Traditional multi-class classification methods based on Fisher kernel combine generative models such as Gaussian mixture models(GMMs)of all the classes together.However,the combination generates high dimensional featu...Traditional multi-class classification methods based on Fisher kernel combine generative models such as Gaussian mixture models(GMMs)of all the classes together.However,the combination generates high dimensional feature vectors and leads to large computation.In this paper,a new classification method is proposed.This method adopts an intelligent feature space selection strategy by clustering similar Gaussian mixtures in order to reduce the feature dimensions.Audio classification experiments show that the proposed method is more accurate and effective with less computation compared with traditional methods.展开更多
A typical approach to describe an image in loop closure detection for visual SLAM is to extract a set of local patch descriptors and encode them into a co-occurrence vector.The most common patch encoding strategy is k...A typical approach to describe an image in loop closure detection for visual SLAM is to extract a set of local patch descriptors and encode them into a co-occurrence vector.The most common patch encoding strategy is known as bag-of-visual-words(BoVW)representation,which consists of clustering the local descriptors into visual vocabulary.The distinctiveness of images is difficult to represent since most of them contain similar texture information,which may lead to false positive results.In this paper,the vocabulary is used as a whole by adopting the Fisher kernel(FK)framework.The new representation describes the image as the gradient vector of the likelihood function.The efficiently computed vectors can be compressed with a minimal loss of accuracy using product quantization and perform well in the task of loop closure detection.The proposed method achieves a higher recall rate with 100%precision in loop closure detection compared with state-of-the-art methods,and the detection on bidirectional loops is also enhanced.vSLAM systems may perceive the environment more efficiently by constructing a globally consistent map with the proposed loop closure detection method,which is potentially valuable for applications such as autonomous driving.展开更多
Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenome...Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.展开更多
Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust min...Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.展开更多
工业控制系统面临的威胁增多,为做到主动防御,以及更加准确地识别入侵数据类型,基于工控蜜罐的部署环境,设计一种模型,用来识别入侵数据具体类型。首先对捕获的数据进行核主成分分析(Kernel Principal Component Analysis,KPCA)降维,然...工业控制系统面临的威胁增多,为做到主动防御,以及更加准确地识别入侵数据类型,基于工控蜜罐的部署环境,设计一种模型,用来识别入侵数据具体类型。首先对捕获的数据进行核主成分分析(Kernel Principal Component Analysis,KPCA)降维,然后利用Fisher算法对处理后的数据进行分类,如果判定为异常类,则再利用BP神经网络(Back Propagation neural network)进行二次判别,确定具体的入侵类别。实验结果表明,该方法检测率可达到95%,可以较好地对数据进行分类,判定具体的入侵类型。展开更多
黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲...黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲醇为溶剂制备四种不同浓度的黄曲霉毒素溶液，并将其逐一滴在等量的4组共120粒玉米颗粒表面，以未处理的30粒洁净玉米作为一组对照样本，将大小、形状相似的150个样品随机分为训练集103个，验证集47个；对获取的400～1000 nm波段范围内的高光谱图像，先进行标准正态变量变换（standard normal variate transformation ，SNV）预处理，然后引入基于 Fisher判别最小误判率的方法选择最优波长，并以所选波长作为Fisher判别分析法的输入建立判别模型，对玉米颗粒表面不同浓度的黄曲霉毒素进行识别，最后对模型判别正确率进行了验证。结果表明，选取四个最优波长（812.42，873.00，900.36和965.00 nm ）时Fisher判别分析模型对训练集与验证集的准确率分别为87.4％和80.9％。该方法为含黄曲霉毒素玉米颗粒便携式检测仪器的开发，以及对田间霉变玉米自然代谢产生毒素的检测奠定了技术基础。展开更多
基金supported by the National Natural Science Foundation of China (Grant No.60705019)the National High Technology Research and Development Program of China (Nos.2006AA010102 and 2007AA01Z417)NOKIA project,and the 111 Project (No.B08004).
文摘Traditional multi-class classification methods based on Fisher kernel combine generative models such as Gaussian mixture models(GMMs)of all the classes together.However,the combination generates high dimensional feature vectors and leads to large computation.In this paper,a new classification method is proposed.This method adopts an intelligent feature space selection strategy by clustering similar Gaussian mixtures in order to reduce the feature dimensions.Audio classification experiments show that the proposed method is more accurate and effective with less computation compared with traditional methods.
文摘A typical approach to describe an image in loop closure detection for visual SLAM is to extract a set of local patch descriptors and encode them into a co-occurrence vector.The most common patch encoding strategy is known as bag-of-visual-words(BoVW)representation,which consists of clustering the local descriptors into visual vocabulary.The distinctiveness of images is difficult to represent since most of them contain similar texture information,which may lead to false positive results.In this paper,the vocabulary is used as a whole by adopting the Fisher kernel(FK)framework.The new representation describes the image as the gradient vector of the likelihood function.The efficiently computed vectors can be compressed with a minimal loss of accuracy using product quantization and perform well in the task of loop closure detection.The proposed method achieves a higher recall rate with 100%precision in loop closure detection compared with state-of-the-art methods,and the detection on bidirectional loops is also enhanced.vSLAM systems may perceive the environment more efficiently by constructing a globally consistent map with the proposed loop closure detection method,which is potentially valuable for applications such as autonomous driving.
基金Supported by the National Natural Science Foundation of China(61273070)the Foundation of Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.
基金Project(51874353)supported by the National Natural Science Foundation of ChinaProject(GCX20190898Y)supported by Mittal Student Innovation Project,China。
文摘Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.
文摘黄曲霉毒素是广泛存在于玉米中且具有剧毒的一种代谢产物，以美国农业部农业研究署（USDA-ARS） Toxicology and Mycotoxin Research Unit提供的2010年先锋玉米为研究对象，验证了高光谱成像技术对玉米中黄曲霉毒素检测的可行性。以甲醇为溶剂制备四种不同浓度的黄曲霉毒素溶液，并将其逐一滴在等量的4组共120粒玉米颗粒表面，以未处理的30粒洁净玉米作为一组对照样本，将大小、形状相似的150个样品随机分为训练集103个，验证集47个；对获取的400～1000 nm波段范围内的高光谱图像，先进行标准正态变量变换（standard normal variate transformation ，SNV）预处理，然后引入基于 Fisher判别最小误判率的方法选择最优波长，并以所选波长作为Fisher判别分析法的输入建立判别模型，对玉米颗粒表面不同浓度的黄曲霉毒素进行识别，最后对模型判别正确率进行了验证。结果表明，选取四个最优波长（812.42，873.00，900.36和965.00 nm ）时Fisher判别分析模型对训练集与验证集的准确率分别为87.4％和80.9％。该方法为含黄曲霉毒素玉米颗粒便携式检测仪器的开发，以及对田间霉变玉米自然代谢产生毒素的检测奠定了技术基础。
