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Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission 优化用于电能计量数据传输的 SM4 加密算法
IF 1
International Journal of Engineering and Technology Innovation Pub Date : 2023-12-29 DOI: 10.46604/ijeti.2023.12675
Yen-Chun Hsieh, Yi-Ming Zhang, Jia Xu, Yi-Tao Zhao, Qing-Chan Liu, Qiu-Hao Gong
{"title":"Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission","authors":"Yen-Chun Hsieh, Yi-Ming Zhang, Jia Xu, Yi-Tao Zhao, Qing-Chan Liu, Qiu-Hao Gong","doi":"10.46604/ijeti.2023.12675","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12675","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" 9","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning 基于集合学习的配电网线路损耗率预测
IF 1
International Journal of Engineering and Technology Innovation Pub Date : 2023-12-29 DOI: 10.46604/ijeti.2023.12869
Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang
{"title":"Prediction of Distribution Network Line Loss Rate Based on Ensemble Learning","authors":"Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang","doi":"10.46604/ijeti.2023.12869","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12869","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" 3","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm 基于改进遗传算法的考虑不可行路线的车辆选线问题研究
IF 1
International Journal of Engineering and Technology Innovation Pub Date : 2023-12-29 DOI: 10.46604/ijeti.2023.12612
Xiao-Yun Jiang, Wen-Chao Chen, Yu-Tong Liu
{"title":"A Study on the Vehicle Routing Problem Considering Infeasible Routing Based on the Improved Genetic Algorithm","authors":"Xiao-Yun Jiang, Wen-Chao Chen, Yu-Tong Liu","doi":"10.46604/ijeti.2023.12612","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12612","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":" 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Finite Element Analysis of a Novel Tensegrity-Based Vibratory Platform 新型张弦振动平台的有限元分析
IF 1
International Journal of Engineering and Technology Innovation Pub Date : 2023-12-29 DOI: 10.46604/ijeti.2023.13230
Wen-Hsiang Hsieh, Chen-Ji Pan, Yen-Chun Hsieh
{"title":"Finite Element Analysis of a Novel Tensegrity-Based Vibratory Platform","authors":"Wen-Hsiang Hsieh, Chen-Ji Pan, Yen-Chun Hsieh","doi":"10.46604/ijeti.2023.13230","DOIUrl":"https://doi.org/10.46604/ijeti.2023.13230","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"111 4","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Preparation and Characterization of Carrot Nanocellulose and Ethylene/Vinyl Acetate Copolymer-Based Green Composites 胡萝卜纳米纤维素与乙烯/醋酸乙烯共聚物基绿色复合材料的制备与表征
International Journal of Engineering and Technology Innovation Pub Date : 2023-11-02 DOI: 10.46604/ijeti.2023.12375
None Yu-Cian Ke, None Ying-Chieh Chao, None Chun-Wei Chang, None Yeng-Fong Shih
{"title":"Preparation and Characterization of Carrot Nanocellulose and Ethylene/Vinyl Acetate Copolymer-Based Green Composites","authors":"None Yu-Cian Ke, None Ying-Chieh Chao, None Chun-Wei Chang, None Yeng-Fong Shih","doi":"10.46604/ijeti.2023.12375","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12375","url":null,"abstract":"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 × 1011 cells/cm3.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"16 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Simulation and Measurement Analysis of an Integrated Flow Battery Energy-Storage System with Hybrid Wind/Wave Power Generation 风波混合发电一体化液流电池储能系统仿真与测量分析
International Journal of Engineering and Technology Innovation Pub Date : 2023-11-02 DOI: 10.46604/ijeti.2023.12033
None Li Wang, None Shih-Chia Lin, None Sheng-Jie Zhang, None Ching-Chung Tseng, None Hung-Hsien Ku, None Chin-Lung Hsieh
{"title":"Simulation and Measurement Analysis of an Integrated Flow Battery Energy-Storage System with Hybrid Wind/Wave Power Generation","authors":"None Li Wang, None Shih-Chia Lin, None Sheng-Jie Zhang, None Ching-Chung Tseng, None Hung-Hsien Ku, None Chin-Lung Hsieh","doi":"10.46604/ijeti.2023.12033","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12033","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135934113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds 基于机器学习的肺部疾病实时肺音分类
International Journal of Engineering and Technology Innovation Pub Date : 2023-10-20 DOI: 10.46604/ijeti.2023.12294
None Sangeetha Balasubramanian, None Periyasamy Rajadurai
{"title":"Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds","authors":"None Sangeetha Balasubramanian, None Periyasamy Rajadurai","doi":"10.46604/ijeti.2023.12294","DOIUrl":"https://doi.org/10.46604/ijeti.2023.12294","url":null,"abstract":"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.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135665530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing 基于深度学习边缘计算的捻纱异常张力模式识别模块的开发
International Journal of Engineering and Technology Innovation Pub Date : 2023-09-28 DOI: 10.46604/ijeti.2023.11158
None Chuan-Pin Lu, None Yan-Long Huang, None Po-Jen Lai
{"title":"Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing","authors":"None Chuan-Pin Lu, None Yan-Long Huang, None Po-Jen Lai","doi":"10.46604/ijeti.2023.11158","DOIUrl":"https://doi.org/10.46604/ijeti.2023.11158","url":null,"abstract":"This study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications 一种用于环境Wi-Fi射频功率传输和能量收集的共面波导超宽带天线
International Journal of Engineering and Technology Innovation Pub Date : 2023-09-28 DOI: 10.46604/ijeti.2023.11444
None Nuraiza Ismail, None Ermeey Abd Kadir
{"title":"A Co-Planar Waveguide Ultra-Wideband Antenna for Ambient Wi-Fi RF Power Transmission and Energy Harvesting Applications","authors":"None Nuraiza Ismail, None Ermeey Abd Kadir","doi":"10.46604/ijeti.2023.11444","DOIUrl":"https://doi.org/10.46604/ijeti.2023.11444","url":null,"abstract":"This study proposes an ultra-wideband antenna for ambient radio frequency (RF) energy harvesting applications. The antenna is based on a co-planar waveguide (CPW) transmission line and incorporates a rectangular slot as an antenna harvester. The proposed antenna utilizes an evolutionary design process to achieve impedance matching of the 50 Ω CPW feeding line over the desired frequency bands. A parametric study investigates CPW elements and rectangular slot size. The harvester antenna is then connected to the primary rectifier circuit of the voltage doubler to examine the signal characteristics. The antenna covers the Industry, Science, and Medicine (ISM) Wi-Fi bands of 2.45 GHz and 5 GHz, achieving a realized gain of 3.641 dBi and 4.644 dBi at 2.45 GHz and 5 GHz, respectively. It exhibits a relatively broad frequency ranging from 2.16 GHz to 6.32 GHz, covering the ultra-wideband fractional bandwidth (FBW) of 105%.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135386355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network 无线可充电传感器网络中停车点选择的混合元启发式算法
International Journal of Engineering and Technology Innovation Pub Date : 2023-09-28 DOI: 10.46604/ijeti.2023.11552
None Siron Anita Susan T, None Nithya Balasubramanian
{"title":"A Hybrid Metaheuristic Algorithm for Stop Point Selection in Wireless Rechargeable Sensor Network","authors":"None Siron Anita Susan T, None Nithya Balasubramanian","doi":"10.46604/ijeti.2023.11552","DOIUrl":"https://doi.org/10.46604/ijeti.2023.11552","url":null,"abstract":"A wireless rechargeable sensor network (WRSN) enables charging of rechargeable sensor nodes (RSN) wirelessly through a mobile charging vehicle (MCV). Most existing works choose the MCV’s stop point (SP) at random, the cluster’s center, or the cluster head position, all without exploring the demand from RSNs. It results in a long charging delay, a low charging throughput, frequent MCV trips, and more dead nodes. To overcome these issues, this paper proposes a hybrid metaheuristic algorithm for stop point selection (HMA-SPS) that combines the techniques of the dragonfly algorithm (DA), firefly algorithm (FA), and gray wolf optimization (GWO) algorithms. Using FA and GWO techniques, DA predicts an ideal SP using the run-time metrics of RSNs, such as energy, delay, distance, and trust factors. The simulated results demonstrate faster convergence with low delay and highlight that more RSNs can be recharged with fewer MCV visits, further enhancing energy utilization, throughput, network lifetime, and trust factor.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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