This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the C...This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the CID card from the background.Next,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector.Finally,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.展开更多
Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods ...Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems,leading to inaccuracies that compromise the efficiency and reliability of electric vehicles.This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks.Specifically,teaching-learning based optimization(TLBO)is employed to optimize the weights and biases of the deep neural networks model,enhancing estimation accuracy.The proposed TLBO-deep neural networks(TLBO-DNNs)method was evaluated on a dataset of 1,064,000 samples,with performance assessed using mean absolute error(MAE),root mean square error(RMSE),and convergence value.The TLBO-DNNs model achieved an MAE of 3.4480,an RMSE of 4.6487,and a convergence value of 0.0328,outperforming other hybrid approaches.These include the barnacle mating optimizer-deep neural networks(BMO-DNNs)with an MAE of 5.3848,an RMSE of 7.0395,and a convergence value of 0.0492;the evolutionary mating algorithm-deep neural networks(EMA-DNNs)with an MAE of 7.6127,an RMSE of 11.2287,and a convergence value of 0.0536;and the particle swarm optimization-deep neural networks(PSO-DNNs)with an MAE of 4.3089,an RMSE of 5.9672,and a convergence value of 0.0345.Additionally,the TLBO-DNNs approach outperformed standalone models,including the autoregressive integrated moving average(ARIMA)model(MAE:14.3301,RMSE:7.0697)and support vector machines(MAE:6.0065,RMSE:8.0360).This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles,contributing to improved efficiency and reliability in electric vehicle operations.展开更多
This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating condit...This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating conditions.The electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery management.This study applies a Random Forest(RF)machine learning algorithm to improve SOC estimation.Traditionally,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions.Previous methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV applications.In contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world scenarios.By leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC values.This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions.Comprehensive comparative analyses showcase the superiority of the RF over ELM.The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV industry.The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3.This integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV technology.Importantly,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a lower Mean Absolute Error(MAE)of 4.432,1%versus 5.111,2%for ELM across rigorous k-fold cross-validation testing,reaffirming its superiority in quantitative SOC estimation.展开更多
Accurate forecasting of cooling and heating loads in buildings is vital for effective energy management,cost efficiency,and environmental sustainability.However,traditional forecasting models often face limitations in...Accurate forecasting of cooling and heating loads in buildings is vital for effective energy management,cost efficiency,and environmental sustainability.However,traditional forecasting models often face limitations in capturing the complex and non-linear characteristics of building energy consumption patterns.To address this challenge,this study proposes a hybrid predictive approach by integrating eXtreme gradient boosting(XGBoost)with eight metaheuristic optimization algorithms.The selected algorithms are ant colony optimization(ACO),barnacles mating optimizer(BMO),genetic algorithm(GA),gradient-based optimizer(GBO),hippopotamus optimization(HO),Kepler optimization algorithm(KOA),particle swarm optimization(PSO),and teachinglearning-based optimization(TLBO).Each metaheuristic was used to optimize the hyperparameters of the XGBoost model,resulting in the following hybrid models:ACO-XGBoost,BMO-XGBoost,GA-XGBoost,GBOXGBoost,HO-XGBoost,KOA-XGBoost,PSO-XGBoost,and TLBO-XGBoost.The models were evaluated based on the root mean square error(RMsE)metric to determine prediction accuracy.Among the tested combinations,the GA-XGBoost model produced the lowest RMSE for both cooling and heating load forecasting,indicating superior performance.These findings suggest that hybridizing XGBoost with metaheuristic algorithms can substantially improve forecasting accuracy.The consistent effectiveness of GA highlights its continued relevance in solving complex optimization tasks,aligning with the no free lunch theorem which states that no single algorithm performs best across all problems.展开更多
Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and resources.This study introduces a hybrid ap...Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and resources.This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN.The performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a battery.Specifically,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098.Additionally,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction inaccuracies.This study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.展开更多
This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power genera...This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.展开更多
This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified...This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified values to model uncertainty parameters in radial distribution network power flow analysis,whereas the Grey Wolf Optimizer(GWO)was employed to optimize photovoltaic distributed generation(PVDG)placement and sizing,aiming to minimize power losses and improve voltage deviations.Load uncertainties in the residential,commercial,and industrial sectors were modeled using triangular fuzzy membership functions derived from real-world data representing Malaysian urban loads.Simulations on the 33-bus distribution network validated the approach and demonstrated its effectiveness in handling fuzzy uncertainties across three load sectors.The findings showed that the proposed F-BFS-GWO method significantly reduced the total power losses and improved the voltage profiles.Under high load conditions,active power losses were reduced by approximately 28.04%in residential,46.06%in commercial,and 46.24%in industrial sectors at the highest membership degree in the fuzzy set,compared to the scenario without photovoltaic generation.The critical voltage magnitudes at the weakest bus under high-load conditions in the fuzzy set also improve significantly,reaching nearly 1.0 p.u.The main contributions of this work are the integration of fuzzy-logic within a BFS framework to manage multi-sector load uncertainties,coupled with a hybrid F-BFS-GWO algorithm that enhances system planning and optimization under the risk of uncertainty of photovoltaic generation and load demand.展开更多
基金supported by Sai Gon University under Fund(Grant No.TD2020-11).
文摘This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the CID card from the background.Next,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector.Finally,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques.
基金supported by Ministry of Higher Education Malaysia(MOHE)under Fundamental Research Grant Scheme(Grant No.:FRGS/1/2024/ICT02/UMP/02/).
文摘Accurate estimation of the state of charge(SoC)of lithium-ion batteries in electric vehicles(EVs)is crucial for optimizing performance,ensuring safety,and extending battery life.However,traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems,leading to inaccuracies that compromise the efficiency and reliability of electric vehicles.This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks.Specifically,teaching-learning based optimization(TLBO)is employed to optimize the weights and biases of the deep neural networks model,enhancing estimation accuracy.The proposed TLBO-deep neural networks(TLBO-DNNs)method was evaluated on a dataset of 1,064,000 samples,with performance assessed using mean absolute error(MAE),root mean square error(RMSE),and convergence value.The TLBO-DNNs model achieved an MAE of 3.4480,an RMSE of 4.6487,and a convergence value of 0.0328,outperforming other hybrid approaches.These include the barnacle mating optimizer-deep neural networks(BMO-DNNs)with an MAE of 5.3848,an RMSE of 7.0395,and a convergence value of 0.0492;the evolutionary mating algorithm-deep neural networks(EMA-DNNs)with an MAE of 7.6127,an RMSE of 11.2287,and a convergence value of 0.0536;and the particle swarm optimization-deep neural networks(PSO-DNNs)with an MAE of 4.3089,an RMSE of 5.9672,and a convergence value of 0.0345.Additionally,the TLBO-DNNs approach outperformed standalone models,including the autoregressive integrated moving average(ARIMA)model(MAE:14.3301,RMSE:7.0697)and support vector machines(MAE:6.0065,RMSE:8.0360).This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems in electric vehicles,contributing to improved efficiency and reliability in electric vehicle operations.
基金supported by the Ministry of Higher Education(MoHE)Malaysia under the Fundamental Research Grant Scheme(FRGS/1/2022/ICT04/UMP/02/1)Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)under Distinguished Research Grant(#RDU223003).
文摘This paper introduces an innovative approach to addressing a critical challenge in the electric vehicle(EV)industry—the accurate estimation of the state of charge(SOC)of EV batteries under real-world operating conditions.The electric mobility landscape is rapidly evolving,demanding more precise SOC estimation methods to improve range prediction accuracy and battery management.This study applies a Random Forest(RF)machine learning algorithm to improve SOC estimation.Traditionally,SOC estimation has posed a formidable challenge,particularly in capturing the complex dependencies between various parameters and SOC values during dynamic driving conditions.Previous methods,including the Extreme Learning Machine(ELM),have exhibited limitations in providing the accuracy and robustness required for practical EV applications.In contrast,this research introduces the RF model,for SOC estimation approach that excels in real-world scenarios.By leveraging decision trees and ensemble learning,the RF model forms resilient relationships between input parameters,such as voltage,current,ambient temperature,and battery temperatures,and SOC values.This unique approach empowers the model to deliver precise and consistent SOC estimates across diverse driving conditions.Comprehensive comparative analyses showcase the superiority of the RF over ELM.The RF model not only outperforms in accuracy but also demonstrates exceptional robustness and reliability,addressing the pressing needs of the EV industry.The results of this study not only underscore the potential of RF in advancing electric mobility but also suggest a promising integration of the SOC estimation approach into the battery management system of BMW i3.This integration holds the key to more efficient and dependable electric vehicle operations,marking a significant milestone in the ongoing evolution of EV technology.Importantly,the RF model demonstrates a lower Root Mean Squared Error(RMSE)of 5.902,8%compared to 6.312,7%for ELM,and a lower Mean Absolute Error(MAE)of 4.432,1%versus 5.111,2%for ELM across rigorous k-fold cross-validation testing,reaffirming its superiority in quantitative SOC estimation.
基金supported by Ministry of Higher Education Malaysia(MOHE)under Fundamental Research Grant Scheme(Grant No.FRGS/1/2024/ICT02/UMP/02/).
文摘Accurate forecasting of cooling and heating loads in buildings is vital for effective energy management,cost efficiency,and environmental sustainability.However,traditional forecasting models often face limitations in capturing the complex and non-linear characteristics of building energy consumption patterns.To address this challenge,this study proposes a hybrid predictive approach by integrating eXtreme gradient boosting(XGBoost)with eight metaheuristic optimization algorithms.The selected algorithms are ant colony optimization(ACO),barnacles mating optimizer(BMO),genetic algorithm(GA),gradient-based optimizer(GBO),hippopotamus optimization(HO),Kepler optimization algorithm(KOA),particle swarm optimization(PSO),and teachinglearning-based optimization(TLBO).Each metaheuristic was used to optimize the hyperparameters of the XGBoost model,resulting in the following hybrid models:ACO-XGBoost,BMO-XGBoost,GA-XGBoost,GBOXGBoost,HO-XGBoost,KOA-XGBoost,PSO-XGBoost,and TLBO-XGBoost.The models were evaluated based on the root mean square error(RMsE)metric to determine prediction accuracy.Among the tested combinations,the GA-XGBoost model produced the lowest RMSE for both cooling and heating load forecasting,indicating superior performance.These findings suggest that hybridizing XGBoost with metaheuristic algorithms can substantially improve forecasting accuracy.The consistent effectiveness of GA highlights its continued relevance in solving complex optimization tasks,aligning with the no free lunch theorem which states that no single algorithm performs best across all problems.
基金supported by Universiti Malaysia Pahang Al-Sultan Abdullah,grant number:RDU220379.
文摘Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and resources.This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN.The performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a battery.Specifically,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098.Additionally,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction inaccuracies.This study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.
基金supported by the Ministry of Higher Education Malaysia(MOHE)under Fundamental Research Grant Scheme(FRGS/1/2022/ICT04/UMP/02/1)Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)under Distinguished Research Grant(#RDU223003).
文摘This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases of deep neural networks (DNN) for forecasting the solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC power output using real solar power plant measurements spanning a 34-day period, recorded at 15-minute intervals. The intricate nonlinear relationship between solar irradiation, ambient temperature, and module temperature is captured for accurate prediction. Additionally, the paper conducts a comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Mating Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search Algorithm (HSA-DNN), DNN with Adaptive Moment Estimation optimizer (ADAM) and Nonlinear AutoRegressive with eXogenous inputs (NARX). The experimental results distinctly highlight the exceptional performance of EMA-DNN by attaining the lowest Root Mean Squared Error (RMSE) during testing. This contribution not only advances solar power forecasting methodologies but also underscores the potential of merging metaheuristic algorithms with contemporary neural networks for improved accuracy and reliability.
基金Universiti Malaysia Pahang Al-Sultan Abdullah and International Islamic University Malaysia for providing the research grant RDU223219.
文摘This research presented a novel framework of fuzzy-backward/forward sweep(F-BFS)power flow to address uncertainties in radial distribution networks with photovoltaic generation.The F-BFS framework integrated fuzzified values to model uncertainty parameters in radial distribution network power flow analysis,whereas the Grey Wolf Optimizer(GWO)was employed to optimize photovoltaic distributed generation(PVDG)placement and sizing,aiming to minimize power losses and improve voltage deviations.Load uncertainties in the residential,commercial,and industrial sectors were modeled using triangular fuzzy membership functions derived from real-world data representing Malaysian urban loads.Simulations on the 33-bus distribution network validated the approach and demonstrated its effectiveness in handling fuzzy uncertainties across three load sectors.The findings showed that the proposed F-BFS-GWO method significantly reduced the total power losses and improved the voltage profiles.Under high load conditions,active power losses were reduced by approximately 28.04%in residential,46.06%in commercial,and 46.24%in industrial sectors at the highest membership degree in the fuzzy set,compared to the scenario without photovoltaic generation.The critical voltage magnitudes at the weakest bus under high-load conditions in the fuzzy set also improve significantly,reaching nearly 1.0 p.u.The main contributions of this work are the integration of fuzzy-logic within a BFS framework to manage multi-sector load uncertainties,coupled with a hybrid F-BFS-GWO algorithm that enhances system planning and optimization under the risk of uncertainty of photovoltaic generation and load demand.
