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  • Open Access

    ARTICLE

    Fully Automated Density-Based Clustering Method

    Bilal Bataineh*, Ahmad A. Alzahrani

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1833-1851, 2023, DOI:10.32604/cmc.2023.039923

    Abstract Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters are then used in a… More >

  • Open Access

    PROCEEDINGS

    Multi-resolution Topology Optimization Using B-spline to Represent the Density Field

    Zhenbiao Guo1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.08904

    Abstract This work proposes a novel multi-resolution topology optimization method using B-spline to represent the density field, and overcomes the defects of tedious post-processing of element-based models and low computational efficiency of topology optimization for large-scale problems. The design domain embedded in the B-spline space is discretized with a coarser analysis mesh and a finer density mesh to reduce the computational cost of finite element analysis. As design variables, the coefficients of the control points control the shape of the B-spline. The optimized B-spline can be quickly and precisely converted into a CAD model. Sensitivity filtering is additionally applied to enhance… More >

  • Open Access

    PROCEEDINGS

    Segment Crack Formation and Density Regulation in Air Plasma Sprayed Coatings

    Liuyu Yang1, Peng Jiang1, Tiejun Wang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010538

    Abstract Air Plasma Sprayed (APS) Thermal Barrier Coatings (TBCs) have been widely used in land-based gas engines for enhancing the high temperature performance due to their outstanding thermal insulation and high durability. Introducing the segment cracks into APS-TBCs to enhance its durability has been quite attractive approaches nowadays. Qualitative conclusions have been drawn to explore the mechanisms on segment crack formation in the past decades. This article acts as a quantitative study of segment crack formation and crack density regulation mechanism in APS Yttria-Stabilized Zirconia (YSZ) TBCs with experimental observations and analytical calculations. An in-situ stress measurement method is developed through… More >

  • Open Access

    ARTICLE

    Cyclists’ exposure to air pollution and noise in Mexico City

    Contribution of real-time traffic density indicators integrated into GIS

    Philippe Apparicio1 , Jérémy Gelb1, Paula Negron-Poblete2, Mathieu Carrier1, Stéphanie Potvin1 , Élaine Lesage-Mann1

    Revue Internationale de Géomatique, Vol.30, No.2, pp. 155-179, 2020, DOI:10.3166/rig.2021.00110

    Abstract Air pollution and road traffic noise are two important environmental nuisances that could be harmful to the health and well-being of urban populations. In Mexico City, as in many North American cities, there has been an upsurge in bicycle ridership. However, Mexico City is also well known for having high levels of noise and air pollution. The purpose of this study is threefold: 1) evaluate cyclists’ exposure to air pollution (nitrogen dioxide) and road traffic noise; 2) identify local factors that increase or reduce cyclists’ exposure, in paying particular attention to the type of road and bicycle path or lane… More >

  • Open Access

    ARTICLE

    Adaptive Density-Based Spatial Clustering of Applications with Noise (ADBSCAN) for Clusters of Different Densities

    Ahmed Fahim1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3695-3712, 2023, DOI:10.32604/cmc.2023.036820

    Abstract Finding clusters based on density represents a significant class of clustering algorithms. These methods can discover clusters of various shapes and sizes. The most studied algorithm in this class is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It identifies clusters by grouping the densely connected objects into one group and discarding the noise objects. It requires two input parameters: epsilon (fixed neighborhood radius) and MinPts (the lowest number of objects in epsilon). However, it can’t handle clusters of various densities since it uses a global value for epsilon. This article proposes an adaptation of the DBSCAN method so… More >

  • Open Access

    ARTICLE

    Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

    J. Jalaney1,*, R. S. Ganesh2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2819-2834, 2023, DOI:10.32604/iasc.2023.034844

    Abstract Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle… More >

  • Open Access

    ARTICLE

    A Novel Ultra Short-Term Load Forecasting Method for Regional Electric Vehicle Charging Load Using Charging Pile Usage Degree

    Jinrui Tang*, Ganheng Ge, Jianchao Liu, Honghui Yang

    Energy Engineering, Vol.120, No.5, pp. 1107-1132, 2023, DOI:10.32604/ee.2023.025666

    Abstract Electric vehicle (EV) charging load is greatly affected by many traffic factors, such as road congestion. Accurate ultra short-term load forecasting (STLF) results for regional EV charging load are important to the scheduling plan of regional charging load, which can be derived to realize the optimal vehicle to grid benefit. In this paper, a regional-level EV ultra STLF method is proposed and discussed. The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles, and then constructed by our collected EV charging transaction data in the field. Secondly, these usage degrees… More >

  • Open Access

    ARTICLE

    Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

    Musaed Alrashidi*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1073-1088, 2023, DOI:10.32604/cmc.2023.036170

    Abstract Harvesting the power coming from the wind provides a green and environmentally friendly approach to producing electricity. To facilitate the ongoing advancement in wind energy applications, deep knowledge about wind regime behavior is essential. Wind speed is typically characterized by a statistical distribution, and the two-parameters Weibull distribution has shown its ability to represent wind speeds worldwide. Estimation of Weibull parameters, namely scale and shape parameters, is vital to describe the observed wind speeds data accurately. Yet, it is still a challenging task. Several numerical estimation approaches have been used by researchers to obtain c and k. However, utilizing such… More >

  • Open Access

    ARTICLE

    A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

    Yuxuan Gu, Meng Wu*, Qian Wang, Siguang Chen, Lijun Yang

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 493-512, 2023, DOI:10.32604/cmc.2023.035974

    Abstract In this paper, a deep learning-based method is proposed for crowd-counting problems. Specifically, by utilizing the convolution kernel density map, the ground truth is generated dynamically to enhance the feature-extracting ability of the generator model. Meanwhile, the “cross stage partial” module is integrated into congested scene recognition network (CSRNet) to obtain a lightweight network model. In addition, to compensate for the accuracy drop owing to the lightweight model, we take advantage of “structured knowledge transfer” to train the model in an end-to-end manner. It aims to accelerate the fitting speed and enhance the learning ability of the student model. The… More >

  • Open Access

    ARTICLE

    Impulsive Noise Cancellation in OFDM System Using Low Density Parity Check

    Attia Irum1, Abdul Muiz Fayyaz1, Sara Ayub2, Mudassar Raza3, Majed Alhaisoni4, Muhammad Attique Khan5, Abdullah Alqahtani6, Heebum Kim7, Byeong-Gwon Kang7,*

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1265-1276, 2023, DOI:10.32604/csse.2023.032861

    Abstract An effective communication application necessitates the cancellation of Impulsive Noise (IN) from Orthogonal Frequency Division Multiplexing (OFDM), which is widely used for wireless applications due to its higher data rate and greater spectral efficiency. The OFDM system is typically corrupted by Impulsive Noise, which is an unwanted short-duration pulse with random amplitude and duration. Impulsive noise is created by humans and has non-Gaussian characteristics, causing problems in communication systems such as high capacity loss and poor error rate performance. Several techniques have been introduced in the literature to solve this type of problem, but they still have many issues that… More >

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