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  • Open Access



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

    Frontiers in Heat and Mass Transfer, Vol.19, No.1, 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 of the model is evaluated… More >

  • Open Access


    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

    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 implemented utilising two distinct decision-making… More >

  • Open Access


    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

    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 information from different time steps… More >

  • Open Access


    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

    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. The FEA outcomes indicated that… More >

  • Open Access


    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

    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 tilt angle (T) with… More >

  • Open Access


    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

    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 application for grain yield datasets.… More >

  • Open Access


    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

    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 k-nearest neighbor classifier (SKNNC). Further… More >

  • Open Access


    Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

    Pingping Xia1, *, Aihua Xu2, Tong Lian1

    Journal of Quantum Computing, Vol.2, No.1, pp. 25-32, 2020, DOI:10.32604/jqc.2019.09232

    Abstract Electricity consumption forecasting is one of the most important tasks for power system workers, and plays an important role in regional power systems. Due to the difference in the trend of power load and the past in the new normal, the influencing factors are more diversified, which makes it more difficult to predict the current electricity consumption. In this paper, the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu. According to the historical data of annual electricity consumption and the six factors affecting electricity consumption, the gray correlation analysis method is… More >

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