Pub Date : 2023-10-30DOI: 10.35940/ijeat.a4282.1013123
Lalithendra Chowdari Mandava
Effective change management emerges as a deciding element for an organization's survival and success in the changing terrain of today's fiercely competitive business climate. The variety of change management theories and approaches that are currently available, however, paints a complicated picture that is plagued by inconsistencies, a lack of strong empirical support, and unproven assumptions about contemporary organizational dynamics. This essay seeks to set the basis for a fresh paradigm for effective change administration by critically analyzing popular change management ideas. The gap between theory and practice is addressed in the paper, which concludes with suggestions for more research. In parallel, artificial intelligence (AI) has made incredible progress, giving rise to computers that mimic human autonomy and cognition. Industry-wide excitement has been sparked by the enthusiasm among academics, executives, and the general public, which has resulted in significant investments in utilizing AI's potential through creative business models. However, the lack of thorough academic guidance forces managers to struggle with AI integration issues, increasing the risk of project failure. An in-depth analysis of AI's complexities and its function as a spark for revolutionary business model innovation is provided in this article. A thorough literature assessment, which involves sifting through a sizable library of published works, combines up-to-date information on how AI is affecting the development of new business models. The findings come together to form a roadmap for seamless AI integration that includes four steps: understanding the fundamentals of AI and the skills needed for digital transformation, understanding current business models and their innovation potential, nurturing key proficiencies for AI assimilation, and gaining organizational acceptance while developing internal competencies. This article combines the fields of organizational change management and AI-driven business model innovation with ease, providing a thorough explanation to assist businesses in undergoing a successful transformation and innovation. These disciplines' confluence offers a practical vantage point for successfully adapting to, thriving in, and profiting within a dynamic business environment. Artificial intelligence (AI), a massively disruptive force that is altering international businesses, is at the vanguard of this revolution. The ability of AI to make decisions automatically, based on data analysis and observation, opens up hitherto untapped possibilities for value creation and competitive dominance, with broad consequences spanning several industries. With its quick scaling, ongoing improvement, and self-learning capabilities, this evolutionary invention functions as an agile capital-labor hybrid. Significantly, AI's architecture serves as the cornerstone for data-driven decision support by deftly sifting through large and complicated datasets to extra
{"title":"Transforming Organizational Development with AI: Navigating Change and Innovation for Success","authors":"Lalithendra Chowdari Mandava","doi":"10.35940/ijeat.a4282.1013123","DOIUrl":"https://doi.org/10.35940/ijeat.a4282.1013123","url":null,"abstract":"Effective change management emerges as a deciding element for an organization's survival and success in the changing terrain of today's fiercely competitive business climate. The variety of change management theories and approaches that are currently available, however, paints a complicated picture that is plagued by inconsistencies, a lack of strong empirical support, and unproven assumptions about contemporary organizational dynamics. This essay seeks to set the basis for a fresh paradigm for effective change administration by critically analyzing popular change management ideas. The gap between theory and practice is addressed in the paper, which concludes with suggestions for more research. In parallel, artificial intelligence (AI) has made incredible progress, giving rise to computers that mimic human autonomy and cognition. Industry-wide excitement has been sparked by the enthusiasm among academics, executives, and the general public, which has resulted in significant investments in utilizing AI's potential through creative business models. However, the lack of thorough academic guidance forces managers to struggle with AI integration issues, increasing the risk of project failure. An in-depth analysis of AI's complexities and its function as a spark for revolutionary business model innovation is provided in this article. A thorough literature assessment, which involves sifting through a sizable library of published works, combines up-to-date information on how AI is affecting the development of new business models. The findings come together to form a roadmap for seamless AI integration that includes four steps: understanding the fundamentals of AI and the skills needed for digital transformation, understanding current business models and their innovation potential, nurturing key proficiencies for AI assimilation, and gaining organizational acceptance while developing internal competencies. This article combines the fields of organizational change management and AI-driven business model innovation with ease, providing a thorough explanation to assist businesses in undergoing a successful transformation and innovation. These disciplines' confluence offers a practical vantage point for successfully adapting to, thriving in, and profiting within a dynamic business environment. Artificial intelligence (AI), a massively disruptive force that is altering international businesses, is at the vanguard of this revolution. The ability of AI to make decisions automatically, based on data analysis and observation, opens up hitherto untapped possibilities for value creation and competitive dominance, with broad consequences spanning several industries. With its quick scaling, ongoing improvement, and self-learning capabilities, this evolutionary invention functions as an agile capital-labor hybrid. Significantly, AI's architecture serves as the cornerstone for data-driven decision support by deftly sifting through large and complicated datasets to extra","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"54 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019657","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 : 2023-10-30DOI: 10.35940/ijeat.a4301.1013123
Heru Ismanto, Abner Doloksaribu, Diana Sri Susanti
This Smart and sustainable land use is the key to answering development challenges in the modern era. In the context of Merauke Regency, South Papua, rapid economic growth and significant environmental changes demand an integrated approach to managing land use. This research presents an innovative WebGIS-based model that combines geospatial information technology with land use analysis to provide sustainable solutions. Through the integration of spatial data, predictive analysis and stakeholder participation, this model enables stakeholders to explore alternative land use scenarios and evaluate their environmental, economic and societal impacts. The performance evaluation stage of the model shows its ability to accurately represent existing land use patterns. Validation with actual land use data confirms the ability of the model to reproduce the distribution of agricultural areas and protected forest areas. Furthermore, the evaluation of the environmental impact of the model results indicates that the model is capable of predicting the environmental impact of alternative land use scenarios. Consultation sessions with stakeholders proved the importance of their participation in the validation and adaptation of sustainable solutions. The results of this study indicate that WebGIS-based Smart Land Use model has great potential in assisting sustainable planning and decision-making in Merauke Regency. However, further validation and improvement of the model is needed to strengthen its accuracy and validity. This research provides valuable insights on the integration of geospatial information technology in sustainable development and provides guidance for the development of similar models in other regions.
{"title":"Model of WebGIS Based Sustainable Smart Land Use for Merauke Regency South Papua","authors":"Heru Ismanto, Abner Doloksaribu, Diana Sri Susanti","doi":"10.35940/ijeat.a4301.1013123","DOIUrl":"https://doi.org/10.35940/ijeat.a4301.1013123","url":null,"abstract":"This Smart and sustainable land use is the key to answering development challenges in the modern era. In the context of Merauke Regency, South Papua, rapid economic growth and significant environmental changes demand an integrated approach to managing land use. This research presents an innovative WebGIS-based model that combines geospatial information technology with land use analysis to provide sustainable solutions. Through the integration of spatial data, predictive analysis and stakeholder participation, this model enables stakeholders to explore alternative land use scenarios and evaluate their environmental, economic and societal impacts. The performance evaluation stage of the model shows its ability to accurately represent existing land use patterns. Validation with actual land use data confirms the ability of the model to reproduce the distribution of agricultural areas and protected forest areas. Furthermore, the evaluation of the environmental impact of the model results indicates that the model is capable of predicting the environmental impact of alternative land use scenarios. Consultation sessions with stakeholders proved the importance of their participation in the validation and adaptation of sustainable solutions. The results of this study indicate that WebGIS-based Smart Land Use model has great potential in assisting sustainable planning and decision-making in Merauke Regency. However, further validation and improvement of the model is needed to strengthen its accuracy and validity. This research provides valuable insights on the integration of geospatial information technology in sustainable development and provides guidance for the development of similar models in other regions.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"320 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019659","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 : 2023-10-30DOI: 10.35940/ijeat.a4283.1013123
Nguyen Van Hung, Nguyen Quoc Minh
Vietnam's economy is developing strongly, and the demand for energy use will increase rapidly. The development of smart grids contributes significantly to the transition and sustainable development of energy from renewable energy sources to improve the quality of the national power supply and promote the sustainable use of electricity economically and efficiently. Thus, this is highly beneficial in reducing carbon emissions and other types of pollution. Besides, electrification in the transportation industry is developing rapidly, such as Electric Vehicles (EVs) and Metros in recent years. Integrating electric vehicles into the grid will enable two-way energy exchange, reactive power compensation and load balancing. However, the number of EVs participating in charging at a time will cause some conflicts, such as voltage and power loss at the nodes. Therefore, the balancing problem between load demand and generation source is a difficult task in planning operations. This paper presents a method to optimize island Microgrid (MG) operation with the participation of electric vehicles based on renewable energy sources. Optimization techniques in intelligent resource forecasting and management algorithms are built in MATLAB to achieve different requirements. The proposed Microgrid manages energy efficiency that adapts to the variability of Renewable Energy with improved efficiency.
{"title":"Research on the Influence of Electric Vehicle Integration in Island Microgrid, Vietnam","authors":"Nguyen Van Hung, Nguyen Quoc Minh","doi":"10.35940/ijeat.a4283.1013123","DOIUrl":"https://doi.org/10.35940/ijeat.a4283.1013123","url":null,"abstract":"Vietnam's economy is developing strongly, and the demand for energy use will increase rapidly. The development of smart grids contributes significantly to the transition and sustainable development of energy from renewable energy sources to improve the quality of the national power supply and promote the sustainable use of electricity economically and efficiently. Thus, this is highly beneficial in reducing carbon emissions and other types of pollution. Besides, electrification in the transportation industry is developing rapidly, such as Electric Vehicles (EVs) and Metros in recent years. Integrating electric vehicles into the grid will enable two-way energy exchange, reactive power compensation and load balancing. However, the number of EVs participating in charging at a time will cause some conflicts, such as voltage and power loss at the nodes. Therefore, the balancing problem between load demand and generation source is a difficult task in planning operations. This paper presents a method to optimize island Microgrid (MG) operation with the participation of electric vehicles based on renewable energy sources. Optimization techniques in intelligent resource forecasting and management algorithms are built in MATLAB to achieve different requirements. The proposed Microgrid manages energy efficiency that adapts to the variability of Renewable Energy with improved efficiency.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136018101","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 : 2023-10-30DOI: 10.35940/ijeat.a4298.1013123
Patrick O. M. Ogutu, Dr. Nicholas Oyie, Dr. Winston Ojenge
Many industries employ different modes of control when it comes to PID parameter tuning. The problem of tuning a control system for linear and nonlinear systems has been undertaken by previous authors however the level of error reduction in the system performance has not been done quite well, hence the study on improved particle swarm optimization using improved Algorithm for PID parameter tuning. This paper tackled optimization of PID parameters based on improved PSO algorithm for the non-linear system. The particle swarm optimization is used to tune the PID parameters to ensure improved system response and operation. The PSO was deployed in a nonlinear system for application and validation of results achieved through PID tuning of the standard parameters on the MATLAB Simulink platform. The study ensured that the PID parameters were effectively tuned by applying improved PSO Algorithm to the plant process. The research used a standard nonlinear system depicting the real-life situation and an Improved Particle Swarm Optimization Algorithm to analyze and compare the improved behavior on the MATLAB/Simulink toolbox as applied to the PID parameters. Finally, it was logically realized that an improved PSO Algorithm system response was much better in comparison with the non-PSO tuned system. The simulation was performed on the plant transfer function using the MATLAB and Simulink platforms at various parameter choices and situations, and realizations were made from the data obtained. As the iteration was increased from 10, 50, and 100, there was a significant reduction in ITAE error from 0.054806 to a minimum of 0.01900, which is far better than the SPSO algorithm. SPSO reduces the error from 0.065143 to 0.020476. It was noted that the system behavior was far better in terms of settling time and peak overshoot for IPSO.
{"title":"Performance Analysis of an Improved Particle Swarm Optimization and the Standard Particle Swarm Optimization","authors":"Patrick O. M. Ogutu, Dr. Nicholas Oyie, Dr. Winston Ojenge","doi":"10.35940/ijeat.a4298.1013123","DOIUrl":"https://doi.org/10.35940/ijeat.a4298.1013123","url":null,"abstract":"Many industries employ different modes of control when it comes to PID parameter tuning. The problem of tuning a control system for linear and nonlinear systems has been undertaken by previous authors however the level of error reduction in the system performance has not been done quite well, hence the study on improved particle swarm optimization using improved Algorithm for PID parameter tuning. This paper tackled optimization of PID parameters based on improved PSO algorithm for the non-linear system. The particle swarm optimization is used to tune the PID parameters to ensure improved system response and operation. The PSO was deployed in a nonlinear system for application and validation of results achieved through PID tuning of the standard parameters on the MATLAB Simulink platform. The study ensured that the PID parameters were effectively tuned by applying improved PSO Algorithm to the plant process. The research used a standard nonlinear system depicting the real-life situation and an Improved Particle Swarm Optimization Algorithm to analyze and compare the improved behavior on the MATLAB/Simulink toolbox as applied to the PID parameters. Finally, it was logically realized that an improved PSO Algorithm system response was much better in comparison with the non-PSO tuned system. The simulation was performed on the plant transfer function using the MATLAB and Simulink platforms at various parameter choices and situations, and realizations were made from the data obtained. As the iteration was increased from 10, 50, and 100, there was a significant reduction in ITAE error from 0.054806 to a minimum of 0.01900, which is far better than the SPSO algorithm. SPSO reduces the error from 0.065143 to 0.020476. It was noted that the system behavior was far better in terms of settling time and peak overshoot for IPSO.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"101 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019654","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 : 2023-08-30DOI: 10.35940/ijeat.f4259.0812623
Dr. Priya. L, P. K, Dr. P. Kumar
Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.
{"title":"Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation","authors":"Dr. Priya. L, P. K, Dr. P. Kumar","doi":"10.35940/ijeat.f4259.0812623","DOIUrl":"https://doi.org/10.35940/ijeat.f4259.0812623","url":null,"abstract":"Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79496924","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 : 2023-08-30DOI: 10.35940/ijeat.f4262.0812623
Rudresh T. K., M. S. H., Shameem Banu L
In recent times, biometrics is mostly utilized for the authentication or identification of a user for a vast civilian application. Most of the electronic systems have been proposed that employed distinct behavioral or physiological human beings signature for identifying or verifying the user in an automatic manner. Nowadays, Electro Cardio Gram (ECG)-oriented biometric systems are in the exploration stage. The behavior of the ECG signal is distinctive to every person. As ECG is an exclusive physiological signal that is present only in the live people, it is utilized in the new biometric systems for recognizing the people and to counter the fraud as well as the forge attacks. Majority of the traditional techniques limits from the restriction in several points detection in the ECG signal. The contribution of this paper is the enhancement of the novel structure of person identification model by ECG signal. At first, the ECG signal collected from the three benchmark source is subjected for pre-processing, in which the noise is removed by Low Pass Filter (LPF) approach. Further, the Empirical Mode Decomposition (EMD) is adopted for the decomposition of signal. As feature selection is the significant part of classification enhancement, Principle Component Analysis (PCA) is used as the effective feature extraction that takes the most important features from the signal. Finally, the adoption of Deep Neural Network (DNN) is performed as the deep learning model that could identify the exact person from the given ECG signal. The effectiveness of the method is extensively validated on benchmark datasets and retrieves the outcome.
{"title":"Deep Neural Network-based Person Identification using ECG Signals","authors":"Rudresh T. K., M. S. H., Shameem Banu L","doi":"10.35940/ijeat.f4262.0812623","DOIUrl":"https://doi.org/10.35940/ijeat.f4262.0812623","url":null,"abstract":"In recent times, biometrics is mostly utilized for the authentication or identification of a user for a vast civilian application. Most of the electronic systems have been proposed that employed distinct behavioral or physiological human beings signature for identifying or verifying the user in an automatic manner. Nowadays, Electro Cardio Gram (ECG)-oriented biometric systems are in the exploration stage. The behavior of the ECG signal is distinctive to every person. As ECG is an exclusive physiological signal that is present only in the live people, it is utilized in the new biometric systems for recognizing the people and to counter the fraud as well as the forge attacks. Majority of the traditional techniques limits from the restriction in several points detection in the ECG signal. The contribution of this paper is the enhancement of the novel structure of person identification model by ECG signal. At first, the ECG signal collected from the three benchmark source is subjected for pre-processing, in which the noise is removed by Low Pass Filter (LPF) approach. Further, the Empirical Mode Decomposition (EMD) is adopted for the decomposition of signal. As feature selection is the significant part of classification enhancement, Principle Component Analysis (PCA) is used as the effective feature extraction that takes the most important features from the signal. Finally, the adoption of Deep Neural Network (DNN) is performed as the deep learning model that could identify the exact person from the given ECG signal. The effectiveness of the method is extensively validated on benchmark datasets and retrieves the outcome.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80830345","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 : 2023-08-30DOI: 10.35940/ijeat.f4255.0812623
G. Moustafa
This paper proposes a new strategy based on the differential evolution algorithm to optimize the performance of distribution networks through the optimal coordination of Static VAR Compensator modules (SVCs). Installation costs minimization and savings maximization due to reducing power losses are merged in one multi-objective function. In order to investigate the influences of varying loading conditions, various regular loadings are further combined. This framework implemented on a 37-bus real feeder connected to the Egyptian Unified Network (EUN). The findings of the simulation reveal evident technical and economical characteristics of the proposed algorithm. The reactive power compensation using SVCs based on the pro-posed scheme leads to major quality improvements of the entire nodes’ voltage with variations of loads. Especially, in light loading condition, the SVCs control their performance characteristics according to the reactive power demands in the adjacent nodes.
{"title":"Differential Evolution Algorithm for Coordination of SVC Modules in MV Distribution Systems","authors":"G. Moustafa","doi":"10.35940/ijeat.f4255.0812623","DOIUrl":"https://doi.org/10.35940/ijeat.f4255.0812623","url":null,"abstract":"This paper proposes a new strategy based on the differential evolution algorithm to optimize the performance of distribution networks through the optimal coordination of Static VAR Compensator modules (SVCs). Installation costs minimization and savings maximization due to reducing power losses are merged in one multi-objective function. In order to investigate the influences of varying loading conditions, various regular loadings are further combined. This framework implemented on a 37-bus real feeder connected to the Egyptian Unified Network (EUN). The findings of the simulation reveal evident technical and economical characteristics of the proposed algorithm. The reactive power compensation using SVCs based on the pro-posed scheme leads to major quality improvements of the entire nodes’ voltage with variations of loads. Especially, in light loading condition, the SVCs control their performance characteristics according to the reactive power demands in the adjacent nodes.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"262 1-2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78445810","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 : 2023-08-30DOI: 10.35940/ijeat.f4249.0812623
Evangelin Jeba J, C. Rajesh
In micro grids, energy management is referred to as an information and control system that offers the essential functionality to ensure that the energy supply from the generation and distribution systems occurs at the lowest possible operational cost. Energy management systems (EMS) support distributed energy resource utilization in micro grids, especially when variable generation and pricing are present. In this paper, an Artificial Neural Network (ANN)-based energy management approach for a hybrid wind, solar and Battery Storage System (BSS) is presented. To sustain the DC voltage, a 3 Port DC-DC Converter is also proposed. While renewable energy systems have numerous advantages, one of the challenges they face is the intermittency of power generation, leading to fluctuations in the power supply to the grid. Therefore, EMS aims to reduce these variations. Another goal is to maintain the battery state of charge (SOC) within the allowed ranges to extend the battery life. The implementation is carried out in Simulink/Matlab platform. To demonstrate the efficacy of the suggested approach, we compare the Total Harmonic Distortion (THD) of the proposed controller (1.52%) with that of conventional controllers, including the ZSI-based PID controller (3.05%), PI controller (4.02%), and FO-PI (3.32%) controller.
{"title":"Artificial Neural Network with 3-Port Dc-Dc Converter Based Energy Management Scheme in Sustainable Energy Sources","authors":"Evangelin Jeba J, C. Rajesh","doi":"10.35940/ijeat.f4249.0812623","DOIUrl":"https://doi.org/10.35940/ijeat.f4249.0812623","url":null,"abstract":"In micro grids, energy management is referred to as an information and control system that offers the essential functionality to ensure that the energy supply from the generation and distribution systems occurs at the lowest possible operational cost. Energy management systems (EMS) support distributed energy resource utilization in micro grids, especially when variable generation and pricing are present. In this paper, an Artificial Neural Network (ANN)-based energy management approach for a hybrid wind, solar and Battery Storage System (BSS) is presented. To sustain the DC voltage, a 3 Port DC-DC Converter is also proposed. While renewable energy systems have numerous advantages, one of the challenges they face is the intermittency of power generation, leading to fluctuations in the power supply to the grid. Therefore, EMS aims to reduce these variations. Another goal is to maintain the battery state of charge (SOC) within the allowed ranges to extend the battery life. The implementation is carried out in Simulink/Matlab platform. To demonstrate the efficacy of the suggested approach, we compare the Total Harmonic Distortion (THD) of the proposed controller (1.52%) with that of conventional controllers, including the ZSI-based PID controller (3.05%), PI controller (4.02%), and FO-PI (3.32%) controller.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82953860","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 : 2023-06-30DOI: 10.35940/ijeat.e4195.0612523
Dr. Nirmla Sharma, Sameera Iqbal
Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. Gini index is the name of the level task that has applied to assess the binary changes in the dataset and worked with the definite object variable “Success” or “Failure”. Split creation is basically covering the dataset values. Decision trees monitor a top-down, greedy method that has recognized as recursive binary splitting. It has statistics for 15 statistics facts of scholar statistics on pass or fails an online Machine Learning exam. Decision trees are in the class of supervised machine learning. It has been commonly applied as it has informal implement, interpreted certainly, derived to quantitative, qualitative, nonstop, and binary splits, and provided consistent outcomes. The CART tree has regression technique applied to expected standards of nonstop variables. CART regression trees are an actual informal technique of understanding outcomes.
{"title":"Applying Decision Tree Algorithm Classification and Regression Tree (CART) Algorithm to Gini Techniques Binary Splits","authors":"Dr. Nirmla Sharma, Sameera Iqbal","doi":"10.35940/ijeat.e4195.0612523","DOIUrl":"https://doi.org/10.35940/ijeat.e4195.0612523","url":null,"abstract":"Decision tree study is a predictive modelling tool that is used over many grounds. It is constructed through an algorithmic technique that is divided the dataset in different methods created on varied conditions. Decisions trees are the extreme dominant algorithms that drop under the set of supervised algorithms. However, Decision Trees appearance modest and natural, there is nothing identical modest near how the algorithm drives nearby the procedure determining on splits and how tree snipping happens. The initial object to appreciate in Decision Trees is that it splits the analyst field, i.e., the objective parameter into diverse subsets which are comparatively more similar from the viewpoint of the objective parameter. Gini index is the name of the level task that has applied to assess the binary changes in the dataset and worked with the definite object variable “Success” or “Failure”. Split creation is basically covering the dataset values. Decision trees monitor a top-down, greedy method that has recognized as recursive binary splitting. It has statistics for 15 statistics facts of scholar statistics on pass or fails an online Machine Learning exam. Decision trees are in the class of supervised machine learning. It has been commonly applied as it has informal implement, interpreted certainly, derived to quantitative, qualitative, nonstop, and binary splits, and provided consistent outcomes. The CART tree has regression technique applied to expected standards of nonstop variables. CART regression trees are an actual informal technique of understanding outcomes.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74703350","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 : 2023-06-30DOI: 10.35940/ijeat.e4209.0612523
Dr. Subhashini Sailesh Bhaskaran
Knowledge management practices in Higher education institutions can lead to better decision making, better curriculum development, and research, enhanced academic and administrative services and better utilisation of resources (Kidwell et al., 2000) [6]. Moreover, the advancement in the field of Data Mining and big data science has opened up significant opportunities for these institutions to create, manage, protect and disseminate knowledge effectively. This paper presents a knowledge management model to enhance the research processes, teaching and learning processes, student and alumni services, administrative services and processes, strategic planning and management. This paper uses data mining and big data science techniques to unearth the knowledge hidden in student information systems to enable improved HEIs management and progress.
{"title":"A Knowledge Management Model to Improve Strategic Planning and Decision Making in HEIs","authors":"Dr. Subhashini Sailesh Bhaskaran","doi":"10.35940/ijeat.e4209.0612523","DOIUrl":"https://doi.org/10.35940/ijeat.e4209.0612523","url":null,"abstract":"Knowledge management practices in Higher education institutions can lead to better decision making, better curriculum development, and research, enhanced academic and administrative services and better utilisation of resources (Kidwell et al., 2000) [6]. Moreover, the advancement in the field of Data Mining and big data science has opened up significant opportunities for these institutions to create, manage, protect and disseminate knowledge effectively. This paper presents a knowledge management model to enhance the research processes, teaching and learning processes, student and alumni services, administrative services and processes, strategic planning and management. This paper uses data mining and big data science techniques to unearth the knowledge hidden in student information systems to enable improved HEIs management and progress.","PeriodicalId":13981,"journal":{"name":"International Journal of Engineering and Advanced Technology","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73678294","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：