picking Robots

Deep Learning-based Visual Recognition System for Fruit-Pick...

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IEEE Conference on Cybernetics and Intelligent Systems

作者： Rui Ma Yihan Liu Qike Wu Shaoru Zhang Nan Wang Jielu Zhang Fang Lin Luo College of Engineering Hebei Normal University Shijiazhuang China Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology Jiangsu Tailong Reducer Co. LTD Taizhou China Jiangsu Tailong Reducer Co. LTD Taizhou China School of Electrical and Electronic Engineering Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Fruit recognition plays an important role in automated picking, in order to improve the accuracy and real-time picking, this study uses deep learning methods to design a fruit-picking robot visual recognition system. First, multiple preprocessing methods are used to expand the sample data, and the images are proportionally cropped and scaled to make the image dataset more complete; subsequently, to improve the accuracy of image recognition, a multilayer Convolutional Neural networks (CNN) is established; and the Adam algorithm is utilized to train the model parameters several times to determine the optimal hyperparameters, thus overcoming the defects of the multilayer neural network that has a local optimal search. The experimental results show that compared with the random forest method, this system has the characteristics of high-speed recognition and high accuracy in fruit picking, and can quickly and accurately recognize fruit images, with the recognition speed of a single image taking only 0.081 seconds, and the recognition accuracy reaching more than 97.35%. The method has important theoretical and application value and provides an effective means for automatic fruit recognition.

关键词： Visualization Image recognition Accuracy Random access memory Nonhomogeneous media Real-time systems Character recognition

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A Novel Multi-level Wavelet Image Coding Algorithm by Full Sub-band Compressed Sensing

A Novel Multi-level Wavelet Image Coding Algorithm by Full S...

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2018 3rd International Conference on Electrical, Automation and Mechanical engineering(EAME 2018)

作者： Weiping Qiu Dong Hu Yongyang He Education Ministry's Key Lab of Broadband Wireless Communication and Sensor Network Technology Education Ministry's Engineering Research Center of Ubiquitous Network and Heath Service Jiangsu Province's Key Lab of Image Procession and Image Communications Nanjing University of Posts and Telecommunications

The performance of wavelet transform based image compressed sensing coding algorithms severely rely on the level of wavelet transform. To this end, this paper investigated the combination of mutil-level wavelet full sub-band in compressed sensing framework. Firstly, we constructed a full sub-band coefficient sparse vector of mutil-level discrete wavelet transform. Secondly, we designed a weight matrix to improve measurement matrix. Finally, sparsity vector was processed by compressed sensing to get the measured value. Compared with the exiting algorithms, the experimental results of the proposed algorithm show that the PSNRs of reconstructed images is improve up to 1～2 dB under the same objective quality.

关键词： image coding wavelet transform compressed sensing full sub-band coefficient sparsity vector

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