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Chinese Automation Congress (CAC)

作者： Dongchao Lv Shaobo Li Tao Zhang Fengbin Wu Menghan Li Chaojie Zheng School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory Of Public Big Data Guizhou University Guiyang China School of Computer Science and Technology Guizhou University Guiyang China

This paper discusses an adaptive neural tracking control of permanent magnet synchronous motors subject to input saturation and output constraint. The difficulty is to consider output constraints and input saturation. Firstly, the adaptive dynamic surface control design process is systematized by embedding many existing tools into the classical backstepping framework. Then, a nonlinear transformation function is proposed to transform the output constrained system into an unconstrained system. Furthermore, using radial basis function neural networks to Process the unknown terms, the Gaussian error function is utilized to describe the continuously differentiable asymmetric saturation nonlinearity. It turns out that all signals in the proposed scheme are bounded. The simulation results are provided to further show the feasibility of the proposed method.

关键词： Control design Simulation Process control Radial basis function networks Transforms Permanent magnet motors Stability analysis

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Dts-Adapstnet: An Adaptive Spatiotemporal Neural Networks for Traffic Prediction with Mulit-Graph Fusion

SSRN

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SSRN 2023年

作者： Zhang, Jing Shi, Wenlong Ye, Xiucai Tsai, Peiwei School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

Traffic prediction is of vital importance in intelligent transportation systems. It can realize efficient route planning, avoid traffic congestion and reduce travel time, etc. However, it is difficult to make accurate road traffic prediction, due to the complex spatiotemporal dependencies of the traffic network. The construction and learning of spatial dependencies play a pivotal role in road traffic prediction, laying the foundation for accurate traffic prediction. Regrettably, most of the existing methods for capturing spatial dependencies only consider a single spatial relationship, ignoring other potential temporal and spatial correlations in the traffic network. Moreover, the end-to-end training methods are difficult to control the training direction during graph learning. Additionally, the result of traffic forecasting lacks of fusing multiple traffic data information. All of these have adverse effects on road traffic prediction. In other to capture the spatiotemporal dependencies of the traffic network accurately, a novel traffic prediction framework, Adaptive Spatio-Temporal Graph Neural Network based on Multi-graph Fusion (DTS-adapSTNet), is proposed. First of all, in order to better extract the hidden spatial dependencies, a method of fusing multiple factors is designed, which includs the distance relationship, transfer relationship and same-road segment relationship of traffic data, etc. Secondly, an adaptive learning method is proposed, which can control the learning direction of parameters better by the adaptive matrix generation module and traffic prediction module. Thirdly, an improved loss function is designed for training processes and a multi-matrix fusion module is designed to perform weighted fusion of the learned matrices, updating the spatial adjacency matrix continuously, which fuses as much traffic information as possible for more accurate traffic prediction. Finally, based on the experiments by two large real-world datasets, the DTS-adapSTNe

关键词： Travel time

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Boosting Fine-Grained Visual Anomaly Detection with Coarse-knowledge-Aware Adversarial Learning

arXiv

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arXiv 2024年

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou510330 China

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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EFFECTIVE DISTRIBUTED LEARNING WITH RANDOM FEATURES: IMPROVED BOUNDS AND ALGORITHMS 9

EFFECTIVE DISTRIBUTED LEARNING WITH RANDOM FEATURES: IMPROVE...

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9th International Conference on Learning Representations, ICLR 2021

作者： Liu, Yong Liu, Jiankun Wang, Shuqiang Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China Institute of Information Engineering Chinese Academy of Sciences China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds. Specifically, we first show that the simple combination of divide-and-conquer technique and random features can achieve the same statistical accuracy as the exact KRR in expectation requiring only O(|D|) memory and O(|D|1.5) time. Then, beyond the generalization bounds in expectation that demonstrate the average information for multiple trails, we derive generalization bounds in probability to capture the learning performance for a single trail. Finally, we propose an effective communication strategy to further improve the performance of DKRR-RF, and validate the theoretical bounds via numerical experiments. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Regression analysis

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Dual Contrastive Learning Model for Representation Learning and Background Debiasing in SAR Object Classification

Dual Contrastive Learning Model for Representation Learning ...

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SAR in Big data Era: Models, Methods and Applications (BIGSARdata)

作者： Jing Wang Sirui Tian Xiaolin Feng Bo Zhang Fan Wu Chao Wang School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China International Research Center of Big Data for Sustainable Development Goals Beijing China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

As a popular representation learning framework, contrastive learning has been widely used in SAR image interpretation for feature extraction. Recent studies have shown that some features obtained by the model are related to the background information due to the spurious correlation of the backgrounds during the SAR data collection. As a result, the generalization ability of the representation learning model will be greatly affected by the background information and cannot satisfy the requirements of practical applications. This paper constructed a multi-branch dual contrastive learning model that simultaneously compares the similarities between objects and backgrounds of various images. The motivation is to alleviate the impact of the spurious correlation between background information and the object category. The experimental results and interpretable Shapley value demonstrated that the proposed method significantly improved the generalization performance of the SAR object classification algorithm.

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Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

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Computers, Materials & Continua 2020年第4期63卷 489-507页

作者： Yong Yu Yongjun Luo Tong Li Shudong Li Xiaobo Wu Jinzhuo Liu Yu Jiang School of Software Key Laboratory in Software Engineering of Yunnan ProvinceYunnan UniversityKunming650091China School of Big Data Yunnan Agricultural UniversityKunming650201China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou510006China

Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current *** this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is *** edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between *** can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender *** verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.

关键词： Personalized recommendation one-mode projection weighted bipartite network novelty recommendation diversity

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Max-Min Beamformer for THz Wideband Communications

Max-Min Beamformer for THz Wideband Communications

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IEEE International Conference on Communications Workshops, ICC

作者： Boyu Ning Weidong Mei Lipeng Zhu Zhi Chen Rui Zhang National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) Chengdu China Department of Electrical and Computer Engineering National University of Singapore The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters. However, the conventional analog beamforming based on array response vector to steer a narrow beam may suffer from severe performance loss in wideband systems due to the “beam squint” effect over frequency. To tackle this issue, we propose a new max-min beamformer design in this paper for THz wideband communication systems, which aims to maximize the minimum beam gain over the entire frequency band. To solve this challenging problem, an augmented Lagrange method (ALM) is proposed to obtain a high-quality solution with low complexity. Numerical results show that the proposed beam design can yield considerably better performance than the conventional narrowband beamformer, especially in the case of large angle of departure (AoD) from the base station (BS) to the user.

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Weather classification method based on spiking neural network

Weather classification method based on spiking neural networ...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Meng Tian Xuefei Chen Hongkuo Zhang Peng Zhang Kejing Cao Ruiyi Wang School of Computer and Information Engineering Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9781665406314

People's life and production activities are directly or indirectly affected by the weather. It is very necessary to accurately and quickly predict weather conditions. At present, the weather prediction system needs a series of sensors and manual assistance, but it cannot be arranged in high density due to high cost, which leads to inaccurate weather prediction. Computer vision technology can classify weather conditions through images, which reduces the cost and can be arranged in high density to ensure the accuracy of weather prediction. Because the training and reasoning of traditional p Convolutional Neural Network has very large energy consumption, while Spiking Neural Network has the characteristics of ultra-low energy consumption, which can further reduce the energy cost. In this paper, a shallow Spiking Neural Network for weather classification is constructed, which is trained and tested on a dataset containing four categories (cloudy, rainy, sunny and sunrise). Experiments show that the classification accuracy of the model is 93.45%, which is higher than that of the Convolutional Neural Network based on vgg19. In addition, the computational complexity of Spiking Neural Network and Convolutional Neural Network are analyzed to show the advantages of Spiking Neural Network in energy consumption.

关键词： Training Energy consumption Costs Neural networks Weather forecasting Cognition Sensor systems

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Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

arXiv

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arXiv 2022年

作者： Leng, Xinlin Li, Chenxu Xu, Weifeng Sun, Yuyan Wang, Hongtao School of Control and Computer Engineering North China Electric Power University Baoding071051 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan250014 China Beijing Key Laboratory of IoT Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding071051 China

Federated linear regressions have been developed and applied in various domains, where multiparties collaboratively and securely perform optimization algorithms, e.g., Gradient Descent, to learn a set of optimal model weights. However, traditional Gradient-Descent based methods fail to solve problems which contain L1regularization in objective functions, such as lasso regression. In this paper, we present Federated Coordinate Descent, a new distributed scheme called FCD, to address this issue securely under multiparty scenarios. Specifically, through secure aggregation and noise perturbations, our scheme guarantees that: (1) no local information is leaked to other parties, and (2) global model weights are not exposed to cloud servers. The proposed FCD scheme fills the gap between multiparty secure learning and coordinate descent methods, and is applicable for general linear regressions, including linear, ridge, and lasso regressions. Theoretical analyses prove the security guarantees of our scheme against semi-honest attackers. Experimental results on three UCI datasets demonstrate that regressions under our FCD scheme are effective as centralized models. The performance evaluation on synthetic dataset also reveals that our scheme achieves efficient properties on computation cost and communication *** Codes 68Q32 Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

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Channel Modeling Based On Quantum Generative Adversarial Network

Channel Modeling Based On Quantum Generative Adversarial Net...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Zhairui Gong Xinling He Zhifan Wan Zetong Li Xianchao Zhang Xutao Yu School of Information Science and Engineering Southeast University Nanjing China Agricultural Bank of China Data Center Shanghai China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing China

ISBN: (纸本)9781665450867

Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a generative adversarial model with a quantum circuit as the generative module and a deep neural network as the discriminant module, thereby exploiting the privilege of quantum algorithms in simulating probability distributions to stochastic channel models. Experiments were conducted on IBM QX quantum computing platform. The gradient descent of the cost function and Kullback-Leibler divergence were analyzed. Results verify the feasibility and superiority of the quantum generative adversarial network for channel modeling.

关键词： Wireless communication Quantum algorithm Computational modeling Neural networks Stochastic processes Signal processing Generative adversarial networks

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