An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformat...An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.展开更多
Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike s...Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network （PCAN） and normal boundary response （NBR） self-organizing mapping （SOM） net- work classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals contain- ing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.展开更多
Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present...Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.展开更多
The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency a...The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency and power consumption over the conventional wireless NoC. Therefore, this paper studies how to optimize the network performance and power consumption by giving the packet-switching fabric and traffic pattern of each application. Compared with the conventional method of wire-linked, which adds wireless transceivers by using the genetic algorithm （GA）, the proposed maximal declining sorting algorithm （MDSA） can effectively reduce time consumption by as much as 20.4% to 35.6%. We also evaluate the power consumption and configuration time to prove the effective of the proposed algorithm.展开更多
面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提...面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。展开更多
基金Supported by the National Natural Science Foundation of China(64601500)
文摘An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.
基金supported by the National Natural Science Foundation of China(60971084,61272049)the Science Foundation for the Excellent Youth Scholars of Ministry of Education of China (20091102120046)
文摘Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network （PCAN） and normal boundary response （NBR） self-organizing mapping （SOM） net- work classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals contain- ing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.
文摘Silicon Carbide (SiC) machining by traditional methods with regards to its high hardness is not possible. Electro Discharge Machining, among non-traditional machining methods, is used for machining of SiC. The present work is aimed to optimize the surface roughness and material removal rate of electro discharge machining of SiC parameters simultaneously. As the output parameters are conflicting in nature, so there is no single combination of machining parameters, which provides the best machining performance. Artificial neural network (ANN) with back propagation algorithm is used to model the process. A multi-objective optimization method, non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time (Toff) on electric discharge machining of SiC are considered. Experiments have been conducted over a wide range of considered input parameters for training and verification of the model. Testing results demonstrate that the model is suitable for predicting the response parameters. A pareto-optimal set has been predicted in this work.
基金supported by the National Natural Science Foundation of China under Grant No.61376024 and No.61306024the Natural Science Foundation of Guangdong Province under Grant No.S2013040014366Basic Research Program of Shenzhen No.JCYJ20140417113430642 and JCYJ20140901003939020
文摘The networks-on-chip （NoC） communication has an increasingly larger impact on the system power consumption and performance. Emerging technologies, like surface wave, are believed to have lower transmission latency and power consumption over the conventional wireless NoC. Therefore, this paper studies how to optimize the network performance and power consumption by giving the packet-switching fabric and traffic pattern of each application. Compared with the conventional method of wire-linked, which adds wireless transceivers by using the genetic algorithm （GA）, the proposed maximal declining sorting algorithm （MDSA） can effectively reduce time consumption by as much as 20.4% to 35.6%. We also evaluate the power consumption and configuration time to prove the effective of the proposed algorithm.
文摘面向规模化屋顶光伏接入配电网急需进行有序控制的现状,提出了一种考虑源荷匹配特性的屋顶光伏并网的综合排序方法。首先,综合考虑负荷与光伏协调特性等需求,设计了兼顾光伏业主侧、电网侧和用电用户侧需求的综合评价指标体系;其次,提出了一种基于改进层次分析法(improved analytic hierarchy process,IAHP)-改进反熵权法(improved anti-entropy method,IAM)-博弈组合赋权法-改进逼近理想解法(improved technique for order preference by similarity to ideal solution,improved TOPSIS)的评价方法,先根据改进的层次分析法进行主观赋权,同时考虑到指标间的相关性和波动性,采用所提改进反熵权法确定各指标的客观权重,再基于博弈论思想获取综合权重,以确保权重的合理性,然后,为提高各方案的整体区分度,采用所提改进逼近理想解法对屋顶光伏接入方案进行排序。最后,以IEEE 33节点系统为例,在MATLAB平台验证了所提指标体系和排序方法的有效性。
