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Construction of energy minimized WSN using GA-SAMP-MWPSO and k-mean clustering algorithm with LDCF deployment strategy

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JOURNAL OF SUPERCOMPUTING 2022年第8期78卷 11015-11050页

作者： Banerjee, Avishek De, Sudip kumar Majumder, koushik Dash, Dinesh Chattopadhyay, Samiran Bennett Univ Sch Comp Sci Engn & Technol Greater Noida UP India Asansol Engn Coll Dept Informat Technol Asansol 713305 W Bengal India MAKAUT WB Dept Comp Sci & Engn Kolkata India NIT Dept Comp Sci & Engn Patna Bihar India Jadavpur Univ Inst Adv Intelligence TCG Ctr Res & Educ Sci & Technol Kolkata 700098 India Jadavpur Univ Dept Informat Technol Kolkata 700098 India

Energy is the most valuable resource in WSN. As the power storage capability of the tiny WSN is very limited, efficient mechanisms for energy minimization should be implemented to increase the overall lifetime of the network. Efficient clusterization of the area, cluster head selection leads toward the energy minimization of the whole network. Previously very few research works had been reported where the use of serve points had been considered. In this research work, the WSN has been configured depending upon many important parameters like (1) the frequency of serve points in a particular area and (2) WSN node deployment strategy in the target area. The main goal of this paper is to construct an efficient wireless sensor network. The efficiency of the network directly depends on the minimization technique of the consumed energy. So by minimizing the consumed energy an efficient wireless sensor network has been established. In this research, the specific region (target area) has been clustered using a modified k-mean algorithm to fix the position of the sink node. Deployment of WSN nodes in the target area has been done using least distance connect first (LDCF) deployment strategy. The cluster head has been selected for each cluster for each duty cycle. After configuring the WSN by using a modified k-mean algorithm and (LDCF) deployment strategy, a modified metaheuristic hybrid algorithm (GA-SAMP-MWPSO) has been used to get the optimized energy consumption for the network. Ultimately the consumed energy and the total day-life of the network have been calculated. The result has been compared with the existing literature, and it has been noted that the proposed technique yields 15.5544456 day-life of the network where the existing literature's approach gives 6.333777 day-life of the network. The proposed algorithm delivers a better outcome.

关键词： Wireless sensor network (WSN) Deployment strategies k-mean clustering algorithm Genetic algorithm-self-adaptive multi-population-based mean-weighted particle swarm optimization (GA-SAMP-MWPSO) Metaheuristic methods Energy minimization

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Research on the Application of clustering algorithm for Online Education Students Based on Artificial Intelligence 24

Research on the Application of Clustering Algorithm for Onli...

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International Conference on algorithms, Software Engineering, and Network Security (ASENS)

作者： Liu, Bingzhan Shandong Inst Commerce & Technol Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798400709784

The aim of this study is to explore the application of artificial intelligence in clustering algorithms for student data in the field of online education. clustering analysis, as a data mining technique, effectively supports the discovery of student behavior patterns. Firstly, the principle of clustering analysis is elaborated, and by listing the applicable data types, the implementation principles and characteristics of k-means clustering algorithm and grid clustering algorithm are thoroughly analyzed. To enhance the accuracy of clustering, the GBkM clustering algorithm is introduced, which combines the advantages of Bayesian theory to improve the efficiency and accuracy of clustering. Subsequently, a design of an AI-based online education platform is described, outlining the software development model and system functionality design of the platform. Through time complexity analysis and experimental result comparison, the performance differences between the GBkM algorithm and traditional grid clustering and k-means clustering algorithms are tested, further confirming the superiority of GBkM algorithm in clustering students for online education and revealing the potential application value of AI technology in personalized teaching resource recommendation systems.

关键词： Artificial Intelligence Online Education Platform k-mean clustering algorithm GBkM clustering algorithm

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Research on the Design and Sustainable Evaluation of Metro-Based Underground Logistics Systems

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IEEE ACCESS 2023年 11卷 67600-67612页

作者： Hu, Hui Wang, Jinglei Southwest Univ Business Coll Chongqing 400715 Peoples R China

Along with the development of underground space and the increasing maturity of automation technology, a new type of transport and supply system called "Underground Logistics System (ULS)" is gradually gaining attention in various countries as an effective means of solving the problem of urban traffic congestion. However, the high cost and risk of underground construction are the main factors that have led to ULS remaining in the theoretical stage for the time being. Therefore, the paper aims to design a solution for implementing a metro-based underground logistics system (M-ULS). Firstly, the paper uses the k-mean clustering algorithm and genetic algorithm for path planning of conventional ground transportation and M-ULS. Secondly, the article is a retrofitting design for a site that combines the metro with an underground logistics system and simulates the underground logistics nodes with the help of AnyLogic simulation software. Finally, the paper explores the economic and environmental value of M-ULS in terms of carbon emissions, time costs, fuel costs, and environmental pollution costs. The research provides a solution to alleviate urban traffic congestion, reduce environmental pollution and promote sustainable urban development.

关键词： Metro-based underground logistics system k-mean clustering algorithm genetic algorithm AnyLogic simulation sustainable urban development

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New energy electric vehicle battery health state prediction based on vibration signal characterization and clustering

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HELIYON 2024年第1期10卷 e23420页

作者： Lu, Liping Zhai, Huiying Gao, Yun Henan Polytech Zhengzhou 450046 Peoples R China

The health status of the battery of new energy electric vehicles is related to the quality of vehicle use, so it is of high practical application value to predict the health status of the battery of electric vehicles. In order to predict the health status of lithium battery, this study proposes to optimize the empirical modal decomposition method and obtain the ensemble empirical modal decomposition algorithm, and use this algorithm to collect the vibration signal of the battery, then use wavelet transform to pre-process the collected signal, and finally combine k-mean clustering and particle swarm algorithm to cluster the signal types to complete the prediction of battery State of Health. The experimental results show that the ensemble empirical modal decomposition algorithm proposed in this study can effectively perform signal acquisition for different state types of batteries, and the k-mean clustering-particle swarm algorithm predicts a 63 % decrease in the health state of the battery at 600 cycles, with a prediction error of 2.6 %. Therefore, the algorithm proposed in this study is feasible in predicting the battery health state.

关键词： Vibration signal k-mean clustering algorithm EEMD Prediction Lithium battery

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Research on Elderly Services Based on Matlab Image Processing and SPSSPRO 6

Research on Elderly Services Based on Matlab Image Processin...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Yang, Yijin North University of China Shanxi Taiyuan China

ISBN: (纸本)9798350313444

At present, the trend of aging in China is getting more and more serious, and it is of great significance to predict the number of beds demanded by the elderly. In this paper, we firstly investigated and analyzed, we chose the number of the elderly population, the "elderly model 9037", the constant level of Chinese household consumption and the degree of self-reliance of the elderly as the variables of the model of the demand for elderly service beds. By reviewing the number of elderly population and the number of labor employment in China from 2009-2018 using polynomial fitting to reasonably predict the values of the two variables for each year from 2019-2025, substituting them into the model of demand for senior care service beds to derive the values of the demand for senior care service beds. Secondly, the model of balance of payments and revenues is established to consider the costs and revenues, and the amount of revenues and expenditures is calculated according to the different types of elderly people. The potential revenue in different years is calculated to quantify the size of the market opportunity. meanwhile, to further address the current situation of the prominent contradiction between supply and demand of elderly facilities in Jiancaoping District, Taiyuan City, this paper utilizes Matlab image processing technology and SPSSPRO software to carry out k-mean clustering algorithm, and designs a reasonable planning scheme for the elderly facilities in the district. © 2023 IEEE.

关键词： 9037 Pension Model k-mean clustering algorithm Matlab Image Processing Polynomial Fitting SPSSPRO

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Extraction of LMS Student Engagement and Behavioral Patterns in Online Education Using Decision Tree and k-means algorithm 2022

Extraction of LMS Student Engagement and Behavioral Patterns...

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4th Asia Pacific Information Technology Conference, APIT 2022

作者： Fernando Raguro, Ma.Corazon Carpio Lagman, Ace Abad, Lalaine P. Ong, Paul Lawrenz S. Information Technology FEU Institute of Technology Manila Philippines

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E-commerce recommender system based on improved k-means commodity information management model

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HELIYON 2024年第9期10卷 e29045页

作者： Zhang, Wei Wu, Zonghua South China Univ Technol Sch Math Guangzhou 510641 Guangdong Peoples R China

Since the start of the 21st century, there has been a rapid development of internet technology, causing electronic computers and smartphones to become increasingly popular. The e -commerce industry also experiences quick development. However, the recommendation technology of ecommerce progresses slowly, hindering it from keeping up with the changing times. To enhance the efficiency and accuracy of e -commerce recommender systems, this research introduces an ecommerce recommender system that utilizes an enhanced k -means clustering algorithm to manage commodity information. This method combines the k -means algorithm with a genetic algorithm by encoding the genetic algorithm, setting the initial population, defining the fitness function, and configuring other parameters. The results of the test indicated that the k -mean clustering algorithm and fuzzy C -mean algorithm had a recommendation accuracy of 87.9 % and 84.8 % respectively under the test dataset. The highest recommendation accuracy was observed from the improved k -mean clustering algorithm, which was 91.1 %. The convergence rate of the improved k -mean clustering algorithm was faster by 44 % compared to the traditional k -mean clustering algorithm and 73 % quicker than the fuzzy C -mean algorithm. The study ' s findings demonstrate that the refined k -means clustering algorithm greatly enhances the recommendation proficiency and precision of the e -commerce recommendation system, in comparison to other comparable algorithms. This research can potentially advance the e -commerce industry and stimulate its growth.

关键词： E-commerce Recommender system k-mean clustering algorithm Fuzzy C-mean algorithm Genetic algorithm

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Delineation of the Arm Blood Vessels Utilizing Hyperspectral Imaging to Assist with Phlebotomy for Exploiting the Cutaneous Tissue Oxygen Concentration

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PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY 2021年 33卷 102190-102190页

作者： Fouad Aref, Mohamed Hisham Sharawi, Amr A. R. El-Sharkawy, Yasser H. Mil Tech Coll Biomed Engn Dept Cairo Egypt Cairo Univ Fac Engn Syst & Biomed Engn Dept Giza Egypt

Significance: The estimation of tissue oxygenation is vital in the diagnosis and therapeutic evaluation of a huge assortment of diseases. The hyperspectral (HS) imaging system is a rising innovation that can be utilized to build a highly sensitive, non-invasive, and tissue hemoglobin immersion map. Objective: As a result of the urgent need to design and implement early detection devices and applications for the COVID-19 pandemic, we propose building a non-invasive custom optical imaging system to assist with phle-botomy and vascular approach to survey the reliability of blood oxygen saturation (SpO2) levels recovered from spectral images. Materials and Methods: HS images were gathered from 15 healthy subjects without previous medical history complications and with an average age range of 20 to 38 years, who were undergoing phlebotomy. The forearm was vigorously illuminated utilizing an HS camera with polychromatic source light of spectrum range (400 similar to 980 nm). Spectroscopic reflectance images were caught by a focal plane exhibit for the region of interest (ROI). Then the custom algorithm comprising normalization and moving average filtering for noise removal was applied, followed by k-mean clustering for image segmentation to visualize and highlight the arteries and the veins in the investigated forearm. Results: The investigations show that after normalization of the recorded signal from the HS camera of the participating subjects it was noticed that at wavelength of 460 nm the oxygenated arteries had a stronger signal than the de-oxygenated veins, and at a wavelength of 750 nm the de-oxygenated veins had a stronger signal than the oxygenated arteries. Thus, the ideal wavelength to reveal the oxygenated arteries was 460 nm, and the ideal wavelength to reveal the de-oxygenated veins was 750 nm. Conclusions: HSI is a prospective technique to assist with phlebotomy and non-contact oxygen saturation approach. Additionally, it may permit future surgical or phar

关键词： Blood Vessels Viewer Hyperspectral Imaging system Oxygen Saturation k-mean clustering algorithm Remote Sensing

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Real-time energy management for commute HEVs using modified A-ECMS with traffic information recognition

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第4期13卷 729-737页

作者： Li, Yang Jiao, Xiaohong Yanshan Univ Sch Elect Engn Qinhuangdao Hebei Peoples R China

To further improve fuel consumption performance of hybrid electric vehicles (HEVs) running on commute route in the face of time-varying traffic information, this paper investigates a real-time energy management strategy based on the adaptive equivalent consumption minimization strategy (A-ECMS) framework with traffic information recognition. The proposed management strategy integrates the global near optimization and the real-time performance. The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic data to form four clusters. The adaptive equivalence factor of the A-ECMS is designed as a three-dimensional mapping on each cluster and the system states by employing stochastic dynamic programming (SDP) policy iteration to solve offline the stochastic optimal control problem formulated by each cluster statistical characteristic. In real-time energy management controller online, the instantaneous power split is performed by the ECMS with a proper equivalent factor, which is obtained from mappings according to the cluster recognised by the current traffic situation and the state-of-charge (SOC). The effectiveness of the designed control strategy is verified by the simulation test conducted on GT-suite HEV simulator over real driving cycles.

关键词： dynamic programming energy management systems hybrid electric vehicles optimal control stochastic programming state-of-charge k-mean clustering algorithm current traffic situation proper equivalent factor real-time energy management controller online cluster statistical characteristic stochastic optimal control problem stochastic dynamic programming policy iteration adaptive equivalence factor historical traffic data simple traffic recognition real-time performance adaptive equivalent consumption minimization strategy real-time energy management strategy time-varying traffic information hybrid electric vehicles fuel consumption performance traffic information recognition A-ECMS commute HEVs GT-suite HEV simulator

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Regional and seasonal variations of outdoor thermal comfort in China from 1966 to 2016

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 665卷 1003-1016页

作者： Wu, Feifei Yang, Xiaohua Shen, Zhenyao Beijing Normal Univ State Key Lab ofWater Environm Simulat Sch Environm Beijing 100875 Peoples R China

The optimized design of outdoor environment is of utmost importance due to its impact on human health, urban livability and energy consumption inside buildings. The outdoor thermal comfort and its spatiotemporal variations were assessed using Universal Thermal Climate Index (UTCI). Annual and seasonal UTCI were calculated using the daily dataset collected from 591 stations in China between 1966 and 2016. A REOF-cluster-EOF hybrid model was developed to optimize regionalization and assess regional-scale variations for UTCI. The results showed the following: (1) UTCI values decreased due to the increase of the latitude in China except for the Qinghai-Tibet Plateau. 69.5% of the total area of China experienced "no thermal stress" conditions in summer, whereas it was only 7.7% in winter. Additionally, the outdoor environment in summer had a wider "thermal comfort zone" than that in other seasons. (2) China was divided into a small number of regions with coherent UTCI changes using REOF analysis and k-means clustering algorithm. Eight homogeneous regions were obtained for annual UTCI. From spring to winter, the numbers of homogeneous regions were eight, nine, ten and seven, respectively. (3) Using EOF analysis, dominant patterns of UTCI in each region were extracted by the first two EOF modes, which accounted for >60% of the total variance. In the first mode, the significant upward trends of UTCI were detected in each region, suggesting the stronger outdoor heat stress. In the second mode, UTCI showed fluctuation between the cold and warm periods with different turning points between regions. Overall, the outdoor thermal comfort seemed to be improved more in high-latitude regions than that in low-latitude regions. (c) 2019 Elsevier B.V. All rights reserved.

关键词： Outdoor thermal comfort Universal Thermal Climate Index (UTCI) Regionalization k-mean clustering algorithm Empirical orthogonal function (EOF) analysis China

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