This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization a...This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.展开更多
For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best coo...For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.展开更多
Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutio...Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'.展开更多
现今关于驾驶员路怒情绪识别方法中语音特性分析相对较少,该研究以路怒情绪为研究对象,利用模拟驾驶系统建立数据集,通过分析驾驶员语音的频谱特征,将时域中短时能量及短时过零率特征参数和改进Mel频率倒谱系数(Mel frequency cepstral ...现今关于驾驶员路怒情绪识别方法中语音特性分析相对较少,该研究以路怒情绪为研究对象,利用模拟驾驶系统建立数据集,通过分析驾驶员语音的频谱特征,将时域中短时能量及短时过零率特征参数和改进Mel频率倒谱系数(Mel frequency cepstral coefficients,MFCC)特征参数融合构成特征参数向量,利用萤火虫算法(firefly algorithm,FA)优化PNN神经网络(probabilistic neural networks)并构建识别模型,实现驾驶员路怒情绪的识别。实验结果表明,在相同神经网络下,改进MFCC融合特征提取方法相比传统MFCC特征提取方法具有更好的抗噪性。同时,FA-PNN模型的识别准确率为93.0%,相比传统PNN模型提高了11个百分点;F1-Score值为0.9328,提高了0.1047。该研究论证了语音信号处理技术对驾驶员路怒情绪识别的可行性,为汽车主动安全驾驶预警研究提供了新方法。展开更多
基金This research was funded by the Faculty of Engineering,King Mongkut’s University of Technology North Bangkok.Contract No.ENG-NEW-66-39.
文摘This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.
基金supported by the National Natural Science Foundation of China(61571149)the Special China Postdoctoral Science Foundation(2015T80325)+2 种基金the Heilongjiang Postdoctoral Fund(LBH-Z13054)the China Scholarship Council and the Fundamental Research Funds for the Central Universities(HEUCFP201772HEUCF160808)
文摘For acquiring high energy efficiency and the maximal throughput, a new time slot structure is designed for energy harvesting(EH) cognitive radio(CR). Considering the CR system with EH and cooperative relay, a best cooperative mechanism(BCM)is proposed for CR with EH. To get the optimal estimation performance, a quantum fireworks algorithm(QFA) is designed to resolve the difficulties of maximal throughput and EH, and the proposed cooperative mechanism is called as QFA-BCM. The proposed QFA combines the advantages of quantum computation theory with the fireworks algorithm(FA). Thus the QFA is able to obtain the optimal solution and its convergence performance is proved. By using the new cooperation mechanism and computing algorithm, the proposed QFA-BCM method can achieve comparable maximal throughput in the new timeslot structure. Simulation results have proved that the QFA-BCM method is superior to previous non-cooperative and cooperative mechanisms.
基金supported in part by the National Natural Science Foundation of China(62071230,62061146002)the Natural Science Foundation of Jiangsu Province(BK20211567)the Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia(FP-147-43)。
文摘Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'.
