A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets p...A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.展开更多
This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the sur...This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.展开更多
利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,...利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,对检测方法的适用性提出了更高的要求。针对以上问题,本文提出基于改进PatchSVDD (Patch-level Support Vector Data Description)模型的田间异常区域检测方法,该方法仅使用田间正常区域的标注信息,即可对田间异常区域进行检测和定位。首先,改进方法引入不相邻图像块之间的边界损失函数,从而提升了正常与异常样本边界的判别性,改进了检测的鲁棒性;第二,引入外部记忆组件,通过压缩存储正常样本特征,从而在保证检测精度的基础上有效减少了测试阶段的时间和空间消耗;第三,构建了包含杂草簇、种植缺失、障碍物、双倍种植和积水共5个异常类型的田间异常数据集。本文方法在平均检测AUC(Area Under Curve)值和平均定位AUC值上分别达到了96.9%和94.6%,相比于原算法分别提升1.2%和1.6%,从而验证了方法的有效性。展开更多
为掌握矿山地质环境问题的影响程度,以调查区多年遥感图像为信息源,通过图像识别技术,解译矿山地物信息,提取量化评价数据;然后结合改进序关系法与拉开档次法,最终建立基于直觉模糊集(Intuitionistic Fuzzy Set,IFS)-理想解法(Technique...为掌握矿山地质环境问题的影响程度,以调查区多年遥感图像为信息源,通过图像识别技术,解译矿山地物信息,提取量化评价数据;然后结合改进序关系法与拉开档次法,最终建立基于直觉模糊集(Intuitionistic Fuzzy Set,IFS)-理想解法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)的矿山地质环境综合评价模型。最后以陕西省富平县曹村镇为实例,基于遥感信息提取数据的分析,并结合研究区矿山开发与自然地理情况,构建矿山地质环境评价指标体系,应用所提出的模型对研究区的矿山进行地质环境评价,得出以矿山为单元的评价等级分区。结果表明,所提出的方法能够客观地反映研究区矿山地质环境现状,能为政府进一步制定矿山地质环境保护与治理政策提供合理参考。展开更多
In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed d...In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification.Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification.To assess the performance of discretization methods this article adopts three indicators,which are the compression capability indicator(CCI),consistency indicator(CI),and number of the cut points(NCP).An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods.To investigate the effectiveness of our method,this article applies three discretization methods of the Entropy/MDL,Naive,and SemiNaive to a TM image and three indicators for these discretization methods are then calculated.After comparing the three indicators and the classification accuracies of the discretized remotely sensed images,it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.展开更多
随着电力系统的发展和智能化技术的应用,对高压互感器的测试和监测要求越来越高。传统的有线测试方式存在放线收线烦琐、效率低下和受环境限制等问题。采用远距离无线电(Long Range Radio,LoRa)无线技术的高压互感器智能测试装置,通过...随着电力系统的发展和智能化技术的应用,对高压互感器的测试和监测要求越来越高。传统的有线测试方式存在放线收线烦琐、效率低下和受环境限制等问题。采用远距离无线电(Long Range Radio,LoRa)无线技术的高压互感器智能测试装置,通过无线通信实现对高压互感器的远程监测和测试,提高测试效率和可靠性。介绍了高压互感器的基本原理和测试方法,详细阐述了LoRa无线技术的特点和工作原理。在此基础上,设计了一种基于LoRa无线技术的高压互感器智能测试装置,并进行了实验验证。实验结果表明,该装置能够稳定地实现对高压互感器的测试和监测,具有较高的可靠性和实用性。研究成果对于提高电力系统的测试效率和可靠性具有一定的实际应用价值。展开更多
基金Sponsored by the National Pandeng Project(Grant No.PD9521907)
文摘A type of remote monitoring and diagnosis system is brought forward which based on Matlab Web Server.Firstly,wavelet packet decomposition is introduced to acquire energy features of which reflect hydrogenerator sets performance to be Feature Parameter.Then these Feature Parameters can be adopted as BP Neural Network input variable to realize fault diagnosis.Most of all,it is the first time to adopt Matlab Web Server to hydro-generator sets faults diagnosis field to implement distributed remote monitoring and diagnosis system.Therefore,remote diagnosis application is independent from the OS used on server side.There is no need for software maintenance by clients.And clients can finish remote diagnosis by Web Browser and without installation of Matlab-software.Client users can monitor and diagnose hydro-generator sets by Browser.Finally,further research work is pointed out such as hydro-generator sets fault modeling,accelerating BP Neural Network learning speed and convergence property,improving data transfer speed of Matlab Web Server to meet the needs of real-time diagnosis for hydropower generator sets.
文摘This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.
文摘利用无人机遥感技术对农田进行监测并及时发现田间异常对保证农业生产具有重要意义。目前田间异常区域检测需要标注大量的正常与异常样本。然而,异常样本在整个农田区域中占比过小且无法充分收集。特别是农田异常的多样性和不可预知性,对检测方法的适用性提出了更高的要求。针对以上问题,本文提出基于改进PatchSVDD (Patch-level Support Vector Data Description)模型的田间异常区域检测方法,该方法仅使用田间正常区域的标注信息,即可对田间异常区域进行检测和定位。首先,改进方法引入不相邻图像块之间的边界损失函数,从而提升了正常与异常样本边界的判别性,改进了检测的鲁棒性;第二,引入外部记忆组件,通过压缩存储正常样本特征,从而在保证检测精度的基础上有效减少了测试阶段的时间和空间消耗;第三,构建了包含杂草簇、种植缺失、障碍物、双倍种植和积水共5个异常类型的田间异常数据集。本文方法在平均检测AUC(Area Under Curve)值和平均定位AUC值上分别达到了96.9%和94.6%,相比于原算法分别提升1.2%和1.6%,从而验证了方法的有效性。
文摘为掌握矿山地质环境问题的影响程度,以调查区多年遥感图像为信息源,通过图像识别技术,解译矿山地物信息,提取量化评价数据;然后结合改进序关系法与拉开档次法,最终建立基于直觉模糊集(Intuitionistic Fuzzy Set,IFS)-理想解法(Technique for Order Preference by Similarity to an Ideal Solution,TOPSIS)的矿山地质环境综合评价模型。最后以陕西省富平县曹村镇为实例,基于遥感信息提取数据的分析,并结合研究区矿山开发与自然地理情况,构建矿山地质环境评价指标体系,应用所提出的模型对研究区的矿山进行地质环境评价,得出以矿山为单元的评价等级分区。结果表明,所提出的方法能够客观地反映研究区矿山地质环境现状,能为政府进一步制定矿山地质环境保护与治理政策提供合理参考。
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.40971222)the National High Technology Research and Development Program of China(Grant No.2006AA120106)。
文摘In recent years,the rough set(RS)method has been in common use for remotesensing classification,which provides one of the techniques of information extraction for Digital Earth.The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification.Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification.To assess the performance of discretization methods this article adopts three indicators,which are the compression capability indicator(CCI),consistency indicator(CI),and number of the cut points(NCP).An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods.To investigate the effectiveness of our method,this article applies three discretization methods of the Entropy/MDL,Naive,and SemiNaive to a TM image and three indicators for these discretization methods are then calculated.After comparing the three indicators and the classification accuracies of the discretized remotely sensed images,it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.
文摘随着电力系统的发展和智能化技术的应用,对高压互感器的测试和监测要求越来越高。传统的有线测试方式存在放线收线烦琐、效率低下和受环境限制等问题。采用远距离无线电(Long Range Radio,LoRa)无线技术的高压互感器智能测试装置,通过无线通信实现对高压互感器的远程监测和测试,提高测试效率和可靠性。介绍了高压互感器的基本原理和测试方法,详细阐述了LoRa无线技术的特点和工作原理。在此基础上,设计了一种基于LoRa无线技术的高压互感器智能测试装置,并进行了实验验证。实验结果表明,该装置能够稳定地实现对高压互感器的测试和监测,具有较高的可靠性和实用性。研究成果对于提高电力系统的测试效率和可靠性具有一定的实际应用价值。
