Pub Date : 2023-12-29DOI: 10.46604/ijeti.2023.12869
Jian-Yu Ren, Jian-Wei Zhao, Nan Pan, Nuo-Bin Zhang, Jun-Wei Yang
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-12-29DOI: 10.46604/ijeti.2023.12612
Xiao-Yun Jiang, Wen-Chao Chen, Yu-Tong Liu
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-12-29DOI: 10.46604/ijeti.2023.13230
Wen-Hsiang Hsieh, Chen-Ji Pan, Yen-Chun Hsieh
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.
{"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":null,"pages":null},"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}
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-11-02DOI: 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
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.
{"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":null,"pages":null},"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}
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.
该研究提出了一种基于计算机的自动化系统，该系统采用机器学习技术，利用从医院收集的肺声数据对肺部疾病进行分类。应用离散小波变换和变分模态分解等降噪技术来提高分类器的性能。该系统结合了Mel-frequency倒谱系数和gamma - one -frequency倒谱系数等倒谱特征进行分类。比较了四种机器学习分类器，即决策树、k近邻、线性判别分析和随机森林。评估指标如准确性、召回率、特异性和f1评分被采用。本研究包括慢性阻塞性肺疾病、哮喘、支气管扩张患者和健康个体。结果表明，随机森林分类器优于其他分类器，达到99.72%的准确率以及100%的召回率，特异性和f1分数。该研究表明，基于计算机的系统可作为肺部疾病分类的决策工具，特别是在资源有限的环境中。
{"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":null,"pages":null},"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}
Pub Date : 2023-09-28DOI: 10.46604/ijeti.2023.11158
None Chuan-Pin Lu, None Yan-Long Huang, None Po-Jen Lai
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-09-28DOI: 10.46604/ijeti.2023.11444
None Nuraiza Ismail, None Ermeey Abd Kadir
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%.
{"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":null,"pages":null},"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}
Pub Date : 2023-09-28DOI: 10.46604/ijeti.2023.11552
None Siron Anita Susan T, None Nithya Balasubramanian
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-09-28DOI: 10.46604/ijeti.2023.11936
None Muhammad Syamsul Imran Zaini, None Muzamir Hasan
The study aims to investigate the properties of kaolinitic clay using silica fume, eggshell ash, and lime. The experiment employs varying amounts of silica fume (2%, 4%, and 6%), eggshell ash, lime (3%, 6%, and 9%), and combinations of silica fume, eggshell ash, and lime, which are cured for 1, 7, 14, and 30 days. The investigated properties of the soils include the improvement of Atterberg limits, maximum dry density (MDD), optimum moisture content (OMC), specific gravity, compressive strength, morphology characteristics, and chemical compositions. The results reveal that the optimal application of these materials can be achieved at 6% silica fume, 6% eggshell ash, and 9% lime mixture into the soils and increase the shear strength by as much as 88.74% at 30 days of curing.
{"title":"Effectiveness of Silica Fume Eggshell Ash and Lime Use on the Properties of Kaolinitic Clay","authors":"None Muhammad Syamsul Imran Zaini, None Muzamir Hasan","doi":"10.46604/ijeti.2023.11936","DOIUrl":"https://doi.org/10.46604/ijeti.2023.11936","url":null,"abstract":"The study aims to investigate the properties of kaolinitic clay using silica fume, eggshell ash, and lime. The experiment employs varying amounts of silica fume (2%, 4%, and 6%), eggshell ash, lime (3%, 6%, and 9%), and combinations of silica fume, eggshell ash, and lime, which are cured for 1, 7, 14, and 30 days. The investigated properties of the soils include the improvement of Atterberg limits, maximum dry density (MDD), optimum moisture content (OMC), specific gravity, compressive strength, morphology characteristics, and chemical compositions. The results reveal that the optimal application of these materials can be achieved at 6% silica fume, 6% eggshell ash, and 9% lime mixture into the soils and increase the shear strength by as much as 88.74% at 30 days of curing.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135387313","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}
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