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8th Asia-Pacific Conference on intelligent robot Systems, ACIRS 2023

作者： Hu, Chong Mu, Chunyang Xing, Ma Zhang, Chuntao Zhou, Wenya Yang, Ke School of Electrical and Information Engineering North Minzu University Yinchuan China College of Mechanical and Electrical Engineering North Minzu University The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan China School of Electrical and Information Engineering North Minzu University The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan China College of Mechanical and Electrical Engineering North Minzu University Yinchuan China

ISBN: (纸本)9798350302851

In the application of RRT (Rapidly-exploring Random Trees) algorithm in obstacle avoidance path planning of redundant robot arms in high-dimensional space, the sampling area of random sampling points is large, the search time is long, the path twists and turns, and the redundant points are too many. In this paper, an efficient sampling RRT algorithm with adaptive step size and fast automatic convergence is proposed. Firstly, by adding the optimized gravity function, the step size of the extended tree is changed adaptively and the convergence is stronger. Secondly, the limited sampling of parent node area expansion is proposed to avoid useless point sampling and repeated point sampling, so that the utilization of sampling points is greatly improved. Finally, by changing the way of parent node reconnection and re-selection, the path tortuous degree is lower, the path planning cost and redundancy points are reduced, and MATLAB software is used to carry out the path planning simulation experiment. By comparing RRT, gravitational field RTT and RRT∗ algorithm, the improved RRT algorithm has significantly improved in distance, time and number of nodes. It has important engineering application value. © 2023 IEEE.

关键词： Motion planning

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Sensor Placement Optimization and Rapid Deployment of a Class of Visual Sensor Networks 42

Sensor Placement Optimization and Rapid Deployment of a Clas...

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42nd Chinese Control Conference, CCC 2023

作者： Deng, Heng Zhou, Jiahui Zhao, Zhiyao Zhang, Liguo Beijing University of Technology Key Laboratory of Computational Intelligence and Intelligent Systems Faculty of Information Technology Beijing100124 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9789887581543

To achieve the purpose of large-scale visual tracking of targets based on Visual Sensor Networks (VSNs), this paper proposes a sensor placement optimization method to provide a suitable sensor configuration that satisfies the requirements of maximal network coverage and minimal reconstruction error as closely as possible. In the proposed method, some necessary models are established as the mathematical basis of the optimization process. Then, a multi-objective optimization function is designed to balance the coverage and reconstruction error, followed by an intelligent optimization algorithm that combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Additionally, a dimensionality reduction technique is used to represent the sensor location information using a one-dimensional forward distance, which makes the search process simpler and increases execution speed. Finally, comprehensive comparison experiments are carried out in various settings to show the reliability and effectiveness of the optimization algorithms. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multiobjective optimization

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Research on WNN Greenhouse Temperature Prediction Method Based on GA

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Phyton-International Journal of Experimental Botany 2022年第10期91卷 2283-2296页

作者： Wenbin Dai Lina Wang Binrui Wang Xiaohong Cui Xue Li College of Mechanical and Electronic Engineering China Jiliang UniversityHangzhou310018China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou310018China

Temperature in agricultural production has a direct impact on the growth of *** emergence of greenhouses has improved the impact of the original unpredictable changes in temperature,but the temperature modeling of greenhouses is still the main direction at *** network modeling relies on sufficient actual data to model greenhouses,but there is a widening gap in the application of different neural *** paper proposes a greenhouse temperature prediction model based on wavelet neural network with genetic algorithm(GA-WNN).With the simple network structure and the nonlinear adaptability of the wavelet basis function,wavelet neural network(WNN)improved model training speed and accuracy of prediction results compared with back propagation neural networks(BPNN),which was conducive to the prediction and control of short-term greenhouse temperature *** the same time,the genetic algorithm(GA)was introduced to globally optimize the initial weights of the original model,which improved the insensitivity of the model to the initial weights and thresholds,and improved the training speed and stability of the ***,simulation results for the greenhouse showed that the model training speed,prediction results accuracy and model stability of the GA-WNN in the greenhouse were improved in comparison to results obtained by the WNN and BPNN in the greenhouse.

关键词： Greenhouse temperature greenhouse modeling wavelet neural network genetic algorithm

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HVAC Optimal Control Based on the Sensitivity Analysis:An Improved SA Combination Method Based on a Neural Network

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Computer Modeling in Engineering & Sciences 2023年第9期136卷 2741-2758页

作者： Lifan Zhao Zetian Huang Qiming Fu Nengwei Fang Bin Xing Jianping Chen School of Electronic and Information Engineering Suzhou University of Science and TechnologySuzhou215009China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou215009China Suzhou Key Laboratory of Mobile Network Technology and Application Suzhou University of Science and TechnologySuzhou215009China Chongqing Industrial Big Data Innovation Center Co. Ltd.Chongqing400707China

Aiming at optimizing the energy consumption of HVAC,an energy conservation optimization method was proposed for HVAC systems based on the sensitivity analysis(SA),named the sensitivity analysis combination method(SAC).Based on the SA,neural network and the related settings about energy conservation of HVAC systems,such as cooling water temperature,chilled water temperature and supply air temperature,were ***,based on the data of the existing HVAC system,various optimal control methods ofHVAC systems were tested and evaluated by a simulated HVAC system in *** results show that the proposed SA combination method can reduce significant computational load while maintaining an equivalent energy performance compared with traditional optimal control methods.

关键词： Energy conservation sensitivity analysis HVAC system neural network

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Model-Based Offline Policy Optimization with Adversarial Network 26

Model-Based Offline Policy Optimization with Adversarial Net...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781643684369

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model's uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. © 2023 The Authors.

关键词： Reinforcement learning

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ConvTrans-TPS: A Convolutional Transformer Model for Disk Failure Prediction in Large-Scale Network Storage Systems 26

ConvTrans-TPS: A Convolutional Transformer Model for Disk Fa...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Shicheng Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China

ISBN: (纸本)9798350331684

Disk failure is one of the most important reliability problems in large-scale network storage systems. Disk failure may lead to serious data loss and even disastrous consequences if the missing data cannot be recovered. Therefore, predicting disk failures is an important means of ensuring storage security in network storage systems. However, since the fault data in the fast degradation stage is smaller than the healthy data in the normal state, the mixture of healthy data and faulty data leads to extremely unbalanced data, which brings great challenges to finding hidden fault information, thus making fault prediction more accurate. Aiming at the above problems, a convolutional transformer model ConvTrans-TPS model for disk failure prediction in large-scale network storage systems is proposed. The ConvTrans-TPS model acquires dependencies between long-term sequence data through transformers and uses convolutional projections for attention computation to enhance attention to local contextual information. data augmentation to predict failures in the next 7 days. Validated by the analysis on the Backblaze dataset, the F1 is 0.96 and the Matthews correlation coefficient (MCC) is 0.92. Compared with the popular CNN-LSTM model in recent years, our proposed method improves F1 and MCC by 4% and 5%, respectively, improving the prediction accuracy. © 2023 IEEE.

关键词： Convolution

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Research on Highly Secure Metaverse Based on Extended Reality Under Edge Computing 16th

Research on Highly Secure Metaverse Based on Extended Realit...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Fu, Jinrong Liu, Yiwen Yan, Haobo Yang, Yahui Peng, Ling Shi, Yuanquan Feng, Tao School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000 China School of Foreign Languages Huaihua University Huaihua418000 China

ISBN: (纸本)9783031580529

With the explosion of ChatGPT, the development of artificial intelligence technology ushered in another explosion. Similarly, with the rapid development of extended reality technology and Internet of Things technology, Metaverse will also usher in greater breakthroughs. However, in the development of the extended reality metaverse under edge computing, many security issues will arise. This paper focuses on data security, considering that data will be transmitted and processed between multiple devices and nodes instead of being concentrated in the cloud, which may bring To solve data security issues, relying on the integrated architecture of Metaverse educational applications based on edge computing (MEC), it provides an identity verification and access with higher security and scalability, better performance, and service requirements that meet the current environment Control mechanism solutions, while analyzing other problems that will arise during the development of the extended reality metaverse. Aiming at the security problem on the edge side, a signature authentication scheme is designed based on Elliptic Curve Cryptography (ECC) integrated blockchain encryption technology and the effectiveness of the method is proved. In order to promote the extended reality metaverse under edge computing, it provides a mirror for the application in the field of education under the condition of ensuring data security. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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A Fast Deep Unfolding Learning Framework for Robust MU-MIMO Downlink Precoding

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第2期9卷 359-372页

作者： Xu, Jing Kang, Chaohui Xue, Jiang Zhang, Yizhai Xi An Jiao Tong Univ Sch Informat & Commun Engn Shaanxi Key Lab Deep Space Explorat Intelligent In Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Int Acad Math & Math Technol Natl Engn Lab Big Data Analyt Xian 710049 Peoples R China Northwestern Polytech Univ Res Ctr Intelligent Robot Xian 710072 Peoples R China

This paper reformulates a worst-case sum-rate maximization problem for optimizing robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding under realistic per-antenna power constraints. We map the fixed number of iterations in the developed mean-square-error uplink-downlink duality iterative algorithm into a layer-wise trainable network to solve it. In contrast to black-box approximation neural networks, this proposed unfolding network has higher explanatory power due to fusing domain knowledge from existing iterative optimization approaches into deep learning architecture. Moreover, it could provide faster robust beamforming decisions by using several trainable key parameters. We optimize the determination of the channel error's spectral norm constraint to improve the sum rate performance. The experimental results verify that the proposed deep unfolding "RMSED-Net" could combat channel errors in comparison with the non-robust baseline. It is also confirmed by the simulations that the proposed RMSED-Net in a fixed network depth could substantially reduce the computing time of the conventional iterative optimization method at the cost of a mild sum rate performance loss.

关键词： Deep unfolding per-antenna power constraint robust multi-user multiple-input multiple-output (MU-MIMO) downlink precoding worst-case robust precoder design

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MC-PDARTS: Multi-Cell Progressive Differentiable Architecture Search

MC-PDARTS: Multi-Cell Progressive Differentiable Architectur...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qun Liu Yi Cai Junying Chen School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot (SCUT) Ministry of Education Guangzhou China

Recently, network architecture search has been practiced in more and more tasks, but its high cost makes it difficult for small research teams or individual researchers to follow. Differentiable architecture search (DARTS) algorithm provides an easier available solution in searching effective network architectures. Although DARTS algorithm has greatly reduced the search cost as compared with other evolution based or reinforcement learning based methods, there is still a long way to go before it is widely used, since gradient based methods consume a lot of GPU memory and its search space lacks diversity. Encouraged by the idea of progressive differentiable architecture search (P-DARTS), we propose a multi-cell progressive differentiable architecture search (MC-PDARTS) algorithm, which allows cells at different levels to learn different structures, and progressively searches the best structure for the corresponding level in each search stage. The proposed algorithm greatly enlarges the search space of the search network while reducing the memory consumption and search time in the search process, and alleviates the problem of performance collapse caused by too many skip connections which often occur in previous gradient based methods. Compared with the previous algorithms, the proposed MC-PDARTS algorithm achieves state-of-the-art performance on CIFAR-10 and CIFAR-100 datasets with around 4-hour search on a single Nvidia GTX1080Ti GPU. In order to reduce the performance loss in the transfer process, MC-PDARTS searches directly on a large target dataset (ImageNet) without proxy tasks, achieving state-of-the-art performance.

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Efficient QoS data Prediction Based on Tensor Kernel Paradigm-Tensor Decomposition

Efficient QoS Data Prediction Based on Tensor Kernel Paradig...

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International Conference on Networking and Network Applications (NaNA)

作者： Hong Xia Jing Xu Qingyi Dong Hui Jia Yanping Chen Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

In the Mobile Edge Computing (MEC) environment, the prediction efficiency is low when user recommendation is based on Quality of Service (QoS) data due to network environment and other factors. Traditional methods use Collaborative Filtering (CF) or tensor decomposition methods to mine the relationship between historical QoS data for prediction, but there are problems with prediction accuracy and efficiency. Given this, this paper proposes an efficient QoS data prediction method (CTT) combining tensor kernel paradigm-tensor decomposition. The method first introduces the tensor kernel norm to approximate the tensor rank function to better capture the spatio-temporal correlation of real QoS data; Second, tensor decomposition is combined to address inefficiencies. Experimental results on the public QoS dataset WS-Dream show that the method significantly improves the runtime while guaranteeing prediction accuracy and applies to scenarios with different data missing densities.

关键词： Tensors Accuracy Runtime Multi-access edge computing Computational modeling Quality of service Predictive models

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