https://ojs.imeti.org/index.php/IJETI/issue/feedInternational Journal of Engineering and Technology Innovation2024-01-01T00:00:00+08:00The editorial office of IJETIijeti.taeti@gmail.comOpen Journal Systems<p><strong><em>International Journal of Engineering and Technology Innovation</em></strong> (IJETI), ISSN 2223-5329 (Print), ISSN 2226-809X (Online), is an international, multidiscipline, open access, peer-reviewed scholarly journal, published quarterly for researchers, developers, technical managers, and educators in the field of engineering and technology innovation. The official abbreviated title is <strong><em>Int. j. eng. technol. innov</em>.</strong></p> <p>IJETI is indexed by: </p> <p><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;"><span style="font-family: 'Noto Sans'; font-size: 10.5pt;"><img style="width: 220px; height: 50px;" src="https://ojs.imeti.org/public/site/images/ijeti/ESCI3.png" /><img style="font-size: 0.875rem;" /> <img src="https://ojs.imeti.org/public/site/images/allen/Ei_Compendex(150x39)1.png" /> <img style="width: 136px; height: 38px;" src="https://ojs.imeti.org/public/site/images/allen/image001.png" alt="" width="171" height="38" /> <img style="width: 136px; height: 38px;" src="https://ojs.imeti.org/public/site/images/ijeti/EBSCO-1.png" width="170" height="35" /> <img style="width: 136px; height: 38px;" src="https://ojs.imeti.org/public/site/images/ijeti/image0031.jpg" width="116" height="38" /> <img style="font-family: 'Noto Sans'; font-size: 10.5pt;" src="https://ojs.imeti.org/public/site/images/ijeti/DOAJ4.png" alt="" /></span></span></p> <p><img style="font-family: 'Noto Sans'; font-size: 10.5pt;" src="https://ojs.imeti.org/public/site/images/ijeti/google5.png" alt="" /><img src="https://ojs.imeti.org/public/site/images/ijeti/CNKI.png" alt="" /> <img src="https://ojs.imeti.org/public/site/images/allen/ProQuest-41.png" /> <img src="https://ojs.imeti.org/public/site/images/ijeti/Resarch_Bible5.png" alt="" /> <img src="https://ojs.imeti.org/public/site/images/ijeti/WorldCat5.png" alt="" width="118" height="40" /> <img src="https://ojs.imeti.org/public/site/images/allen/academia-12.png" width="136" height="27" /> <img src="https://ojs.imeti.org/public/site/images/ijeti/TOCs5.jpg" alt="" /> <img src="https://ojs.imeti.org/public/site/images/allen/Publons-22.5_1.png" /> <img src="https://ojs.imeti.org/public/site/images/allen/crossref3.png" width="92" height="42" /></p> <p> </p> <p style="margin: 0cm 0cm 0pt;"><span style="color: black; font-family: 'Noto Sans'; font-size: 10.5pt;">Under evaluation of SCI.</span></p> <p style="margin: 0cm 0cm 0pt;"> </p>https://ojs.imeti.org/index.php/IJETI/article/view/12033Simulation and Measurement Analysis of an Integrated Flow Battery Energy-Storage System with Hybrid Wind/Wave Power Generation2023-07-20T15:40:56+08:00Li Wangliwangncku@gmail.comShih-Chia Linlinyijun880407@gmail.comSheng-Jie Zhangqaz871220@gmail.comChing-Chung Tsengliwangncku@gmail.comHung-Hsien KuHHKu@iner.gov.twChin-Lung Hsiehclhsieh@iner.gov.tw<p>This study aims to evaluate the power-system stability and the mitigation of fluctuations in a hybrid wind/wave power-generation system (HWWPGS) under different operating and disturbance conditions. This evaluation is performed by employing a vanadium redox flow battery-based energy storage system (VRFB-ESS) as proposed. The measurement results obtained from a laboratory-scale HWWPGS platform integrated with the VRFB-ESS, operating under specific conditions, are used to develop the laboratory-scale simulation model. The capacity rating of this laboratory-scale simulation model is then enlarged to develop an MW-scale power-system model of the HWWPGS. Both operating characteristics and power-system stability of the MW-scale HWWPGS power system model are evaluated through frequency-domain analysis (based on eigenvalue) and time-domain analysis (based on nonlinear-model simulations) under various operating conditions and disturbance conditions. The simulation results demonstrate that the fluctuations and stability of the studied HWWPGS under different operating and disturbance conditions can be effectively smoothed and stabilized by the proposed VRFB-ESS.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Li Wang, Shih-Chia Lin, Sheng-Jie Zhang, Ching-Chung Tseng, Hung-Hsien Ku, Chin-Lung Hsiehhttps://ojs.imeti.org/index.php/IJETI/article/view/12375Preparation and Characterization of Carrot Nanocellulose and Ethylene/Vinyl Acetate Copolymer-Based Green Composites2023-06-04T22:17:55+08:00Yu-Cian Kejackalen369@gmail.comYing-Chieh Chaorockway5718@yahoo.com.twChun-Wei Changadad94033@gmail.comYeng-Fong Shihsyf@cyut.edu.tw<p>This study aims to investigate the effect of nanocellulose on the properties and physical foaming of ethylene/vinyl acetate (EVA) copolymer. The nanocellulose is prepared from waste carrot residue using the 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidation method (CT) and is further modified through suspension polymerization of methyl methacrylate (MMA) monomer (CM). The obtained nanocellulose samples (CT or CM) are added to EVA to create a series of nanocomposites. Moreover, the EVA and CM/EVA composite were further foamed using supercritical carbon dioxide physical foaming. TEM results show that the average diameters of CT and CM are 24.35 ± 3.15 nm and 30.45 ± 1.86 nm, respectively. The analysis of mechanical properties demonstrated that the tensile strength of pure EVA increased from 10.02 MPa to 13.01 MPa with the addition of only 0.2 wt% of CM. Furthermore, the addition of CM to EVA enhanced the melt strength of the polymer, leading to improvements in the physical foaming properties of the material. The results demonstrate that the pore size of the CM/EVA foam material is smaller than that of pure EVA foam. Additionally, the cell density of the CM/EVA foam material can reach 3.23 × 10<sup>11</sup> cells/cm<sup>3</sup>.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Yu-Cian Ke, Ying-Chieh Chao, Chun-Wei Chang, Yeng-Fong Shihhttps://ojs.imeti.org/index.php/IJETI/article/view/11837A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction2023-05-08T14:44:43+08:00Ajay Kumarajay.kumar.it@kiet.eduKamaldeep Kaurkdkau.99@gmail.com<p>Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multi-criteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Ajay Kumar, Kamaldeep Kaurhttps://ojs.imeti.org/index.php/IJETI/article/view/12675Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission2023-08-03T13:36:49+08:00Yi-Ming Zhang3318832253@qq.comJia Xuy17787199084@163.comYi-Tao Zhao172941665@qq.comQing-Chan Liuzhizhe-520@163.comQiu-Hao Gong2638154685@qq.com<p>This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Yi-Ming Zhang, Jia Xu, Yi-Tao Zhao, Qing-Chan Liu, Qiu-Hao Gonghttps://ojs.imeti.org/index.php/IJETI/article/view/13230Finite Element Analysis of a Novel Tensegrity-Based Vibratory Platform2023-12-26T00:21:37+08:00Wen-Hsiang Hsiehijeti.eic@gmail.comChen-Ji Panallen@nfu.edu.twYen-Chun Hsiehallen@nfu.edu.tw<p>The study aims to conduct the finite element analysis (FEA) of a novel tensegrity-based vibratory platform by using IronCAD software. and analyze its deformation under external forces to verify if the platform can generate the required advancing motion. Firstly, the structure and operating principles of the proposed platform are introduced. Subsequently, individual parts are created using IronCAD software and assembled to form a solid model of the entire platform. Finally, employing Multiphysics for IronCAD, FEA is conducted to analyze the platform’s displacement under different external forces, as well as to examine its natural frequencies and mode shapes. The simulation results indicate that the proposed platform effectively moves a part in a specified direction. Additionally, the maximum stress remains below the yield strength. Moreover, the mode shapes corresponding to the initial 3 natural frequencies contribute to the advancing motion.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Wen-Hsiang Hsieh, Chen-Ji Pan, Yen-Chun Hsiehhttps://ojs.imeti.org/index.php/IJETI/article/view/12612A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm2023-07-18T15:33:24+08:00Xiao-Yun Jiangjxycom@163.comWen-Chao Chen758617064@qq.comYu-Tong Liu15659440146@163.com<p>The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Xiao-Yun Jiang, Wen-Chao Chen, Yu-Tong Liuhttps://ojs.imeti.org/index.php/IJETI/article/view/12294Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds2023-05-24T21:28:34+08:00Sangeetha Balasubramaniansangeetha27may@gmail.comPeriyasamy Rajaduraiperiyasamyr@nitt.edu<p> The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Sangeetha Balasubramanian, Periyasamy Rajaduraihttps://ojs.imeti.org/index.php/IJETI/article/view/12869Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning2023-09-18T22:49:49+08:00Jian-Yu Ren871050568@qq.comJian-Wei Zhao202214506235@stu.kust.edu.cnNan Pannanpan@kust.edu.cnNuo-Bin Zhangzhangnuobin@stu.kust.edu.cnJun-Wei Yangyangjunwei@longshine.com<p>The distribution network line loss rate is a crucial factor in improving the economic efficiency of power grids. However, the traditional prediction model has low accuracy. This study proposes a predictive method based on data preprocessing and model integration to improve accuracy. Data preprocessing employs dynamic cleaning technology with machine learning to enhance data quality. Model integration combines long short-term memory (LSTM), linear regression, and extreme gradient boosting (XGBoost) models to achieve multi-angle modeling. This study employs regression evaluation metrics to assess the difference between predicted and actual results for model evaluation. Experimental results show that this method leads to improvements over other models. For example, compared to LSTM, root mean square error (RMSE) was reduced by 44.0% and mean absolute error (MAE) by 23.8%. The method provides technical solutions for building accurate line loss monitoring systems and enhances power grid operations.</p>2024-01-01T00:00:00+08:00Copyright (c) 2023 Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang