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Search Results (11)
  • Open Access

    ARTICLE

    Predicting Turbidite Channel in Deep-Water Canyon Based on Grey Relational Analysis-Support Vector Machine Model: A Case Study of the Lingshui Depression in Qiongdongnan Basin, South China Sea

    Haichen Li1,2, Jianghai Li1, Li Li3,4,*, Zhandong Li5,*

    Energy Engineering, Vol.121, No.9, pp. 2435-2447, 2024, DOI:10.32604/ee.2024.050771 - 19 August 2024

    Abstract The turbidite channel of South China Sea has been highly concerned. Influenced by the complex fault and the rapid phase change of lithofacies, predicting the channel through conventional seismic attributes is not accurate enough. In response to this disadvantage, this study used a method combining grey relational analysis (GRA) and support vector machine (SVM) and established a set of prediction technical procedures suitable for reservoirs with complex geological conditions. In the case study of the Huangliu Formation in Qiongdongnan Basin, South China Sea, this study first dimensionalized the conventional seismic attributes of Gas Layer Group… More >

  • Open Access

    ARTICLE

    Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

    Jianfeng Zhao1,2, Yunliang Huo1,2, Ji Xiong1,*, Junbo Liu1,2, Zhixing Guo1, Qingxian Li3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1663-1678, 2024, DOI:10.32604/cmes.2023.030584 - 17 November 2023

    Abstract To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform, a case-based reasoning framework is proposed. Specifically, a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties. In addition, AHP (Analytic Hierarchy Process) and entropy weight method are integrated to provide features weight, where both user preferences and comprehensive impact of the index have been concerned. Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.… More >

  • Open Access

    ARTICLE

    Materials Selection of Thermoplastic Matrices of Natural Fibre Composites for Cyclist Helmet Using an Integration of DMAIC Approach in Six Sigma Method Together with Grey Relational Analysis Approach

    N. A. Maidin1,2, S. M. Sapuan1,*, M. T. Mastura2, M. Y. M. Zuhri1

    Journal of Renewable Materials, Vol.11, No.5, pp. 2381-2397, 2023, DOI:10.32604/jrm.2023.026549 - 13 February 2023

    Abstract Natural fibre reinforced polymer composite (NFRPC) materials are gaining popularity in the modern world due to their eco-friendliness, lightweight nature, life-cycle superiority, biodegradability, low cost, and noble mechanical properties. Due to the wide variety of materials available that have comparable attributes and satisfy the requirements of the product design specification, material selection has become a crucial component of design for engineers. This paper discusses the study’s findings in choosing the suitable thermoplastic matrices of Natural Fibre Composites for Cyclist Helmet utilising the DMAIC, and GRA approaches. The results are based on integrating two decision methods More >

  • Open Access

    ARTICLE

    Short-Term Power Load Forecasting with Hybrid TPA-BiLSTM Prediction Model Based on CSSA

    Jiahao Wen, Zhijian Wang*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 749-765, 2023, DOI:10.32604/cmes.2023.023865 - 05 January 2023

    Abstract Since the existing prediction methods have encountered difficulties in processing the multiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, the bidirectional LSTM layer models the data of the historical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant… More >

  • Open Access

    ARTICLE

    Multi-index cutting parameters optimization for surface quality and cutting energy consumption of boring

    Youji Zhan1, Jiaqi Zhou1, Zhen Zuo1, Yongchao Xu1, Xiaoshui Li1, Minzhong Jia1

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.38, No.1, pp. 1-18, 2022, DOI:10.23967/j.rimni.2022.03.004 - 10 March 2022

    Abstract Saving energy is one of the ways to achieve sustainable development. As an important equipment for manufacturing, machine tool has the characteristics of high energy consumption and high emission. In order to cope with reducing energy consumption and carbon emissions without reducing processing quality, the search for optimal cutting parameters requires balancing the contradiction between machining quality and cutting energy consumption, so that cutting parameters can both reduce energy consumption and ensure the quality of processing. It plays an important role in achieving energy saving and emission reduction. In this paper, the processing quality (residual More >

  • Open Access

    ARTICLE

    PREDICTING THE WAX DEPOSITION RATE BASED ON EXTREME LEARNING MACHINE

    Qi Zhuanga,* , Zhuo Chenb, Dong Liuc, Yangyang Tiand

    Frontiers in Heat and Mass Transfer, Vol.19, pp. 1-8, 2022, DOI:10.5098/hmt.19.19

    Abstract In order to improve the accuracy and efficiency of wax deposition rate prediction of waxy crude oil in pipeline transportation, A GRA-IPSO-ELM model was established to predict wax deposition rate. Using Grey Relational Analysis (GRA) to calculate the correlation degree between various factors and wax deposition rate, determine the input variables of the prediction model, and establish the Extreme Learning Machine (ELM) prediction model, improved particle swarm optimization (IPSO) is used to optimize the parameters of ELM model. Taking the experimental data of wax deposition in Huachi operation area as an example, the prediction performance More >

  • Open Access

    ARTICLE

    Optimum Design for the Magnification Mechanisms Employing Fuzzy Logic–ANFIS

    Ngoc Thai Huynh1, Tien V. T. Nguyen2, Quoc Manh Nguyen3,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5961-5983, 2022, DOI:10.32604/cmc.2022.029484 - 28 July 2022

    Abstract To achieve high work performance for compliant mechanisms of motion scope, continuous work condition, and high frequency, we propose a new hybrid algorithm that could be applied to multi-objective optimum design. In this investigation, we use the tools of finite element analysis (FEA) for a magnification mechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements. A poly-algorithm including the Grey-Taguchi method, fuzzy logic system, and adaptive neuro-fuzzy inference system (ANFIS) algorithm, was utilized mainly in this study.… More >

  • Open Access

    ARTICLE

    Using the Taguchi Method and Grey Relational Analysis to Optimize the Performance of a Solar Air Heater

    Manar B. AL-Hajji1,*, Nabeel Abu Shaban2, Shahnaz Al Khalil2, Ayat Al-Jarrah3

    Energy Engineering, Vol.118, No.5, pp. 1425-1438, 2021, DOI:10.32604/EE.2021.016413 - 16 July 2021

    Abstract Solar energy is regarded as one of the promising renewable energy sources in the world.The main aim of this study is to use the Taguchi-Grey relational grade analysis to optimize the performance of two Solar Air Heaters (SAHs). A typical Grey–Taguchi method was applied. The Orthogonal Array, Signal-to-Noise ratio, Grey Relational Grade, and Analysis of Variance were employed to investigate the performance characteristics of SAH. Experimental observations were made in agreement with Jordanian climate 32°00′ N latitude and 36°00′ E longitude with a solar intensity of 500 W\m2. The operating factors selected for optimization are the… More >

  • Open Access

    ARTICLE

    Grain Yield Predict Based on GRA-AdaBoost-SVR Model

    Diantao Hu, Cong Zhang*, Wenqi Cao, Xintao Lv, Songwu Xie

    Journal on Big Data, Vol.3, No.2, pp. 65-76, 2021, DOI:10.32604/jbd.2021.016317 - 13 April 2021

    Abstract Grain yield security is a basic national policy of China, and changes in grain yield are influenced by a variety of factors, which often have a complex, non-linear relationship with each other. Therefore, this paper proposes a Grey Relational Analysis–Adaptive Boosting–Support Vector Regression (GRAAdaBoost-SVR) model, which can ensure the prediction accuracy of the model under small sample, improve the generalization ability, and enhance the prediction accuracy. SVR allows mapping to high-dimensional spaces using kernel functions, good for solving nonlinear problems. Grain yield datasets generally have small sample sizes and many features, making SVR a promising… More >

  • Open Access

    ARTICLE

    A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features

    Wei Sun1, 2, *, Xiaorui Zhang2, 3, Xiaozheng He4, Yan Jin1, Xu Zhang3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2489-2510, 2020, DOI:10.32604/cmc.2020.012343 - 16 September 2020

    Abstract Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity… More >

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