Ima essiet - Academia.edu (original) (raw)
Papers by Ima essiet
2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), 2014
Journal of Emerging Trends in Engineering and Applied Sciences, 2014
Neural networks are an extremely powerful tool for data mining. They are especially useful in cas... more Neural networks are an extremely powerful tool for data mining. They are especially useful in cases involving data classification where it is difficult to establish a specific pattern in the search space. In an era when artificial intelligence is increasingly being utilised in industrial and medical applications throughout Africa, it is becoming evident that this is an emerging trend in the continent. Neural networks became popular in the early 20th century and were employed in data classification and pattern recognition. Neural network applications include load forecasting, weather prediction, plant control and time series analysis to mention a few. The concentration of ammonia in cooked food is directly related to its suitability for human consumption. This paper explores the idea of artificial intelligence by employing the use of a feed-forward neural network with two process layers to determine whether a selected cooked food is good or bad. The neural simulation is carried out u...
This paper highlights the numerous advantages of process simulation using neural networks. Apart ... more This paper highlights the numerous advantages of process simulation using neural networks. Apart from reviewing some successful industrial applications of neural networks (specifically in the field of electrical engineering), results of the authors' research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%.
This paper discusses some of the several successful applications of neural networks which have ma... more This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%.
Journal of Emerging Trends in Engineering and Applied Sciences, 2013
The human mouth contains many kinds of substances both in liquid and gaseous form. The individual... more The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. Kidney failure is one disease which is identified by extremely high ammonia content in human breath. This disease is as a result of the kidneys' inability to process the body's liquid waste thereby resulting in high blood urea nitrogen (BUN) level. The result of this is the release of urea throughout the body which is dissipated in the form of ammonia through oral breath. This paper proposes an affordable ammonia breathalyzer for the diagnosis of kidney failure in humans with 85% success rate. The purpose of this research is to provide an affordable and reliable means of detecting kidney failure in hospitals and even in homes. This will h...
This study employs a 4-input and 1-output feedforward neural network with adalines used to implem... more This study employs a 4-input and 1-output feedforward neural network with adalines used to implement learning via error back-propagation (EBP) using least mean square rule. The neural network is used to predict the condition of both cooked and uncooked food as well as fresh vegetables by determining food age (in days). Neurosolutions training software is used to simulate the neural network. Training data is obtained from a constructed metal oxide semiconductor (MOS) ammonia circuit. Results show that a 95% overall accuracy of neural network results is obtained. This demonstrates the capability of neural networks in accurate classification of sample data points. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes. Keywords/Index Terms: neural network, supervised learning, back propagation, e-nose, artificial intelligence
2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), 2014
Artificial intelligence (AI) is the aspect of computing concerned with programming computers to b... more Artificial intelligence (AI) is the aspect of computing concerned with programming computers to behave like humans. In spite of the fact that no artificial intelligence system is capable of fully simulating human behaviour, there are aspects which have been successfully mimicked. One of these applications is the development of intelligent systems to model the human sense of smell. The artificial neural network is one tool which makes inferences based on pattern recognition of selected parameters in their environment. This paper applies the neural network to the determination of food age using ammonia concentration as the major metric. The resulting algorithm is capable of determining age of common food types (in days) using supervised learning to obtain the knowledge inference database. A two process-layer neural network topology was observed to provide most accurate results with overall accuracy of 95 percent. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes.
IOSR Journal of Computer Engineering, 2014
Softcomputing techniques are fast becoming reliable and efficient means of prediction and estimat... more Softcomputing techniques are fast becoming reliable and efficient means of prediction and estimation. This has made their application more wide spread in recent years. With the growing need for intelligent devices and systems comes the need to explore these techniques even further. This paper applies neural networks and differential evolution (two of the most effective softcomputing algorithms) to the estimation of air quality and compares the accuracy of their results using the mean square error (MSE) method. Air pollution is an ever increasing menace in major cities around the world. Air contaminants such as those from motor vehicles and industrial wastes are the most common forms of pollutants. The health implications of inhaling contaminated air are evident in the growing number of cases of lung cancer and tuberculosis. Since these contaminants are invisible to the naked eye, it becomes necessary to implement an algorithm which can accurately identify them especially when their concentration becomes a threat to human health. The aim of this paper is to develop an effective algorithm to achieve this by comparing the efficacy of both neural networks and differential evolution in the determination of the concentration of air pollutants.The air component markers being analysed include oxides of carbon, nitrogen, sulphur and also ammonia. The study also intends to identify the most potent sources of air pollution by analysing air samples obtained at various locations within Kano city in Nigeria.
IOSR Journal of Computer Engineering, 2014
Neural networks are an extremely powerful tool for data mining. They are especially useful in cas... more Neural networks are an extremely powerful tool for data mining. They are especially useful in cases involving data classification where it is difficult to establish a pattern in the search space. In an era when artificial intelligence is increasingly being utilised in industrial and medical applications throughout the world, it is becoming evident that this is an emerging trend. This paper explores the idea of artificial intelligence by employing the use of a feed-forward neural network with two process layers to determine the concentration of ammonia in exhaled human breath. The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. Kidney failure is one diesease which is identified by an extremely high ammonia content in human breath. This disease is as a result of the kidneys' inability to process the body's liquid waste. The result of this is the release of urea throughout the body which is dissipated in the form of ammonia through oral breath. The neural simulation is carried out using NeuroSolutions version 5 software. The neural network correctly identified the concentration of oral ammonia as an indication of kidney failure with an accuracy of 85%.
Acta Periodica Technologica, 2013
An increasing concentration of ammonia in cooked food is in direct proportion to the extent of de... more An increasing concentration of ammonia in cooked food is in direct proportion to the extent of decay. This fact is used to design an electronic nose (e-nose) based on metal oxide semiconductor odour sensor circuit capable of discriminating good and bad cooked food. On the basis of the data produced by the e-nose circuit, a feedforward multilayer neural network is designed and trained to recognize varying concentrations of ammonia in the food. Test results of the prototype e-nose system show that it is capable of classifying cooked food as being good or bad with over 92% average success rate.
Lecture Notes in Computer Science, 2015
The human mouth contains many kinds of substances both in liquid and gaseous form. The individual... more The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. One of such is tooth decay caries which occurs when there is insufficient concentration of ammonia in the mouth. This paper proposes an affordable ammonia breathalyzer designed using metal oxide sensor for the detection and prediction of tooth caries in humans with a 87% overall success rate. Selection of appropriate sensor was done via simulation using feed-forward artificial neural network ANN. The breathalyzer has been designed and constructed to be low-cost such that it can be used for early detection and prevention of tooth decay.
Mathematical Problems in Engineering
Loss of selection pressure in the presence of many objectives is one of the pertinent problems in... more Loss of selection pressure in the presence of many objectives is one of the pertinent problems in evolutionary optimization. Therefore, it is difficult for evolutionary algorithms to find the best-fitting candidate solutions for the final Pareto optimal front representing a multi-objective optimization problem, particularly when the solution space changes with time. In this study, we propose a multi-objective algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). This evolutionary algorithm is an improvement of the earlier proposed dyNSGA-III, which used principal component analysis and Euclidean distance to maintain selection pressure and integrity of the final Pareto optimal front. E-dyNSGA-III proposes a strategy to select a group of super-performing mutated candidates to improve the selection pressure at high dimensions and with changing time. This strategy is based on an earlier proposed approach on the use of mutated candidates, which are...
2018 Conference on Information Communications Technology and Society (ICTAS), 2018
This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs... more This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions, and the improvement in the performance of the single archive approach is validated by the improved performance metrics and overall computational time. Overall, the proposed single-archive algorithm (called DOAEA) generated better metrics and faster computational time for the Gee-Tan-Abbas (GTA) test suite for average MIGD and average MHV compared to previously proposed two-archive algorithm, DTAEA.
IEEE Access, 2020
Many real-world problems are modeled as multi-objective optimization problems whose optimal solut... more Many real-world problems are modeled as multi-objective optimization problems whose optimal solutions change with time. These problems are commonly termed dynamic multi-objective optimization problems (DMOPs). One challenge associated with solving such problems is the fact that the Pareto front or Pareto set often changes too quickly. This means that the optimal solution set at period t may likely vary from that at (t+1), and this makes the process of optimizing such problems computationally expensive to implement. This article proposes the use of adaptive mutation and crossover operators for the non-dominated sorting genetic algorithm III (NSGA-III). The aim is to find solutions that can adapt to fitness changes in the objective function space over time. The proposed approach improves the capability of NSGA-III to solve multi-objective optimization problems with solutions that change quickly in both space and time. Results obtained show that this method of population reinitialization can effectively optimize selected benchmark dynamic problems. In addition, we test the capability of the proposed algorithm to select robust solutions over time. We recognize that DMOPs are characterized by rapidly changing optimal solutions. Therefore, we also test the ability of our proposed algorithm to handle these changes. This is achieved by evaluating its performance on selected robust optimization over time (ROOT) and robust Pareto-optimality over time (RPOOT) benchmark problems.
2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), 2017
The challenges of many-objective optimization are investigated; and one new algorithm, which is b... more The challenges of many-objective optimization are investigated; and one new algorithm, which is based on the NSGA-II, is proposed for multi-objective optimization in this paper. The reference points and an adaptable crossover rate are combined in the algorithm to improve the performance of NSGA-II. The performance of NSGA for optimizing the many objective search space is examined with and without the proposed algorithm through a constrained two-objective problem with up to 40 dimensions. Simulation results show that the proposed algorithm improves the performance of NSGA for the selected test problem in generations where a non-dominated set is not obtained by 39%.
2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 2021
A ceaseless supply of electricity is essential for all developmental sectors and even more import... more A ceaseless supply of electricity is essential for all developmental sectors and even more important nowadays, as the globe evolves through a call for artificially intelligent systems in the fourth industrial revolution. Power interruptions lead to unpleasant human situations as well as great losses within all health, education, and industrial facilities requiring constant electricity. For this reason, we propose the design of an automatic transfer switch (ATS) for power transfer applications to maximize uptime. The ATS ensures the transfer of power between two power sources, hydroelectric and solar, during blackouts or power failures. The design is principally based on an Arduino Mega 2560, triacs, LEDs, as well as an LCD, SD card, Bluetooth, and GSM modules. The Arduino is powerful enough to support all the ATS modules and sustain the combined system processes required for the ATS’s automatic operation. Triacs, which are semiconductor switching devices, were utilized for high-speed switching between the two power supplies. The LEDs and LCD played a signalization role, indicating the state of the ATS at all times. Also, the SD card stores the system’s generated data whilst the Bluetooth modules ensure wireless connectivity between the ATS and a mobile device. The GSM module is for short messages (SMS). The results prove the functioning of the designed ATS, which ensures the transfer of power between two power sources with switching speeds less than 2.58 msec during blackouts or power failures. Furthermore, the ATS is a reliable, fast, and automatic system capable of protecting its internal components from surges. The proposed ATS is validated based on simulations, and the results show that the proposed ATS is promising.
Energy Reports, 2021
Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capab... more Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator 445and445 and 445and45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9-1.0 p.u.
Applied Sciences, 2020
This paper examines the role of demand response aggregators in minimizing the cost of electricity... more This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during wi...
2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), 2014
Journal of Emerging Trends in Engineering and Applied Sciences, 2014
Neural networks are an extremely powerful tool for data mining. They are especially useful in cas... more Neural networks are an extremely powerful tool for data mining. They are especially useful in cases involving data classification where it is difficult to establish a specific pattern in the search space. In an era when artificial intelligence is increasingly being utilised in industrial and medical applications throughout Africa, it is becoming evident that this is an emerging trend in the continent. Neural networks became popular in the early 20th century and were employed in data classification and pattern recognition. Neural network applications include load forecasting, weather prediction, plant control and time series analysis to mention a few. The concentration of ammonia in cooked food is directly related to its suitability for human consumption. This paper explores the idea of artificial intelligence by employing the use of a feed-forward neural network with two process layers to determine whether a selected cooked food is good or bad. The neural simulation is carried out u...
This paper highlights the numerous advantages of process simulation using neural networks. Apart ... more This paper highlights the numerous advantages of process simulation using neural networks. Apart from reviewing some successful industrial applications of neural networks (specifically in the field of electrical engineering), results of the authors' research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%.
This paper discusses some of the several successful applications of neural networks which have ma... more This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%.
Journal of Emerging Trends in Engineering and Applied Sciences, 2013
The human mouth contains many kinds of substances both in liquid and gaseous form. The individual... more The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. Kidney failure is one disease which is identified by extremely high ammonia content in human breath. This disease is as a result of the kidneys' inability to process the body's liquid waste thereby resulting in high blood urea nitrogen (BUN) level. The result of this is the release of urea throughout the body which is dissipated in the form of ammonia through oral breath. This paper proposes an affordable ammonia breathalyzer for the diagnosis of kidney failure in humans with 85% success rate. The purpose of this research is to provide an affordable and reliable means of detecting kidney failure in hospitals and even in homes. This will h...
This study employs a 4-input and 1-output feedforward neural network with adalines used to implem... more This study employs a 4-input and 1-output feedforward neural network with adalines used to implement learning via error back-propagation (EBP) using least mean square rule. The neural network is used to predict the condition of both cooked and uncooked food as well as fresh vegetables by determining food age (in days). Neurosolutions training software is used to simulate the neural network. Training data is obtained from a constructed metal oxide semiconductor (MOS) ammonia circuit. Results show that a 95% overall accuracy of neural network results is obtained. This demonstrates the capability of neural networks in accurate classification of sample data points. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes. Keywords/Index Terms: neural network, supervised learning, back propagation, e-nose, artificial intelligence
2014 IEEE 6th International Conference on Adaptive Science & Technology (ICAST), 2014
Artificial intelligence (AI) is the aspect of computing concerned with programming computers to b... more Artificial intelligence (AI) is the aspect of computing concerned with programming computers to behave like humans. In spite of the fact that no artificial intelligence system is capable of fully simulating human behaviour, there are aspects which have been successfully mimicked. One of these applications is the development of intelligent systems to model the human sense of smell. The artificial neural network is one tool which makes inferences based on pattern recognition of selected parameters in their environment. This paper applies the neural network to the determination of food age using ammonia concentration as the major metric. The resulting algorithm is capable of determining age of common food types (in days) using supervised learning to obtain the knowledge inference database. A two process-layer neural network topology was observed to provide most accurate results with overall accuracy of 95 percent. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes.
IOSR Journal of Computer Engineering, 2014
Softcomputing techniques are fast becoming reliable and efficient means of prediction and estimat... more Softcomputing techniques are fast becoming reliable and efficient means of prediction and estimation. This has made their application more wide spread in recent years. With the growing need for intelligent devices and systems comes the need to explore these techniques even further. This paper applies neural networks and differential evolution (two of the most effective softcomputing algorithms) to the estimation of air quality and compares the accuracy of their results using the mean square error (MSE) method. Air pollution is an ever increasing menace in major cities around the world. Air contaminants such as those from motor vehicles and industrial wastes are the most common forms of pollutants. The health implications of inhaling contaminated air are evident in the growing number of cases of lung cancer and tuberculosis. Since these contaminants are invisible to the naked eye, it becomes necessary to implement an algorithm which can accurately identify them especially when their concentration becomes a threat to human health. The aim of this paper is to develop an effective algorithm to achieve this by comparing the efficacy of both neural networks and differential evolution in the determination of the concentration of air pollutants.The air component markers being analysed include oxides of carbon, nitrogen, sulphur and also ammonia. The study also intends to identify the most potent sources of air pollution by analysing air samples obtained at various locations within Kano city in Nigeria.
IOSR Journal of Computer Engineering, 2014
Neural networks are an extremely powerful tool for data mining. They are especially useful in cas... more Neural networks are an extremely powerful tool for data mining. They are especially useful in cases involving data classification where it is difficult to establish a pattern in the search space. In an era when artificial intelligence is increasingly being utilised in industrial and medical applications throughout the world, it is becoming evident that this is an emerging trend. This paper explores the idea of artificial intelligence by employing the use of a feed-forward neural network with two process layers to determine the concentration of ammonia in exhaled human breath. The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. Kidney failure is one diesease which is identified by an extremely high ammonia content in human breath. This disease is as a result of the kidneys' inability to process the body's liquid waste. The result of this is the release of urea throughout the body which is dissipated in the form of ammonia through oral breath. The neural simulation is carried out using NeuroSolutions version 5 software. The neural network correctly identified the concentration of oral ammonia as an indication of kidney failure with an accuracy of 85%.
Acta Periodica Technologica, 2013
An increasing concentration of ammonia in cooked food is in direct proportion to the extent of de... more An increasing concentration of ammonia in cooked food is in direct proportion to the extent of decay. This fact is used to design an electronic nose (e-nose) based on metal oxide semiconductor odour sensor circuit capable of discriminating good and bad cooked food. On the basis of the data produced by the e-nose circuit, a feedforward multilayer neural network is designed and trained to recognize varying concentrations of ammonia in the food. Test results of the prototype e-nose system show that it is capable of classifying cooked food as being good or bad with over 92% average success rate.
Lecture Notes in Computer Science, 2015
The human mouth contains many kinds of substances both in liquid and gaseous form. The individual... more The human mouth contains many kinds of substances both in liquid and gaseous form. The individual concentrations of each of these substances could provide useful insight to the health condition of the entire body. Ammonia is one of such substances whose concentration in the mouth has revealed the presence or absence of diseases in the body. One of such is tooth decay caries which occurs when there is insufficient concentration of ammonia in the mouth. This paper proposes an affordable ammonia breathalyzer designed using metal oxide sensor for the detection and prediction of tooth caries in humans with a 87% overall success rate. Selection of appropriate sensor was done via simulation using feed-forward artificial neural network ANN. The breathalyzer has been designed and constructed to be low-cost such that it can be used for early detection and prevention of tooth decay.
Mathematical Problems in Engineering
Loss of selection pressure in the presence of many objectives is one of the pertinent problems in... more Loss of selection pressure in the presence of many objectives is one of the pertinent problems in evolutionary optimization. Therefore, it is difficult for evolutionary algorithms to find the best-fitting candidate solutions for the final Pareto optimal front representing a multi-objective optimization problem, particularly when the solution space changes with time. In this study, we propose a multi-objective algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). This evolutionary algorithm is an improvement of the earlier proposed dyNSGA-III, which used principal component analysis and Euclidean distance to maintain selection pressure and integrity of the final Pareto optimal front. E-dyNSGA-III proposes a strategy to select a group of super-performing mutated candidates to improve the selection pressure at high dimensions and with changing time. This strategy is based on an earlier proposed approach on the use of mutated candidates, which are...
2018 Conference on Information Communications Technology and Society (ICTAS), 2018
This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs... more This paper presents an algorithm based on dynamic multiobjective optimization (DMO) which employs a single randomly mutating time-variant archive to balance convergence and diversity in order to efficiently select the final, non-dominated Pareto set. The algorithm is tested on selected dynamic optimization benchmark functions, and the improvement in the performance of the single archive approach is validated by the improved performance metrics and overall computational time. Overall, the proposed single-archive algorithm (called DOAEA) generated better metrics and faster computational time for the Gee-Tan-Abbas (GTA) test suite for average MIGD and average MHV compared to previously proposed two-archive algorithm, DTAEA.
IEEE Access, 2020
Many real-world problems are modeled as multi-objective optimization problems whose optimal solut... more Many real-world problems are modeled as multi-objective optimization problems whose optimal solutions change with time. These problems are commonly termed dynamic multi-objective optimization problems (DMOPs). One challenge associated with solving such problems is the fact that the Pareto front or Pareto set often changes too quickly. This means that the optimal solution set at period t may likely vary from that at (t+1), and this makes the process of optimizing such problems computationally expensive to implement. This article proposes the use of adaptive mutation and crossover operators for the non-dominated sorting genetic algorithm III (NSGA-III). The aim is to find solutions that can adapt to fitness changes in the objective function space over time. The proposed approach improves the capability of NSGA-III to solve multi-objective optimization problems with solutions that change quickly in both space and time. Results obtained show that this method of population reinitialization can effectively optimize selected benchmark dynamic problems. In addition, we test the capability of the proposed algorithm to select robust solutions over time. We recognize that DMOPs are characterized by rapidly changing optimal solutions. Therefore, we also test the ability of our proposed algorithm to handle these changes. This is achieved by evaluating its performance on selected robust optimization over time (ROOT) and robust Pareto-optimality over time (RPOOT) benchmark problems.
2017 2nd International Conference on Robotics and Automation Engineering (ICRAE), 2017
The challenges of many-objective optimization are investigated; and one new algorithm, which is b... more The challenges of many-objective optimization are investigated; and one new algorithm, which is based on the NSGA-II, is proposed for multi-objective optimization in this paper. The reference points and an adaptable crossover rate are combined in the algorithm to improve the performance of NSGA-II. The performance of NSGA for optimizing the many objective search space is examined with and without the proposed algorithm through a constrained two-objective problem with up to 40 dimensions. Simulation results show that the proposed algorithm improves the performance of NSGA for the selected test problem in generations where a non-dominated set is not obtained by 39%.
2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), 2021
A ceaseless supply of electricity is essential for all developmental sectors and even more import... more A ceaseless supply of electricity is essential for all developmental sectors and even more important nowadays, as the globe evolves through a call for artificially intelligent systems in the fourth industrial revolution. Power interruptions lead to unpleasant human situations as well as great losses within all health, education, and industrial facilities requiring constant electricity. For this reason, we propose the design of an automatic transfer switch (ATS) for power transfer applications to maximize uptime. The ATS ensures the transfer of power between two power sources, hydroelectric and solar, during blackouts or power failures. The design is principally based on an Arduino Mega 2560, triacs, LEDs, as well as an LCD, SD card, Bluetooth, and GSM modules. The Arduino is powerful enough to support all the ATS modules and sustain the combined system processes required for the ATS’s automatic operation. Triacs, which are semiconductor switching devices, were utilized for high-speed switching between the two power supplies. The LEDs and LCD played a signalization role, indicating the state of the ATS at all times. Also, the SD card stores the system’s generated data whilst the Bluetooth modules ensure wireless connectivity between the ATS and a mobile device. The GSM module is for short messages (SMS). The results prove the functioning of the designed ATS, which ensures the transfer of power between two power sources with switching speeds less than 2.58 msec during blackouts or power failures. Furthermore, the ATS is a reliable, fast, and automatic system capable of protecting its internal components from surges. The proposed ATS is validated based on simulations, and the results show that the proposed ATS is promising.
Energy Reports, 2021
Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capab... more Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator 445and445 and 445and45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9-1.0 p.u.
Applied Sciences, 2020
This paper examines the role of demand response aggregators in minimizing the cost of electricity... more This paper examines the role of demand response aggregators in minimizing the cost of electricity generation by distribution utilities in a day-ahead electricity market. In this paper, 2500 standard South African homes are considered as end users. Five clusters (and aggregators) are considered with 500 homes in each cluster. Two cases are analysed: (1) Utilization of renewable energy sources (RES) is implemented by the distribution supply operator (DSO), where it meets excess demand for end users during peak hours by purchasing electricity from the renewable sources of the energy market, and (2) Utilization of RES is implemented by end users alone, and it is assumed that every household has one plug-in electric vehicle (PEV). The aggregators then compete with each other for the most cost-effective energy usage profile; the aggregator with the least energy demand wins the bid. In both cases, energy pricing is estimated according to the day-ahead energy market. A typical day during wi...