Showing 101 - 120 results of 7,873 for search 'comparative research algorithm', query time: 0.13s Refine Results
  1. 101

    Research on kd-tree Cache Optimization Based on Particle Index Sorting Algorithm by 张挺, 林震寰, 杨丁颖, 王宗锴, 陈轶凡

    Published 2024-01-01
    “…In these cases, the search time was shortened by 28.5% compared to unsorted <italic>kd-tree</italic> and by 22% compared to Z-index-sort. …”
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    Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor by Baofu Fang, Lu Chen, Hao Wang, Shuanglu Dai, Qiubo Zhong

    Published 2014-01-01
    “…In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.…”
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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

    Published 2014-01-01
    “…It is one of the important issues in the field of today's sewage treatment of researching the MBR membrane flux prediction for membrane fouling. …”
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  17. 117

    Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

    Published 2023-09-01
    “…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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  18. 118

    Research on FTTR WLAN indoor wireless location algorithm based on frequency response by Zhifeng LONG, Jing ZHANG

    Published 2023-09-01
    “…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.…”
    Get full text
    Article
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