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improved algorithm of cluster-based routing protocols for agricultural wireless multimedia sensor networks

International Journal of Agricultural and Biological Engineering 2016年第4期9卷 132-140页

作者： Zhang Fu Liu Hongmei Wang Jun Qiu Zhaomei Mao Pengjun Zhang Yakun College of Agricultural Equipment Engineering Henan University of Science and TechnologyLuoyang 471003China Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province Luoyang 471003China

Low Energy Adaptive Clustering Hierarchy(LEACH)is a routing algorithm in agricultural wireless multimedia sensor networks(WMSNs)that includes two kinds of improved protocol,LEACH_D and LEACH_*** this study,obstacles were overcome in widely used *** improved algorithm was proposed to solve existing problems,such as energy source restriction,communication distance,and energy of the *** optimal number of clusters was calculated by the first-order radio model of the improved algorithm to determine the percentage of the cluster heads in the *** energy and the near sink nodes were chosen as cluster heads based on the residual energy of the nodes and the distance between the nodes to the sink *** the same time,the K-means clustering analysis method was used for equally assigning the nodes to several clusters in the *** simulation and the verification results showed that the survival number of the proposed algorithm LEACH-ED increased by 66%.Moreover,the network load was high and network lifetime was *** mathematical model between the average voltage of nodes(y)and the running time(x)was concluded in the equation y=−0.0643x+4.3694,and the correlation coefficient was R2=*** research results can provide a foundation and method for the design and simulation of the routing algorithm in agricultural WMSNs.

关键词： wireless sensor networks routing protocol LEACH algorithm improved algorithm cluster head K-means clustering

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Research on improved algorithm of PageRank Based on Vector Space

Research on Improved Algorithm of PageRank Based on Vector S...

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2017 2nd International Conference on Computer Engineering,Information Science and Internet Technology(CII 2017)

作者： XIANGWEI TAN GENGSHENG HUANG HUIYONG JIANG South China Institute of Software Engineering.GU Guangdong Vocational Institute Of Public Administration

The traditional PageRank algorithm,which is based on the link analysis algorithm,considers the randomness of user access behavior,but the relevance of the query topic is *** order to improve the relevance of the page data search and acquisition,this paper proposes a PageRank algorithm based on the lucent vector space scoring *** algorithm builds a vector space model based on the web content characteristics,calculates the similarity of the subject content,and combines the original PageRank algorithm,the new PR value is obtained after weighted *** show that the improved algorithm reduces the number of irrelevant pages,and the PR value can better reflect the relevance of the topic.

关键词： improved algorithm PageRank Vector Space

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An improved algorithm on Magnification of Cambered-Plate Moment Limiter in Tower Crane

An Improved Algorithm on Magnification of Cambered-Plate Mom...

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IEEE International Conference on Information and Automation (ICIA)

作者： Li, Li Shi, Kezhao Zeng, Dexue China Three Gorges Univ Hubei Key Lab Hydroelect Machinery Design & Maint Yichang 443002 Hubei Peoples R China Joinhand Construct Machinery CO LTD Jingzhou 434020 Hubei Peoples R China

ISBN: (纸本)9781509041022

Cambered-plate moment limiter in tower crane is an over-load safety protection device. The cambered-plate deflection caused by its deformation can be used to represent the load moment of tower crane. However, inaccurate calculation of magnification exists in limiter design, which results in the moment deviation of the crane. This paper proposed an algorithm to improve the magnification accuracy. The analytical formula of magnification was derived based on mechanics analysis, and the parameters in the analytical formula were calculated. Meanwhile, software simulation was used to prove the validity of the analytical formula. The results have demonstrated that the magnification accuracy can be increased by the improved algorithm.

关键词： tower crane cambered-plate moment limiter deflection magnification improved algorithm

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Optimization of Renewable Energy Hydrogen Production Systems Using Volatility improved Multi-Objective Particle Swarm algorithm

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ENERGIES 2024年第10期17卷 2384页

作者： Wang, Hui Chen, Xiaowen Yang, Qianpeng Li, Bowen Yue, Zongyu Ampah, Jeffrey Dankwa Liu, Haifeng Yao, Mingfa Qinghai Yellow River Upstream Hydropower Dev Co Lt Photovolta Ind Technol Branch Xining 810007 Peoples R China Qinghai Adv Energy Storage Lab Co Ltd Xining 810007 Peoples R China Tianjin Univ State Key Lab Combust Sci Internal Combust Engines Tianjin 300072 Peoples R China

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind-solar complementary, and wind-solar-storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

关键词： wind-solar coupling hybrid hydrogen production fluctuation characteristics improved algorithm

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Multi-threshold image segmentation research based on improved enhanced arithmetic optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第5期18卷 4045-4058页

作者： Li, Hanyu Zhu, Xiaoliang Li, Mengkun Yang, Ziwei Wen, Mengke Xinjiang Univ Coll Software Urumqi 830008 Peoples R China Xinjiang Univ Xinjiang Uygur Autonomous Reg Big Data & Intellige Urumqi 830008 Peoples R China Xinjiang Med Univ Affiliated Tumor Hosp Urumqi 830011 Peoples R China

Aiming at the shortcomings of arithmetic optimization algorithm (AOA), which has low efficiency and is prone to fall into local optimal solutions, this paper proposes an improved AOA, called IAOA, and applies it to multi-threshold image segmentation processing. Compared with the original algorithm, the improvement points are: Firstly, the AOA introduces inertia weights and cosine factors to enhance the stability of the algorithm, then the chaotic mapping is embedded into the algorithm selection search stage to accelerate the convergence speed, and finally, the strategy of Cauchy mutation and the strategy of inverse learning are introduced to avoid the algorithm from falling into the local optimum. In this paper, 23 benchmark functions in CEC-2005 are used to test the performance of IAOA, and 8 commonly used image segmentation metrics were also selected to test the image segmentation effect of IAOA algorithm. In addition, several heuristic algorithms were also selected for comparison. The results show that compared with other algorithms, IAOA is more robust and has better segmentation performance.

关键词： Arithmetic optimization algorithm (AOA) improved algorithm Multi-threshold image segmentation Cauchy mutation Reverse learning

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Basketball motion video target tracking algorithm based on improved gray neural network

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NEURAL COMPUTING & APPLICATIONS 2023年第6期35卷 4267-4282页

作者： Wang, Tianyi Shi, Cuiping Qiqihar Univ Coll Phys Educ Qiqihar 161006 Peoples R China Qiqihar Univ Coll Commun & Elect Engn Qiqihar 161006 Peoples R China

This article takes the basketball game video with high attention in sports video as an example to analyze the feature extraction of basketball game video, improve the gray neural network algorithm, and disassemble the basketball video. Moreover, this paper takes basketball, basket, and athletes as the feature extraction objects. Considering that the basketball is a round sphere and the object in the image is a circle, as well as the edge is added to the original image and saved. In addition, this paper combines the improved gray neural network algorithm to construct a basketball motion video target tracking algorithm. Finally, this paper designs experiments to verify the performance of this method. The experimental test results show that this method can effectively recognize basketball gestures with high recognition accuracy, which provides a new method for basketball posture recognition.

关键词： improved algorithm Gray neural network Video analysis Target tracking

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A deep learning algorithm for fast motion video sequences based on improved codebook model

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NEURAL COMPUTING & APPLICATIONS 2023年第6期35卷 4353-4368页

作者： Zhou, Kai Zhang, Zhendong Yuan, Rui Chen, Enqing Henan Univ Engn Dept Phys Educ Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Phys Educ Main Campus Zhengzhou 450001 Peoples R China Pukyong Natl Univ Dept Marine Sports Busan 48513 South Korea Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

Fast motion video sequence processing is quite difficult. In order to improve the effect of fast motion video sequence processing, this paper improves the traditional codebook model algorithm and proposes an improved codebook model algorithm. Moreover, this paper analyzes and summarizes the development and application of background modeling-based methods in moving target detection, and points out the applicability and limitations of traditional methods, which lays the foundation for the further research of moving target detection based on background modeling in the complex background. In addition, this paper analyzes the characteristics of fast motion videos, and combines deep learning algorithms to improve the feature recognition effect of fast motion video sequences. Finally, this paper verifies the effect of this method through experimental research. Through experimental research, we know that the improved algorithm proposed in this paper can realize effective processing of fast motion video, and can improve the feature recognition effect of motion video frames.

关键词： Codebook model improved algorithm Motion video Deep learning

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Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm

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ALEXANDRIA ENGINEERING JOURNAL 2022年第7期61卷 5351-5359页

作者： Men, Yanhua Jilin Inst Phys Educ Changchun 130022 Jilin Peoples R China

In order to improve the effect of intelligent sports prediction and analysis, this paper analyzes the traditional high-speed model and proposes a reliable sports action recognition algorithm on this basis. With this algorithm as the core algorithm, sports action recognition can be performed. The paper mainly constructs a standard database structure, inputs a reliable standardized movement model into the database structure, and compares the identified sports movement model with the standard movement model to judge the accuracy of the sports movement. Moreover, this paper uses statistical analysis to study sports training and sports competitions, and obtains prediction results through multi-factor analysis and comparison. In addition, this paper uses statistical methods to process experimental data to draw graphs and tables that can visually observe the results of sports predictive analysis. From the experimental analysis, it can be seen that the algorithm model constructed in this paper has a significant effect. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.

关键词： Gaussian fuzzy algorithm improved algorithm Intelligent system Sports prediction

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Multi-objective Optimal Tourism Route Planning and Design Based on improved Particle Swarm Optimization algorithm

Multi-objective Optimal Tourism Route Planning and Design Ba...

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作者： Yan, Jingjing Henan Vocational College of Agriculture Henan Zhengzhou China

In the tourism industry, in order to attract more tourists, there must be a key link, which is route planning. However, the complex optimization problem in tourism route planning has always been a headache. Traditional single route planning often lacks flexibility and personalization and is not conducive to deep tourism. Therefore, this paper proposed a multi-objective optimal tourism route planning and design method based on the improved particle swarm optimization (PSO), which aimed to meet the needs of people for better tourism experience and hoped to further promote the development of tourism. The experimental results showed that this method can not only obtain the optimal solution in tourism routes, but also has advantages in algorithm efficiency and convergence speed. Compared with the previous improvement, the time for solving the optimal route has been reduced by 54%, and the algorithm's efficiency in solving the optimal route has also reached 93%. This showed that the improved PSO can be used as an effective multi-objective optimal tourism route planning method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： improved algorithm Multi-objective optimal design Particle swarm optimization algorithm Tourism route planning

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Simulation of an electronic equipment control method based on an improved neural network algorithm

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ENERGY REPORTS 2022年 8卷 13409-13416页

作者： Liu, Hao Wang, Wei Fudan Univ Sch Comp Sci Shanghai 200438 Peoples R China

The neural network algorithm used to control electronic equipment is an important subject in the field of control and regulation, and its application is becoming increasingly extensive. A neural network algorithm is a very effective learning algorithm that is widely used to control electronic equipment and has the characteristics of good fault tolerance, generalisability and nonlinear mapping. However, in practice, it has many limitations (slow convergence, ease of getting stuck in local minima, forgetting old samples, etc.). To solve these problems, an advanced neural network-based algorithm is introduced, which enables the neural network to be optimised. After simulation research on the electronic equipment control method based on the improved neural network algorithm, experiments were performed, and the experimental data show that the artificial fish swarm algorithm (AFSA) training optimised PIDNN controller not only has no overshoot but also has a fast response speed and excellent dynamic performance. The optimisation by AFSA also benefits PID control, but the dynamic characteristics are slightly worse than those of the former controller by 0.1. Compared with those of the other methods evaluated, the steady-state accuracy of the optimised PIDNN is highest, with a value of 1.0. The precision values for the other two methods are 0.99 and 0.98. It can be seen from the above experimental data that training optimisation of the artificial fish swarm algorithm improves the network parameters of the PIDNN controller, thus resulting in better performance than the PIDNN controller trained by the BP algorithm.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Neural network improved algorithm Electronic device Simulation research

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