摘要：
Aiming to maximize coverage performance and reduce the number of sensors deployed in the reconfigurable wireless sensor networks (RWSNs), in this paper, we first formulate a new cooperative sensing coverage control problem based on the confident information coverage model. Then, inspired by the reinforcement learning and resampling technology, a novel learning automata-based resampling particle swarm optimization (RPSOLA) algorithm is proposed to solve complex multi-peak optimization problem and optimize the cooperative sensing coverage control problem of RWSNs. Experimental results demonstrate that the RPSOLA considerably outperforms other three peer schemes, the RPSO, BASPSO and PSO, in terms of the convergence, coverage rate and sensor redundancy.
期刊：
2022 14th International Conference on Computer Research and Development (ICCRD),2022年:115-120
作者机构：
[Yuang Liu] Ande College, Xi'an University of Architecture and Technology, Xi'an, China;[Zhuo Huang] School of Electrical Engineering, The University of South China, Hengyang, China;[Lewen Sun] College of science and technology, Wenzhou-Kean University, Wenzhou, China
摘要：
During the last decade of the development of computer networks, it is more and more important to identify multiple network attacks to improve computer security. This paper based is on NSL-KDD datasets to achieve the purpose of identifying network attacks. This research not only focuses on improving the accuracy that got from training datasets but also manages to improve the accuracy that gets from official test datasets which is more similar to real life. To get the best accuracy, we applied Random Forest, which is the best model previously. In this model, we use several data reduction methods to improve model performance. Next, we propose a model that has not been used before, which is Artificial Neural Network. According to the accuracy we get from ANN, we found that this model has better performance than traditional models, which increase test accuracy from 0.759 to 0.825. The results showed that ANN has entirely satisfactory performance in intrusion detection.
期刊：
2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics),2022年:101-106
作者机构：
[Yuan Tian; Yihui Sun; Yong Tang] School of Electrical Engineering, University of South China, Hengyang, China
摘要：
Wireless sensor networks are widely used to sense physical properties in the environment, such as temperature, humidity, and air pollution concentration. To improve the ability to monitor air pollution indicators, air pollution monitoring sensors must be precisely deployed in the monitored area. Therefore, how to minimize the cost of network deployment while ensuring the accuracy of cost monitoring has become the most critical issue. Aiming at the optimization problem of wireless sensor network node deployment for air pollution monitoring, this paper proposes a new sensor node deployment method. The confident information coverage (CIC) model is adopted as the basic coverage model, and the node deployment model is formulated using integer linear programming. Correspondingly, a polynomial-time heuristic algorithm based on variable relaxation is proposed. Finally, experiments show that the proposed algorithm can quickly and efficiently obtain optimal deployment in practical scenarios, and requires fewer deployment nodes than other compared deployment methods.
通讯机构：
[Xing Qu; Lin Jiang; Siyu Lin; Juan Wen; Lin Ding] C;College of electrical engineering,University of South China,Hengyang, 421000,China
关键词：
Introduction;Materials and Methods;Results;Discussion;Conclusion;Abstract;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interests;Authors’ Contributions;Funding Statement;Acknowledgements;Acknowledgments;Supplementary Materials;Reference;Dataset Description;Dataset Files;Abstract;Introduction;Introduction and Materials;Introduction and Methods;Materials;Materials and Methods;Methods;Results;Discussion;Results and Discussion;Discussion and Conclusion;Results and Conclusion;Conclusion;Conclusions;Data Availability;Additional Points;Ethical Approval;Consent;Disclosure;Conflicts of Interest;Authors’ Contributions;Funding Statement;Acknowledgements;Supplementary Materials;References;Appendix;Abbreviations;Preliminaries;Introduction and Preliminaries;Notation;Proof of Theorem;Proofs;Analysis of Results;Examples;Numerical Example;Applications;Numerical Simulation;Model;Model Formulation;Systematic Palaeontology;Nomenclatural Acts;Taxonomic Implications;Experimental;Synthesis;Overview;Characterization;Background;Experimental;Theories;Calculations;Model Verification;Model Implementation;Geographic location;Study Area;Geological setting;Data Collection;Field Testing;Data and Sampling;Dataset;Literature Review;Related Works;Related Work;System Model;Methods and Data;Experimental Results;Results and Analysis;Evaluation;Implementation;Case Presentation;Case Report;Search Terms;Case Description;Case Series;Background;Limitations;Additional Points;Case;Case 1;Case 2 etc.;Concern Details;Retraction Details;Copyright;Related Articles
摘要：
Network optimization is one of an effective ways to enhance the performance of an active distribution network (ADN). Aiming to improve the operation and power quality of the ADN considering time variations in load and renewable distributed generation (RDG) power, a multi-time period optimization model and its dynamic solution method are proposed. Considering the real time load demand and power generation variation of RDG versus input parameters like wind speed and solar irradiance, the time variation models of load and RDG power output are developed. The minimum power loss and maximum absorption of RDG power are served as the optimization indexes to construct the dynamic muti-time period optimization model. A hybrid particle swarm optimization (HPSO) algorithm is presented based on integer coding and random coding technique, which can find the most satisfactory solutions for the proposed dynamic model. Considering the time variation of load and RDG power of ADN, the optimal network structure and RDG allocation scheme at any time interval are determined by analyzing the obtained solutions. Additionally, two ADNs with time variation in load and RDG are tested to verify the effectiveness and superiority of the proposed dynamic optimization model and HPSO algorithm. The simulation results show that the proposed method can improve the operation performance and RDG optimal utilization of the ADNs through muti-time period dynamic optimization.
作者机构：
[Qin, Chuanbo; Wu, Yujie; Zeng, Junying; Zhai, Yikui] Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China;[Li, Fang] Automation Science and Engineering, South China University of Technology, Guangzhou, China;[Fang Li] Jiangmen Maternal and Child Healthcare Hospital, Jiangmen, China;[Zhang, Xiaozhi] School of Electrical Engineering, University of South China, Hengyang, China
通讯机构：
[Junying Zeng] F;Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen, China
摘要：
This work investigates a joint power allocation and transmission scheduling algorithm for underwater acoustic (UWA) networks to improve the throughput by leveraging the low sound speed in water. We consider a round-based UWA system where transmission links are active concurrently to send multiple signal blocks during each round. To mitigate the interference, a quasi-interference alignment (IA) method is proposed where the interference signals are aligned as much as possible while a low level of collisions between useful and interference signals is allowed. Specifically, the transmission starting time and transmission power are jointly optimized to maximize the total transmission capacity. Different from the conventional IA, the proposed mechanism intends to reduce the total reception time by tolerating slight collisions of useful signals while adapts the power allocation to compensate for the collisions, which could increase the transmission degrees of freedom (d.o.f.). To solve the optimization problem in a tractable way, we decompose the problem into two smaller sub-problems which optimize the transmission schedule and power allocation with a smaller feasible region. The simulation results show that, with an appropriate collision level, the proposed method could achieve decent throughput improvement, compared to the conventional IA and a benchmark method which solves the optimization problem by the genetic algorithm.
作者机构：
[Wang, Mingfeng; Li, Zhuanxia; Xiong, Wei] Wenzhou Univ, Dept Phys, Wenzhou 325035, Zhejiang, Peoples R China.;[Zhang, Guo-Qiang] Hangzhou Normal Univ, Sch Phys, Hangzhou 311121, Peoples R China.;[Li, Hai-Chao] Hubei Normal Univ, Coll Phys & Elect Sci, Huangshi 435002, Peoples R China.;[Luo, Xiao-Qing] Univ South China, Sch Elect Engn, Hunan Prov Key Lab Ultrafast Micro Nano Technol &, Hengyang 421001, Peoples R China.;[Chen, Jiaojiao] Anhui Univ, Sch Phys & Optoelect Engn, Hefei 230601, Peoples R China.
摘要：
Higher-order exceptional points (EPs) in non-Hermitian systems have attracted great interest due to their advantages in sensitive enhancement and distinct topological features. However, realization of such EPs is still a challenge because more fine-tuning parameters are generically required in quantum systems, compared to the second-order EP (EP2). Here, we propose a non-Hermitian three-mode optomechanical system in the blue-sideband regime for predicting the third-order EP (EP3). By deriving the pseudo-Hermitian condition for the proposed system, one cavity with loss and the other with gain must be required. Then we show that EP3 or EP2 can be observed when the mechanical resonator (MR) is neutral, loss, or gain. For the neutral MR, we find that two degenerate or two nondegenerate EP3s can be predicted by tuning system parameters in the parameter space, while four nondegenerate EP2s can be observed when the system parameters deviate from EP3s, which is distinguished from the previous study in the red-detuned optomechanical system. For the gain (loss) MR, we find that only two degenerate EP3s or EP2s can be predicted by tuning enhanced coupling strength. Our proposal provides a potential way to predict higher-order EPs or multiple EP2s and study multimode quantum squeezing around EPs using blue-detuned non-Hermitian optomechanical systems.
