In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Us...In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.展开更多
The purpose of this paper is to establish sev eral identities containing Gaussian binomial coefficient. These results generali ze several Mercier's results. Key words:Gaussian binomial coefficient; identity; the ...The purpose of this paper is to establish sev eral identities containing Gaussian binomial coefficient. These results generali ze several Mercier's results. Key words:Gaussian binomial coefficient; identity; the functio n A(T,T 2,...,T n)culating eige nvalues of auto-correlation matrix of the physical control force of actuators. T he optimization algorithm calculating the optimal actuator placement is then put forward via the minimization of an energy criterion, which is chosen as the con trol index. Numerical examples show the effectiveness of the proposed method.展开更多
An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challe...An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.展开更多
This work proposes a method to concurrently calibrate multiple acoustic speeds in different mediums with a photoacoustic（PA） and ultrasound（US） dual-modality imaging system. First, physical infrastructure informat...This work proposes a method to concurrently calibrate multiple acoustic speeds in different mediums with a photoacoustic（PA） and ultrasound（US） dual-modality imaging system. First, physical infrastructure information of the target is acquired through a US image. Then, we repeatedly build PA images around a special target to yield the best focused result by dynamically updating the acoustic speeds in a different medium of the target.With these correct acoustic propagation velocities in the according mediums, we can effectively optimize the PA image quality as the experiments proved, which might benefit future research in biomedical imaging science.展开更多
文摘In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.
文摘The purpose of this paper is to establish sev eral identities containing Gaussian binomial coefficient. These results generali ze several Mercier's results. Key words:Gaussian binomial coefficient; identity; the functio n A(T,T 2,...,T n)culating eige nvalues of auto-correlation matrix of the physical control force of actuators. T he optimization algorithm calculating the optimal actuator placement is then put forward via the minimization of an energy criterion, which is chosen as the con trol index. Numerical examples show the effectiveness of the proposed method.
基金supported by the Action Programme for Cultivation of Young and Middle-aged Teachers in Universities in Anhui Province(YQYB2023030),Chinathe Supporting Programme for Outstanding Young Talents in Colleges and Universities of Anhui Provincial Department of Education(gxyq2022068),China+1 种基金the Huainan Normal University Scientific Research Project(2023XJZD016),Chinathe Key Projects of Huainan Normal University(2024XJZD012),China.
文摘An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.
基金supported by National Natural Science Foundation of China(No.61201425)the Natural Science Foundation of Jiangsu Province(No.BK20131280)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘This work proposes a method to concurrently calibrate multiple acoustic speeds in different mediums with a photoacoustic（PA） and ultrasound（US） dual-modality imaging system. First, physical infrastructure information of the target is acquired through a US image. Then, we repeatedly build PA images around a special target to yield the best focused result by dynamically updating the acoustic speeds in a different medium of the target.With these correct acoustic propagation velocities in the according mediums, we can effectively optimize the PA image quality as the experiments proved, which might benefit future research in biomedical imaging science.
