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International Conference on Wireless communications and Signal Processing (WCSP)

作者： Han Zeng Ze-Tong Li Tian Luan Yulong Fu Xu-Tao Yu Zai-Chen Zhang National Mobile Communications Research Laboratory Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Nanjing China Quantum Information Center of Southeast University Nanjing China State Key Lab of Millimeter Waves Southeast University Nanjing China Yangtze Delta Region Industrial Innovation Center of Quantum and Information Technology Suzhou China

ISBN: (纸本)9781665450867

Using variational quantum eigensolver (VQE) algorithm to realize ground state energy calculation on noisy intermediate-scale quantum (NISQ) devices is widely applied in quantum physics, quantum chemistry, and other fields. It can simulate chemical molecules to solve the energy solution required by chemical reactions. This paper proposes an optimized cost function for the original meta-VQE and combines it with the quantum architecture search algorithm to break the limitation of the fixed structure. To expand the penalty degree of error and obtain more accurate results, two indicators are added to the original cost function including the slope error of discrete predicted value and accurate value as well as the error of the first value. The results show that the optimized algorithm can learn more characteristics of the ground state energy changing trend in the noise channel, and obtain a more accurate estimate of equilibrium bond length.

关键词： Wireless communication Training Stationary state Signal processing algorithms Channel estimation Computer architecture Signal processing

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Enhanced competitive swarm optimizer for multi-task optimization 9

Enhanced competitive swarm optimizer for multi-task optimiza...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Li, Wei Yuan, Junqing Luo, Haonan Lei, Zhou Xu, Qingzheng School of Computer Science and Engineering Xi'An University of Technology Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an710048 China College of Information and Communication National University of Defense Technology Xi'an710106 China

ISBN: (纸本)9781450387835

In the real world, many problems possess the high degree of similarity. Inspired by multifactorial inheritance model, evolutionary multi-task optimization paradigm is proposed to simultaneously solve multiple optimization problems. However, experimental results have revealed that the performance of multifactorial evolutionary algorithm deteriorates with negative knowledge transfer between uncorrelated tasks. To alleviate this issue, we proposed an enhanced competitive swarm optimizer to explore the generality of the multitasking paradigm. A new velocity update mechanism for losers is proposed to improve the search ability. Further, a mating approach is proposed to transfer implicit knowledge among tasks for generating offspring. Experimental and statistical analyses are performed on CEC2017 evolutionary multitask optimization competition. Results show that the proposed algorithm is competitive in comparison with other state-of-the-art multifactorial optimization algorithms. © 2020 ACM.

关键词： Evolutionary algorithms

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Robust Beamforming for Localization-Aided Millimeter Wave communication Systems

arXiv

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arXiv 2022年

作者： Sun, Junchang Ma, Shuai Li, Shiyin Yang, Ruixin Min, Minghui Seco-Granados, Gonzalo The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Department of Telecommunications and Systems Engineering Universitat Autònoma de Barcelona Barcelona08193 Spain The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Shanxi Key Laboratory of Information Communication Network and Security Xian University of Posts and Telecommunications Xian710121 China

In this letter, we investigate a robust beamforming problem for localization-aided millimeter wave (mmWave) communication systems. To handle this problem, we propose a novel restriction and relaxation (R&R) method. The proposed R&R method aims at minimizing the total transmit power while the positioning error follows a Gaussian distribution. Specifically, in the restriction phase of R&R, the probabilistic constraint is transformed into the deterministic form by using the Bernstein-type inequality. In the relaxation phase of R&R, the non-convex optimization problem is reformulated into a convex semidefinite program (SDP) by using semidefinite relaxation (SDR) and first-order Taylor expansion methods. To the best of our knowledge, we first consider the impact of the distribution of the positioning error on the channel state information (CSI), which further influences the data rate. Numerical results present the trade-off of the beamforming between the communication and positioning. © 2022, CC BY.

关键词： Beamforming

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An SDN controller security Cluster Scheme Based on Intrusion Detection technology

An SDN Controller Security Cluster Scheme Based on Intrusion...

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作者： Xiao-feng XU Jia-hao DAI Gao-lei YANG Science and Technology on Communication Information Security Control Laboratory China Electronic Group Corporation NO.36 Institute Xidian University

Software Defined Network(SDN) is a new network innovation architecture that separates the control layer and the data layer of the *** the SDNs,the control layer is the core of the entire network,and its security is very *** practical applications,the control layer is usually composed of SDN controller *** order to enhance the security of the SDN controller cluster,this paper first studies the development status of the current SDN technology,and then analyzes the current state of the defense technology of the network *** the analysis and summary of the two,we proposed the discovery and isolation scheme of the invaded controller in the cluster environment,and designed the intrusion detection module,control module and defense ***,we implemented the scheme based on Opendaylight controller,and proved the effectiveness of the scheme by experiments.

关键词： Network security Intrusion prevention Software defined network Cluster

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Metasurfaces with high-Q resonances governed by topological edge state

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Optics letters 2022年第7期47卷 1822-1825页

作者： Zhidong Gu Jiaxin Chen Bofeng Gao Wei Wu Zhenyu Zhao Wei Cai Xinzheng Zhang Mengxin Ren Jingjun Xu The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics and TEDA Applied Physics Institute Nankai University Tianjin 300071 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308 China Department of Physics Shanghai Normal University Shanghai 200234 China e-mail: weicai@*** e-mail: jjxu@***

Achieving high-quality (Q)-factor resonances in metasurfaces is essential for various applications, including nano-lasers, nonlinear optics, and quantum optics. In this work, we propose a high-Q metasurface using a topological strategy: constructing the metasurface by stacking two conjugated nanopillar arrays with different topological invariants. Our study shows that a topological edge state steadily appears at the interfaces of the nanopillars, and a sharp transmission resonance with a Q-factor of more than 1000 can be obtained. The sensing application of such high-Q topological metasurface is also demonstrated, whose figure of merit reaches approximately 145. The proposed strategy and underlying theory can open up new avenues to realize ultrasharp resonances, which can promote numerous potential applications, such as biosensing, optical modulation, and slow-light devices.

关键词： Electromagnetically induced transparency Nonlinear optics Optical systems Quantum optics Refractive index Resonance

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization laboratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework

arXiv

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arXiv 2023年

作者： Zhao, Yang Yang, Jiaxi Wang, Wenbo Yang, Helin Niyato, Dusit 2 Fusionopolis Way Innovis #08-04 Singapore138634 Singapore Shenzhen Institute for Advanced Study School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650050 China Department of Information and Communication Engineering Xiamen University Xiamen361005 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Industrial systems require reliable predictive maintenance strategies to enhance operational efficiency and reduce downtime. Existing studies rely on heuristic models which may struggle to capture complex temporal dependencies. This paper introduces an integrated framework that leverages the capabilities of the Transformer and Deep Reinforcement Learning (DRL) algorithms to optimize system maintenance actions. Our approach employs the Transformer model to effectively capture complex temporal patterns in IoT sensor data, thus accurately predicting the Remaining Useful Life (RUL) of equipment. Additionally, the DRL component of our framework provides cost-effective and timely maintenance recommendations. Numerous experiments conducted on the NASA C-MPASS dataset demonstrate that our approach has a performance similar to the ground-truth results and could be obviously better than the baseline methods in terms of RUL prediction accuracy as the time cycle increases. Additionally, experimental results demonstrate the effectiveness of optimizing maintenance actions. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Radar Sea Clutter Modeling and Reconstruction Based on Statistical Singularity Power Spectrum

Radar Sea Clutter Modeling and Reconstruction Based on Stati...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Gang Xiong Baigang Huang Science and Technology on Communication Information Security Control Lab. Jiaxing China Shanghai Key Lab. for Intelligent Sensing and Recognition SJTU Shanghai China Science and Technology on Communication Information Security Control Laboratory No.36 Research Institute of CETC Jiaxing China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

In this paper, the radar clutter reconstruction model based on singularity power spectrum (SPS) and instantaneous singularity exponent (ISE) is proposed. The proposed ISE-SPS model consists of power measure channel and dimension measure channel. The multi-scale wavelet coefficients are estimated based on the coupling of the two channels to reconstruct the radar sea clutter. Experiment based on the Ice Multi-parameter Imaging X-Band (IPIX) indicates that the proposed model behaves better than the N-partitioned random multiplicative model (NRMM), with about 32% increment in terms of correlation coefficient and normalized MSE of MFS and SPS.

关键词： Power measurement Radar clutter Sea measurements Radar imaging Clutter Image reconstruction Wavelet coefficients

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A Novel eemd-based privacy preserving approach for top-k snps query in genome-wide association studies 6

A Novel eemd-based privacy preserving approach for top-k snp...

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6th IEEE International Conference on control science and Systems Engineering, ICCSSE 2020

作者： Xiaoyun, He Hao, Guo Hongsong, Chen Global Energy Interconnection Research Institute Co Ltd State Grid Key Laboratory of Information Network Security Beijing China University of Science and Technology Beijing School of Computer Communication Engineering Beijing China

ISBN: (纸本)9781728198460

Genome-wide association studies (GWAS) have been a popular method for querying Top-k most significant sets of singe-nucleotide polymorphism locations (SNPs) to discover the genetic factors of diseases. Doctors have been increasing interest to in querying SNPs that significantly associated to diseases. However, the queries and GWAS data share service bring great privacy breach risk to the patients. In traditional Laplace privacy preserving mechanism, privacy budget \varepsilonis different to be determined, the Laplace noise is added to original data only once, which causes randomness and potential security risk to privacy protection. A novel approach is proposed to implement the differential privacy preserving for Top-k SNPs query in GWAS. Ensemble Empirical Mode Decomposition(EEMD) is a noise-Assisted data analysis method, it is firstly proposed to be used as noise-Adding approach in privacy protection mechanism. In the EEMD approach, random white noise will be added into the SNPs querying results for many times. Moreover, the parameters of added white noise can be precisely controlled to achieve better effect. PriSTRAT software tool was redeveloped to evaluate EEMD-based privacy preserving approach, the dataset was generated by PLINK tools. Experimental results show that EEMD-based privacy preserving approach has achieved higher accuracy than Laplace mechanism under the same noise-Adding strength. The novel approach could be used to realize differential privacy preserving for Top-k SNPs query in GWAS to achieve the balance between privacy preserving and query accuracy. © 2020 IEEE.

关键词： Empirical mode decomposition

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Unsupervised Domain Adaptation Using Attention Network and New Loss Function

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 704-718页

作者： Zhao, Li-Quan Zhou, Si-Ying Teng, Zi-Ming Jiang, Zi-Cong Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China College of information and communication Jilin University Changchun130012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan School of Electrical and Information Engineering Beihua University Jilin132013 China

An unsupervised domain adaptation method is proposed based on the step-wise adaptive feature norm method to improve transfer learning accuracy. The feature extraction network consists of channel attention and spatial attention modules, which en-hance feature extraction ability. To reduce the influence of dimension change on channel weight, a convolution layer with the same kernel size is used to replace the fully connected layer in the bottleneck layer used in the common channel attention module. In addition, a smooth loss function is also proposed. The curve of the proposed function is smoother than the L2 function that is the least square error function. The proposed function’s gradient changes more slowly than the L2 function. The proposed function is used as the activate function of classification prediction regression to reduce the risk of gradient explosion. Compared with a selection of existing methods (Resnet50, conditional domain adversarial networks method, deep adaptive network method, joint adaptation network method, domain adaptation neural network method, and stepwise adaptive feature norm method), the proposed transfer learning method has the highest accuracy than other methods on different datasets for all tasks. © 2022 Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Feature extraction

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