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Capacity Maximization for Double Passive/Active RIS-Aided MISO Systems in Multi-User Scenarios

IEEE Internet of Things Journal 2025年

作者： Peng, Xin Liu, Chenyu Yu, Chao Xie, Wenwu Yang, Liang Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan Engineering Research Center for Autonomous Data Perception and Processing in Industrial Intelligent Networking Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

This paper addresses the problem of capacity maximization in double reconfigurable intelligent surface (RIS) -aided multiple-input single-output (MISO) systems under multi-user scenarios. A system model is proposed for double RIS-aided MISO configurations, incorporating both passive RIS (PRIS) and active RIS (ARIS). The primary objective is to maximize the minimum channel capacity across all users, and a corresponding optimization problem is formulated to achieve this goal. To overcome the challenges posed by the fractional structure of the non-convex objective function, auxiliary variables are introduced, and the bisection method is utilized. Given the highly coupled and nonconvex nature of the optimization variables, a joint alternating optimization (AO) algorithm is designed based on the bisection method and successive convex approximation (SCA), enabling iterative determination of the beamforming vectors at the base station and the reflection coefficients of RISs. The results indicate that deploying double RISs substantially enhances the system’s channel capacity through reflected links. Notably, the ARIS protocols yield higher performance gains, particularly under scenarios involving high total power and large amplification factors. In multi-user environments, the double RIS configuration significantly outperforms the single RIS setup due to the increased degrees of freedom, especially when supported by ARIS protocols. These findings underscore the substantial potential and advantages of double RIS configurations in complex communication environments. © 2014 IEEE.

关键词： Convex optimization

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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A portable,low-cost lactate measurement system

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Journal of Measurement science and Instrumentation 2025年第1期16卷 37-46页

作者： WANG Qingqing PAN Yu YU Chuanxin WANG Yanyan ZHANG Kaikai LIU Sheng School of Computer Science and Technology Huaibei Normal UniversityHuaibei 235000China Anhui Provincial Engineering Research Center of Cognitive Behavioral Intelligent Computing and Application Huaibei 235000China

Lactate,as a metabolite,plays a significant role in a number of fields,including medical diagnostics,exercise physiology and food *** methods for lactate measurement often involve expensive and cumbersome *** study developed a portable and low-cost lactate measurement system,including independently detectable hardware circuits and user-friendly embedded software,computer,and smartphone *** experiment verified that the relative error of the detection current in the device circuit was less than 1%.The electrochemical performance was measured by comparing the[Fe(CN)_(6)]^(3−)/[Fe(CN)_(6)]^(4−)solution with the desktop electrochemical workstation CHI660E,and a nearly consistent chronoamperometry(CA)curve was *** modified lactate sensors were used for CA testing of *** the concentration range of 0.1 mmol·L^(−1)to 20 mmol·L^(−1),there was a good linear relationship between lactate concentration and steady-state current,with a correlation coefficient(R2)greater than 0.99 and good repeatability,demonstrating the reliability of the developed *** lactate measurement system developed in this study not only provides excellent detection performance and reliability,but also achieves portability and low cost,providing a new solution for lactate measurement.

关键词： lactate measurement portable device embedded development lactate sensor electrochemical analysis

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PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

PA&DA: Jointly Sampling PAth and DAta for Consistent NAS

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Shun Lu Yu Hu Longxing Yang Zihao Sun Jilin Mei Jianchao Tan Chengru Song Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Kuaishou Technology

Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we further find that large gradient variance occurs during supernet training, which degrades the supernet ranking consistency. To mitigate this issue, we propose to explicitly minimize the gradient variance of the supernet training by jointly optimizing the sampling distributions of PAth and DAta (PA&DA). We theoretically derive the relationship between the gradient variance and the sampling distributions, and reveal that the optimal sampling probability is proportional to the normalized gradient norm of path and training data. Hence, we use the normalized gradient norm as the importance indicator for path and training data, and adopt an importance sampling strategy for the supernet training. Our method only requires negligible computation cost for optimizing the sampling distributions of path and data, but achieves lower gradient variance during supernet training and better generalization performance for the supernet, resulting in a more consistent NAS. We conduct comprehensive comparisons with other improved approaches in various search spaces. Results show that our method surpasses others with more reliable ranking performance and higher accuracy of searched architectures, showing the effectiveness of our method. Code is available at https://***/ShunLu91/PA-DA.

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Statistical static timing analysis for circuit aging prediction

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

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9th International Conference on Energy Engineering and Environmental Engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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research on Optimization of Nuclear Power Security DCS Gateway Encryption Communication Based on OPC UA Protocol 9th

Research on Optimization of Nuclear Power Security DCS Gatew...

引用

9th International Conference on Energy Engineering and Environmental Engineering, ICEEEE 2022

作者： Zhou, Pengjun Chen, Zhi Jiang, Wei Liu, Jie Zhang, Yan Zhong, Ke Wu, Zhiqiang School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (纸本)9783031302329

Nuclear power safety grade DCS gateway is an important hub for information transmission between safety grade equipment and non-safety grade equipment, and its security is very important in the transmission process. At present, the communication protocol used by the Chinese nuclear power security grade DCS gateway does not use data encryption in the data transmission process. However, the signature certificate, user authentication, access control and data encryption transmission of OPC UA protocol have more advantages in ensuring the data security of nuclear power security grade DCS gateway, which helps to meet the technical requirements for nuclear power security grade DCS information security. Based on the cryptography theory, this paper analyzes the security and efficiency of the original RSA encryption algorithm of the OPC UA protocol, and concludes that the encryption efficiency of the RSA algorithm is poor, which leads to the application of the OPC UA protocol to the nuclear power security grade DCS gateway communication process can not meet the time requirements of data transmission, so it is necessary to optimize and improve the communication process of the OPC UA protocol applied to the nuclear power security grade DCS gateway. This paper optimizes and improves the encryption transmission in the communication process of the nuclear power security grade DCS gateway of OPC UA protocol, and proposes a hybrid encryption algorithm of SM2 and AES to replace the original encryption method in OPC UA protocol. Through experiments, it proves that the communication optimization method improves the communication efficiency of the original protocol, and achieves the efficiency index of nuclear power security grade DCS gateway for data transmission. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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PID Controller-Based Adaptive Federated Learning

PID Controller-Based Adaptive Federated Learning

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IEEE International Conference on Communications in China (ICCC)

作者： Mingjun Dai Genghui Xu Jialong Yuan Sanaz Kianoush Stefano Savazzi Guangdong Provincial Engineering Center for Ubiquitous Computing and Intelligent Networking College of Electronic and Information Engineering Shenzhen University China School of Computer Science and Technology Kashi University China National Research Council (CNR) Institute of Electronics Information Engineering and Telecommunications (IEIIT) Italy

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

For federated learning (FL) optimization, FedAdam algorithm suffers from overshooting issues. To address this problem effectively, this work proposes a solution based on the design principles of PID controllers in the field of feedback control, aiming to explore the connection between the working process of PID controllers and the optimization process of FL models through mathematical methods. More specifically, this paper proposes a federated adaptive PID optimization algorithm, named FedAdamPID, that utilizes historical, current, and future information of client gradients. Extensive experiments on standard datasets validate that the proposed FedAdamPID algorithm significantly outperforms the FedAdam algorithm in terms of convergence speed and accuracy.

关键词： Deep learning Accuracy Federated learning Prevention and mitigation Process control Mathematical models Feedback control

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M2F2-Net: Multi-Modal Feature Fusion for Unstructured Off-Road Freespace Detection

M2F2-Net: Multi-Modal Feature Fusion for Unstructured Off-Ro...

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IEEE Symposium on intelligent Vehicle

作者： Hongliang Ye Jilin Mei Yu Hu The Research Center for Advanced Computing Systems Zhejiang Lab Hangzhou China The Research Center for Intelligent Computing Systems Institute of Computing Technology Beijing China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Freespace detection is an important part of autonomous driving technology. Compared with structured on-road scenes, unstructured off-road scenes face more challenges. Multi-modal fusion method is a viable solution to these challenges. But existing fusion methods do not fully utilize the multi-modal features. In this paper, we propose an effective multi-modal network named M2F2-Net for freespace detection in unstructured off-road scenes. We propose a multi-modal feature fusion strategy named Multi-modal Cross Fusion (MCF). MCF module is simple but effective in fusing the features of RGB images and surface normal maps. Meanwhile, a multi-modal segmentation decoder module is designed to decouple the segmentation of two modalities, and it further helps the features of both modalities to be fully utilized. In order to solve the problem that the road edge is difficult to extract in the unstructured scenes, we also propose an edge segmentation decoder module. Extensive experiments show that our approach can lead to significant improvements, which brings 6.1% F1 and 10.8% IoU improvements. Our code will be available at https://***/yhl1010/M2F2-Net.

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Performance prediction for distributed graph computing

Performance prediction for distributed graph computing

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作者： Ji, Shuo Zhao, Yinliang Li, Yuxiang School of Information Science and Technology Northwest University Xi'an China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Information Engineering Henan University of Science and Technology Luoyang China

Performance prediction for executing graph applications on distributed systems is a prerequisite to improve system performance. Especially for distributed systems optimized by sacrificing the accuracy of results to improve runtime performance, performance prediction can be used to determine accuracy-related system parameters to achieve a tradeoff between the runtime and inaccuracy of results. This article presents an approach that predicts the runtime and inaccuracy of executing graph algorithms on bulk synchronous parallel (BSP)-based systems to optimize system parameters by using an artificial neural network (ANN). The proposed approach samples different scales of subgraphs from the input graph by maintaining that the features of subgraphs are similar to the features of the input graph. Then it executes graph algorithm on each subgraph and extracts their runtime features. An ANN-based performance prediction model is trained off-line based on the extracted features and is used to predict the performance of executing graph algorithm on the complete input graph. We have validated the proposed approach by conducting single-source shortest path, connected component, and PageRank on the BSP-based distributed systems. The experimental results demonstrate that the prediction method can effectively predict the runtime and the inaccuracy with a relative error rate under 14% and under 25%, respectively, compared with the actual performance results. © 2020 John Wiley & Sons Ltd.

关键词： Forecasting

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