Novel Node Localization Algorithm Based on Nonlinear Weighting Least Square for Wireless Sensor Networks by Fu Xiao, Mingtan Wu, Haiping Huang, Ruchuan Wang, Sudan Wang

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A New Range-Free Localization Algorithm Based on Amendatory Simulation Curve Fitting in WSN by Zhuang Liu, Xin Feng, Jingjing Zhang, Yanlong Wang, Teng Li

“…Firstly, we present the current research status of localization technology and some improved Range-free localization algorithms based on the modification of hop distance and selection of anchors. …”
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Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine by Fang Zhu, Junfang Wei

“…Sensor node localization is one of research hotspots in the applications of wireless sensor network field. …”
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An Enhanced Deep Q Network Algorithm for Localized Obstacle Avoidance in Indoor Robot Path Planning by Cheng Chen, Jiantao Yu, Songrong Qian

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Performance evaluation on extended neural network localization algorithm on 5 g new radio technology by Deebalakshmi R, S Markkandan, Vinodh Kumar Arjunan

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Surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems by Xiangyong Liu, Zan Yang, Jiansheng Liu, Junxing Xiong, Jihui Huang, Shuiyuan Huang, Xuedong Fu

“…As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions. …”
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An Auto-Adjusting Algorithm to Enhance Indoor Localization Accuracy: A Real-Time Experimental Analysis by Othman A. Alfahad, Husham Saied, Emad Malaekah, Ahmed Al Rashdi, Mohammed Emam, Mohsen Bakouri

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Boosting feature selection efficiency with IMVO: Integrating MVO and mutation-based local search algorithms by Maryam Askari, Farid Khoshalhan, Hodjat Hamidi

“…Effective feature selection lowers computational costs, making it indispensable for processing large datasets. In this research, we introduce the Improved Multi-Verse Optimizer (IMVO) algorithm, a novel feature selection method that integrates the Multi-Verse Optimizer (MVO) with local search algorithms (LSAs). …”
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Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation by Lin Sun, Suisui Chen, Jiucheng Xu, Yun Tian

“…Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. …”
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Research on Multi-Target Point Path Planning Based on APF and Improved Bidirectional RRT* Fusion Algorithm by Zijian Bian, Gang Li, Xizheng Wang

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Research on Multi-peak MPPT of Photovoltaic Array Based on Improved ICA Algorithm by ZANG Xiaodi, LIU Lu

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Research on multi-objective grid task scheduling algorithms based on survivability and Makespan by WANG Shu-peng, YUN Xiao-chun, YU Xiang-zhan

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Research on multi-objective grid task scheduling algorithms based on survivability and Makespan by WANG Shu-peng, YUN Xiao-chun, YU Xiang-zhan

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Research on cardiac image segmentation algorithm based on improved level set modeling by Shuaishuai ZHANG, Ruixia LIU, Ke SHAN, Zhenhua GUO

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The Application and Research of the GA-BP Neural Network Algorithm in the MBR Membrane Fouling by Chunqing Li, Zixiang Yang, Hongying Yan, Tao Wang

“…Then it used the BP neural network to establish the system model of the MBR intelligent simulation, the relationship between three parameters, and membrane flux characterization of the degree of membrane fouling, because the BP neural network has slow training speed, is sensitive to the initial weights and the threshold, is easy to fall into local minimum points, and so on. So this paper used genetic algorithm to optimize the initial weights and the threshold of BP neural network and established the membrane fouling prediction model based on GA-BP network. …”
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Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

“…Highly accurate and reliable indoor wireless positioning services have been widely used.In order to obtain good positioning accuracy, the design of positioning algorithms needs to be matched with wireless positioning facilities.fiber to the room (FTTR) is an indoor access network solution based on IEEE 802.11 ax, a new generation of wireless local area network (WLAN) standard.Compared with the existing Wi-Fi networks, FTTR has a much larger available band width.However, FTTR WLAN also lacks of a public valid data set to support localization functions, which makes the localization research based on FTTR scenarios face huge obstacles.In order to solve the above problems, firstly, a frequency response-based FTTR scene dataset generation method was proposed, which uses the existing Wi-Fi localization dataset to generate the frequency response matrix within the available band width of FTTR.Then, the parallel path principal component analysis (PCA) method was used to generate the classification matrix.And the generated dataset was trained using a fully connected neural network to improve the accuracy.The experimental results on the real measurement dataset show that the proposed localization algorithm can achieve a localization accuracy of less than 1 m, which is not only more accurate than the traditional location estimation algorithm, but also basically meets the fine-grained localization requirements for practical applications.…”
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Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

“…Highly accurate and reliable indoor wireless positioning services have been widely used.In order to obtain good positioning accuracy, the design of positioning algorithms needs to be matched with wireless positioning facilities.fiber to the room (FTTR) is an indoor access network solution based on IEEE 802.11 ax, a new generation of wireless local area network (WLAN) standard.Compared with the existing Wi-Fi networks, FTTR has a much larger available band width.However, FTTR WLAN also lacks of a public valid data set to support localization functions, which makes the localization research based on FTTR scenarios face huge obstacles.In order to solve the above problems, firstly, a frequency response-based FTTR scene dataset generation method was proposed, which uses the existing Wi-Fi localization dataset to generate the frequency response matrix within the available band width of FTTR.Then, the parallel path principal component analysis (PCA) method was used to generate the classification matrix.And the generated dataset was trained using a fully connected neural network to improve the accuracy.The experimental results on the real measurement dataset show that the proposed localization algorithm can achieve a localization accuracy of less than 1 m, which is not only more accurate than the traditional location estimation algorithm, but also basically meets the fine-grained localization requirements for practical applications.…”
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Research on Grounding Grid Corrosion Diagnosis Based on Genetic K‐Means Algorithm by Longsheng Huang, Xianghui Xiao, Mingxian Huang, Zhenshan Zhang, Yunhao Song, Luchang Guan

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Research on UAV path planning algorithm based on improved virtual spring method by XIAO Heng, ZHANG Hui, ZHOU Wen, MU Zhenjie

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Research on Parallel Multi-objective Optimal Submarine Cable Route Planning Algorithm by JIANG Jiarui, ZHAO Zanshan, DUAN Maosheng, GAO Guanjun

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