Pub Date : 2021-01-20DOI: 10.46604/IJETI.2021.6126
Elsa M. Cárdenas, L. Medina
The objective of this research is to present a systematic review of the non-parametric modal analysis methods in the frequency domain. Peak picking (PP), frequency domain decomposition (FDD), enhanced frequency domain decomposition (EFDD), and frequency–spatial domain decomposition (FSDD) are revisited and didactically illustrated by means of modal identification for a study case proposed in previous researches. Algorithm schemes are illustrated to summarize these frequency domain OMA techniques. Modal frequencies, modal damping ratios, and modal shapes are estimated using the different OMA techniques and compared to estimations obtained by the free decay (FD) method reported in previous researches. These are employed to compare the results obtained by the methods presented herein and show a very good correlation in obtaining modal frequencies and a low correlation in the case of modal damping.
{"title":"Non-Parametric Operational Modal Analysis Methods in Frequency Domain: A Systematic Review","authors":"Elsa M. Cárdenas, L. Medina","doi":"10.46604/IJETI.2021.6126","DOIUrl":"https://doi.org/10.46604/IJETI.2021.6126","url":null,"abstract":"The objective of this research is to present a systematic review of the non-parametric modal analysis methods in the frequency domain. Peak picking (PP), frequency domain decomposition (FDD), enhanced frequency domain decomposition (EFDD), and frequency–spatial domain decomposition (FSDD) are revisited and didactically illustrated by means of modal identification for a study case proposed in previous researches. Algorithm schemes are illustrated to summarize these frequency domain OMA techniques. Modal frequencies, modal damping ratios, and modal shapes are estimated using the different OMA techniques and compared to estimations obtained by the free decay (FD) method reported in previous researches. These are employed to compare the results obtained by the methods presented herein and show a very good correlation in obtaining modal frequencies and a low correlation in the case of modal damping.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48049660","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 : 2021-01-01DOI: 10.46604/ijeti.2021.7623
Ziaaddin Zamanzadeh, Farzin Hosseinzadeh, M. Bashiri
The effectiveness of a strengthening technique devised for the concrete beams subjected to bending is presented in this study, where recycled-steel fiber-reinforced mortar (RSFRM) panels are used as an eco-friendly replacement for ordinary steel fibers. Different mix designs for RSFRM are first investigated experimentally by testing 160 × 400 × 400 mm3 notched beam-like specimens in 3-point bending, while 100 × 100 × 100 mm3 cubes are tested in compression, to optimize the mix design. Finite element (FE) analyses are carried out on strengthened and non-strengthened beams to investigate the effectiveness of the proposed strengthening technique based on RSFRM panels. Starting from the tests on notched beams, an inverse FE analysis is used to optimize the RSFRM’s parameters to be implemented into the numerical model. The results show that applying RSFRM panels not only markedly increases the load-bearing capacity of the beams (up to 3.19 times with 3% of fibers by volume), but also changes their fracture mechanism from brittle to ductile fracture.
{"title":"Cement-Based Mortar Panels Reinforced with Recycled Steel Fibers in Flexural Strengthening of Concrete Beams","authors":"Ziaaddin Zamanzadeh, Farzin Hosseinzadeh, M. Bashiri","doi":"10.46604/ijeti.2021.7623","DOIUrl":"https://doi.org/10.46604/ijeti.2021.7623","url":null,"abstract":"The effectiveness of a strengthening technique devised for the concrete beams subjected to bending is presented in this study, where recycled-steel fiber-reinforced mortar (RSFRM) panels are used as an eco-friendly replacement for ordinary steel fibers. Different mix designs for RSFRM are first investigated experimentally by testing 160 × 400 × 400 mm3 notched beam-like specimens in 3-point bending, while 100 × 100 × 100 mm3 cubes are tested in compression, to optimize the mix design. Finite element (FE) analyses are carried out on strengthened and non-strengthened beams to investigate the effectiveness of the proposed strengthening technique based on RSFRM panels. Starting from the tests on notched beams, an inverse FE analysis is used to optimize the RSFRM’s parameters to be implemented into the numerical model. The results show that applying RSFRM panels not only markedly increases the load-bearing capacity of the beams (up to 3.19 times with 3% of fibers by volume), but also changes their fracture mechanism from brittle to ductile fracture.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70564891","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 : 2021-01-01DOI: 10.46604/ijeti.2021.7060
Amir Ebrahim Akbari Baghal, A. Maleki, R. Vafaei
This study presents a three-dimensional non-linear finite element investigation on the pull-out behavior of straight and hooked-end Shape Memory Alloys (SMA) and steel fibers embedded in Ultra-High Performance Concrete (UHPC) using a single fiber pull-out model. A bilinear cohesive zone model is used to characterize the interfacial traction separation relationships. The Concrete Damage Plasticity (CDP) model is used to simulate UHPC, and the mechanical behavior is obtained through experimental tests. Parametric studies are conducted to evaluate the effects of fiber materials, fiber diameters, and hook angles on the load-displacement behavior. A good agreement between the numerical and experimental results is obtained. It is found that the hooked-end fibers with a smaller diameter and a hook angle of 40° can be a better choice for structural application. Furthermore, it is observed that the use of SMA fibers significantly improves the pull-out performance between fibers and UHPC.
{"title":"On the Pull-out Behavior of Hooked-End Shape Memory Alloys Fibers Embedded in Ultra-High Performance Concrete","authors":"Amir Ebrahim Akbari Baghal, A. Maleki, R. Vafaei","doi":"10.46604/ijeti.2021.7060","DOIUrl":"https://doi.org/10.46604/ijeti.2021.7060","url":null,"abstract":"This study presents a three-dimensional non-linear finite element investigation on the pull-out behavior of straight and hooked-end Shape Memory Alloys (SMA) and steel fibers embedded in Ultra-High Performance Concrete (UHPC) using a single fiber pull-out model. A bilinear cohesive zone model is used to characterize the interfacial traction separation relationships. The Concrete Damage Plasticity (CDP) model is used to simulate UHPC, and the mechanical behavior is obtained through experimental tests. Parametric studies are conducted to evaluate the effects of fiber materials, fiber diameters, and hook angles on the load-displacement behavior. A good agreement between the numerical and experimental results is obtained. It is found that the hooked-end fibers with a smaller diameter and a hook angle of 40° can be a better choice for structural application. Furthermore, it is observed that the use of SMA fibers significantly improves the pull-out performance between fibers and UHPC.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70564995","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 : 2021-01-01DOI: 10.46604/ijeti.2021.7086
G. Norouznejad, I. Shooshpasha, S. Mirhosseini, M. Afzalirad
This research investigates the impact of zeolite on the compaction properties and California Bearing Ratio (CBR) of cemented sand. For this purpose, firstly, sand, cement (2, 4, 6, and 8% by the sand dry weight), and zeolite (0%, 30%, 60%, and 90% of cement content, as a replacement material) are mixed. Then, various cylindrical samples with sizes of 101×116 mm and 119×152 mm are prepared for compaction and CBR tests, respectively. After curing for 28 days, the samples are tested according to the standards of compaction and CBR tests. The results depict that the use of zeolite reduces Maximum Dry Density (MDD) while it increases Optimum Moisture Content (OMC) of cemented sand. Furthermore, the inclusion of zeolite up to 30% of cement content contributes to the highest CBR values due to the pozzolanic and chemical reactions. Finally, some correlations with high correlation coefficients are proposed between the CBR and MDD of zeolite-cemented sand.
{"title":"Effect of Zeolite on the Compaction Properties and California Bearing Ratio (CBR) of Cemented Sand","authors":"G. Norouznejad, I. Shooshpasha, S. Mirhosseini, M. Afzalirad","doi":"10.46604/ijeti.2021.7086","DOIUrl":"https://doi.org/10.46604/ijeti.2021.7086","url":null,"abstract":"This research investigates the impact of zeolite on the compaction properties and California Bearing Ratio (CBR) of cemented sand. For this purpose, firstly, sand, cement (2, 4, 6, and 8% by the sand dry weight), and zeolite (0%, 30%, 60%, and 90% of cement content, as a replacement material) are mixed. Then, various cylindrical samples with sizes of 101×116 mm and 119×152 mm are prepared for compaction and CBR tests, respectively. After curing for 28 days, the samples are tested according to the standards of compaction and CBR tests. The results depict that the use of zeolite reduces Maximum Dry Density (MDD) while it increases Optimum Moisture Content (OMC) of cemented sand. Furthermore, the inclusion of zeolite up to 30% of cement content contributes to the highest CBR values due to the pozzolanic and chemical reactions. Finally, some correlations with high correlation coefficients are proposed between the CBR and MDD of zeolite-cemented sand.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70565170","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 : 2020-09-29DOI: 10.46604/IJETI.2020.4951
M. Yunus, M. S. Alsoufi
Implementing non-conventional finishing methods in the aircraft industry by the abrasive flow machining (AFM) process depends on the production quality at optimal conditions. The optimal set of the process variables in metal-matrix-composite (MMC) for a varying reinforcement percentage removes the obstructions and errors in the AFM process. In order to achieve this objective, the resultant output functions of the overall process using every clustering level of variables in a model are configured by using genetic programming (GP). These functions forecast the data to vary the percent of silicon carbide particles (SiCp) particles without experimentation obtaining the output functions for material removing rates and surface roughness changes of Al-MMCs machined with the AFM process by using GP. The obtained genetic optimal global models are simulated and, the results show a higher degree of accuracy up to 99.97% as compared to the other modeling techniques.
{"title":"Genetic Based Experimental Investigation on Finishing Characteristics of AlSiCp-MMC by Abrasive Flow Machining","authors":"M. Yunus, M. S. Alsoufi","doi":"10.46604/IJETI.2020.4951","DOIUrl":"https://doi.org/10.46604/IJETI.2020.4951","url":null,"abstract":"Implementing non-conventional finishing methods in the aircraft industry by the abrasive flow machining (AFM) process depends on the production quality at optimal conditions. The optimal set of the process variables in metal-matrix-composite (MMC) for a varying reinforcement percentage removes the obstructions and errors in the AFM process. In order to achieve this objective, the resultant output functions of the overall process using every clustering level of variables in a model are configured by using genetic programming (GP). These functions forecast the data to vary the percent of silicon carbide particles (SiCp) particles without experimentation obtaining the output functions for material removing rates and surface roughness changes of Al-MMCs machined with the AFM process by using GP. The obtained genetic optimal global models are simulated and, the results show a higher degree of accuracy up to 99.97% as compared to the other modeling techniques.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48554926","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 : 2020-09-29DOI: 10.14569/ijacsa.2020.0110109
A. P. Delima
The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.
{"title":"An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization","authors":"A. P. Delima","doi":"10.14569/ijacsa.2020.0110109","DOIUrl":"https://doi.org/10.14569/ijacsa.2020.0110109","url":null,"abstract":"The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. \u0000The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. \u0000Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82765372","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 : 2020-09-29DOI: 10.46604/IJETI.2020.6262
Ming‐Che Lee
This research proposes a novel method to investigate the performance of the S21 detection circuit. Aiming at low frequencies or DC, the method serves as an efficient way of verification and enjoys the benefit of low testing costs. The novel investigation method is demonstrated at 50 MHz and verified by the scattering parameters at 11.05 GHz. Based on the investigation, a model of process variations is constructed. The length of the interface paths is estimated by the model to be 63μm, which is consistent with the corresponding length of 74.6μm in the layout. For the measured phase and magnitude, the model indicates that the process variations in the device under test cause errors of 18.91% and 1.27%, whereas those in the interface paths lead to errors of 1.83% and 1%. Based on the model, practical recommendations are also proposed to further improve the measurement precision in the future.
{"title":"A Novel Investigation Method for the S21 Detection Circuit","authors":"Ming‐Che Lee","doi":"10.46604/IJETI.2020.6262","DOIUrl":"https://doi.org/10.46604/IJETI.2020.6262","url":null,"abstract":"This research proposes a novel method to investigate the performance of the S21 detection circuit. Aiming at low frequencies or DC, the method serves as an efficient way of verification and enjoys the benefit of low testing costs. The novel investigation method is demonstrated at 50 MHz and verified by the scattering parameters at 11.05 GHz. Based on the investigation, a model of process variations is constructed. The length of the interface paths is estimated by the model to be 63μm, which is consistent with the corresponding length of 74.6μm in the layout. For the measured phase and magnitude, the model indicates that the process variations in the device under test cause errors of 18.91% and 1.27%, whereas those in the interface paths lead to errors of 1.83% and 1%. Based on the model, practical recommendations are also proposed to further improve the measurement precision in the future.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48078835","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 : 2020-09-29DOI: 10.46604/IJETI.2020.5870
A. Farhadi, A. Akbari, A. Zakerian, M. Bina
In this paper, an improved model predictive control method is proposed to drive an induction motor fed by a three-level matrix converter. The main objective of this paper is to present a novel method to increase the switching frequency at a constant sampling time. Also, it is analytically discussed that increasing the switching frequency not only can decrease the motor current ripples, but it can also significantly reduce its torque ripples. Finally, this study demonstrates that reducing the motor current ripple will improve the quality of the supply current. To be the accurate model and validate the motor drive system, a co-simulation method, which is a combination of FLUX and MATLAB software packages, is employed to find the simulation results. The findings indicate that the proposed method diminishes the THD of the supply current up to 26% approximately. Furthermore, increasing the switching frequency results in the torque ripple reduction by up to 10% almost.
{"title":"An Improved Model Predictive Control Method to Drive an Induction Motor Fed by Three-Level Diode-Clamped Indirect Matrix Converter","authors":"A. Farhadi, A. Akbari, A. Zakerian, M. Bina","doi":"10.46604/IJETI.2020.5870","DOIUrl":"https://doi.org/10.46604/IJETI.2020.5870","url":null,"abstract":"In this paper, an improved model predictive control method is proposed to drive an induction motor fed by a three-level matrix converter. The main objective of this paper is to present a novel method to increase the switching frequency at a constant sampling time. Also, it is analytically discussed that increasing the switching frequency not only can decrease the motor current ripples, but it can also significantly reduce its torque ripples. Finally, this study demonstrates that reducing the motor current ripple will improve the quality of the supply current. To be the accurate model and validate the motor drive system, a co-simulation method, which is a combination of FLUX and MATLAB software packages, is employed to find the simulation results. The findings indicate that the proposed method diminishes the THD of the supply current up to 26% approximately. Furthermore, increasing the switching frequency results in the torque ripple reduction by up to 10% almost.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42826471","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 : 2020-09-29DOI: 10.46604/ijeti.2020.4646
Allemar Jhone P. Delima
The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.
{"title":"An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization","authors":"Allemar Jhone P. Delima","doi":"10.46604/ijeti.2020.4646","DOIUrl":"https://doi.org/10.46604/ijeti.2020.4646","url":null,"abstract":"The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. \u0000The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. \u0000Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44843992","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 : 2020-09-29DOI: 10.46604/IJETI.2020.5447
V. Sakthivel, M. Suman, P. Sathya
The multi-area economic load dispatch (MAELD) can reduce running costs through making the areas with more cost-effective units produce more energy. The excess power is transferred to the areas with expensive units. This paper contributes a new physics inspired metaheuristic approach called the Coulomb’s and Franklin’s laws based optimizer (CFLBO) to solve the MAELD problem. The CFLBO approach is developed from Coulomb’s and Franklin’s theories, which comprise attraction/repulsion, probabilistic ionization, and contact stages. The effectiveness of the envisaged CFLBO approach has been examined on three standard test systems with various areas. Results obtained by the CFLBO approach are compared with the exchange market algorithm (EMA) and the existing state-of-the-art approaches to deal with MAELD. Numerical outcomes show the benefits of the quick convergence and better quality of the suggested approach compared to existing strategies. Consequently, the proposed approach is a helpful tool for generation planning in MAELD problems.
{"title":"Robust Multi-Area Economic Dispatch Using Coulomb’s and Franklin’s Laws Based Optimizer","authors":"V. Sakthivel, M. Suman, P. Sathya","doi":"10.46604/IJETI.2020.5447","DOIUrl":"https://doi.org/10.46604/IJETI.2020.5447","url":null,"abstract":"The multi-area economic load dispatch (MAELD) can reduce running costs through making the areas with more cost-effective units produce more energy. The excess power is transferred to the areas with expensive units. This paper contributes a new physics inspired metaheuristic approach called the Coulomb’s and Franklin’s laws based optimizer (CFLBO) to solve the MAELD problem. The CFLBO approach is developed from Coulomb’s and Franklin’s theories, which comprise attraction/repulsion, probabilistic ionization, and contact stages. The effectiveness of the envisaged CFLBO approach has been examined on three standard test systems with various areas. Results obtained by the CFLBO approach are compared with the exchange market algorithm (EMA) and the existing state-of-the-art approaches to deal with MAELD. Numerical outcomes show the benefits of the quick convergence and better quality of the suggested approach compared to existing strategies. Consequently, the proposed approach is a helpful tool for generation planning in MAELD problems.","PeriodicalId":43808,"journal":{"name":"International Journal of Engineering and Technology Innovation","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42881946","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：