Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr...Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.展开更多
The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which ...The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.展开更多
This study develops a hybrid photovoltaic-thermoelectric generator(PV-TEG)system to reduce dependence on fossil fuels and promote sustainable energy generation.However,the inherent randomness of real-world operational...This study develops a hybrid photovoltaic-thermoelectric generator(PV-TEG)system to reduce dependence on fossil fuels and promote sustainable energy generation.However,the inherent randomness of real-world operational environments introduces challenges such as partial shading conditions and uneven temperature distribution within PV and TEG modules.These factors can significantly degrade system performance and reduce energy conversion efficiency.To tackle these challenges,this paper proposes an advanced optimal power extraction strategy and develops a chaotic RIME(c-RIME)optimizer to achieve dynamic maximum power point tracking(MPPT)across varying operational scenarios.Compared with existing methods,this approach enhances the effectiveness and robustness of MPPT,particularly under complex working conditions.Furthermore,the study incorporates a comprehensive assessment framework that integrates both technical performance and sustainability considerations.A broader range of realistic operational scenarios are analyzed,with case studies utilizing on-site data from Hong Kong and Ningxia for technical and environmental evaluations.Simulation results reveal that the c-RIME-based MPPT technique can effectively enhance system energy output with smaller power fluctuations than existing methods.For instance,under startup testing conditions,the c-RIME optimizer achieves energy output increase by up to 126.67% compared to the arithmetic optimization algorithm.展开更多
基金the 2023 Liaoning Institute of Science and Technology Doctoral Program Launch Fund(2307B29),covering aspects such as data collection and publication of the paper。
文摘Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
基金supported in part by the Natural Science Foundation of Zhejiang Province(LZ22F020005)National Natural Science Foundation of China(62076185,62301367).
文摘The persistently high incidence of breast cancer emphasizes the need for precise detection in its diagnosis.Computer-aided medical systems are designed to provide accurate information and reduce human errors,in which accurate and effective segmentation of medical images plays a pivotal role in improving clinical outcomes.Multilevel Threshold Image Segmentation(MTIS)is widely favored due to its stability and straightforward implementation.Especially when dealing with sophisticated anatomical structures,high-level thresholding is a crucial technique in identifying fine details.To enhance the accuracy of complex breast cancer image segmentation,this paper proposes an improved version of RIME optimizer EECRIME,denoted as the double Enhanced solution quality Crisscross RIME algorithm.The original RIME initially conducts an efficient optimization to target promising solutions.The double-enhanced solution quality(EESQ)mechanism is proposed for thorough exploitation without falling into local optimum.In contrast,the crisscross operations perform a further local exploration of the generated feasible solutions.The performance of EECRIME is verified with basic and advanced algorithms on IEEE CEC2017 benchmark functions.Furthermore,an EECRIME-based MTIS method in combination with Kapur’s entropy is applied to segment breast Infiltrating Ductal Carcinoma(IDC)histology images.The results demonstrate that the developed model significantly surpasses its competitors,establishing it as a practical approach for complex medical image processing.
基金supported by the National Natural Sci-ence Foundation of China(No.62263014)Yunnan Provincial Basic Research Project(No.202401AT070344 and No.202301AT070443).
文摘This study develops a hybrid photovoltaic-thermoelectric generator(PV-TEG)system to reduce dependence on fossil fuels and promote sustainable energy generation.However,the inherent randomness of real-world operational environments introduces challenges such as partial shading conditions and uneven temperature distribution within PV and TEG modules.These factors can significantly degrade system performance and reduce energy conversion efficiency.To tackle these challenges,this paper proposes an advanced optimal power extraction strategy and develops a chaotic RIME(c-RIME)optimizer to achieve dynamic maximum power point tracking(MPPT)across varying operational scenarios.Compared with existing methods,this approach enhances the effectiveness and robustness of MPPT,particularly under complex working conditions.Furthermore,the study incorporates a comprehensive assessment framework that integrates both technical performance and sustainability considerations.A broader range of realistic operational scenarios are analyzed,with case studies utilizing on-site data from Hong Kong and Ningxia for technical and environmental evaluations.Simulation results reveal that the c-RIME-based MPPT technique can effectively enhance system energy output with smaller power fluctuations than existing methods.For instance,under startup testing conditions,the c-RIME optimizer achieves energy output increase by up to 126.67% compared to the arithmetic optimization algorithm.
