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Symposium on Visualizing and Quantifying Drug Distribution in Tissue IV held at SPIE BiOS Conference

作者： O'Connor, Sean P. Gil, Eddie M. Keppler, Mark A. Scully, Marlan O. Yakovlev, Vladislav V. Texas A&M Univ Dept Phys & Astron College Stn TX 77843 USA Texas A&M Univ Dept Biomed Engn College Stn TX 77843 USA SAIC Corp Jbsa Ft Sam Houston TX 78234 USA Baylor Univ Dept Phys Waco TX 76798 USA

ISBN: (纸本)9781510632011;9781510632028

Scanning confocal Raman spectroscopy was applied for detecting and identifying topically applied ocular pharmaceuticals on rabbit corneal tissue Raman spectra for Cyclosporin A, Difluprednate, and Dorzolamide were acquired together with Raman spectra from rabbit corneas with an unknown amount of applied drug. kernel principle component analysis (KPCA) was then used to explore a transform that can describe the acquired set of Raman spectra. Using this transform, we observe some spectral similarity between cornea spectra and Cyclosporin A, with little similarity to Dorzolamide and Difluprednate. Further investigation is needed to identify why these differences occur.

关键词： Chemical sensing Cornea Drug detection Eye kernel principle component analysis Ocular tissue Raman imaging Raman spectroscopy

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SAR image noise suppression of BEMD by the kernel principle component analysis

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IET IMAGE PROCESSING 2021年第1期15卷 155-165页

作者： Huang, Changjun Zhou, Xinghua Hu, Jiyuan Zhou, Qingshan Hunan City Univ Sch Municipal & Surveying Engn Yiyang 413000 Peoples R China Key Lab Key Technol Digital Urban Rural Spatial P Yiyang 413000 Peoples R China Zhejiang Inst Hydraulice & Estuary Hangzhou 310008 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

In the process of synthetic aperture radar image noise suppression by the bi-dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD-kernel principal component analysis (BEMD-KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD-KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image.

关键词： Radar equipment, systems and applications bi-dimensional empirical mode decomposition algorithm edge effect image enhancement BEMD-KPCA method BEMD decomposition process BEMD operation decomposed components reliable decomposition results Integral transforms synthetic aperture radar image noise suppression Optical, image and video signal processing detail information speckle principal component analysis wavelet transforms good effect improved BEMD-kernel principal component analysis denoised image traditional BEMD method kernel principle component analysis synthetic aperture radar BEMDKPCA algorithm good capability image denoising radar imaging speckle noise

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Compound fault diagnosis for cooling dehumidifier based on RBF neural network improved by kernel principle component analysis and adaptive genetic algorithm

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SOFT COMPUTING 2023年第3期27卷 1599-1613页

作者： Gao, Yunguang Ma, Changlin Sheng, An Hunan Trinity Ind Vocat & Tech Coll Changsha Changsha County Peoples R China Xian Res Inst Hitech Xian Peoples R China

Developing fault diagnosis for the cooling dehumidifier is very important for improving the equipment reliability and saving energy consumption. This paper mainly studies and explores the compound fault diagnosis for the cooling dehumidifier. Firstly, the dehumidifier data acquisition system is built, which can be applied to the data acquisition, work status simulation and fault diagnosis. Secondly, a compound fault diagnosis model based on radial basis function neural network (RBFNN) improved by kernel principle component analysis (KPCA) and adaptive genetic algorithm (AGA) is proposed. Aiming at the problems that the selection of RBF width depends on expert experience and knowledge, the network structure scale is large or the training speed is slow in the conventional RBFNN models;on the one hand, AGA and K-means clustering algorithm are employed to automatically optimize the RBF width, the number of hidden layer neurons and the neuron centers, which guarantees the model has small structure and fast running speed on the premise of sufficient output precision, and the local optimization problem of K-means clustering algorithm is well suppressed by AGA group calculation;this also ensures the performance of RBFNN;on the other hand, KPCA is employed to reduce the dimension of the model input data, which not only effectively extract the nonlinear features, but also further simplify the network structure. Finally, the proposed method is validated and compared with the conventional models. The results show that this proposed model can not only be effectively applied to the dehumidifier compound fault diagnosis, but also has prominent application advantages.

关键词： Cooling dehumidifier Compound fault RBF neural network kernel principle component analysis Adaptive genetic algorithm

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Radar emitter signals classification using kernel principle component analysis and fuzzy support vector machines

Radar emitter signals classification using kernel principle ...

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5th International Conference on Wavelet analysis and Pattern Recognition

作者： Ren, Ming-Qiu Zhu, Yuan-Qing Mao, Yan Han, Jun Wuhan Radar Acad Dept Elect Countermeasures Wuhan 430019 Peoples R China

ISBN: (纸本)9781424410651

In this paper, a novel approach based on QTFDs and kernel principle component analysis (KPCA) is Proposed to extract features of radar emitter signals. Then, these discriminative and low dimensional features achieved were fed to a Support Vector Machines (SVMs) based on FCM (fuzzy c-means) clustering for multi-class pattern recognition. Experimental results show that the proposed methodology, was efficient for the different complex radar emitter signals defection and classification.

关键词： radar emitter signal classification kernel principle component analysis FCM clustering support vector machines

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Improving Robustness of Gaussian Process-based Inferential Control System using kernel principle component analysis 13

Improving Robustness of Gaussian Process-based Inferential C...

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13th International Conference on Machine Learning and Applications (ICMLA)

作者： Abusnina, Ali Kudenko, Daniel Roth, Rolf Univ York Comp Sci York N Yorkshire England Evonik Ind AG Proc Technol & Engn Marl Germany

ISBN: (纸本)9781479974153

The plausibility and robustness of an inferential control system entirely depend on the prediction accuracy of the estimator used as the feedback element. This paper is based on a previously proposed Gaussian process inferential controller that employs Gaussian process soft sensor as an estimator. The paper enhances the robustness and the reliability of the control system, particularly, during sensor input failures. The contribution of the paper is i) alleviating the affect of the failure on the prediction accuracy of feedback element (soft sensor) and thus improving the robustness of the overall control system. ii) Hybridising kernel Principal component analysis with Gaussian process Inferential Control System to achieve this robustness during all process operating conditions. The paper empirically shows the effectiveness and the plausibility of the processed hybrid system on a simulated chemical reactor process.

关键词： Gaussian processes PI control feedback principal component analysis reliability stability Gaussian process soft sensor Gaussian process-based inferential control system PI controller chemical reactor process control system reliability control system robustness estimator feedback element kernel principle component analysis sensor input failures Control systems kernel Principal component analysis Process control Robustness Vectors principal components analysis Gaussian processes feedback element PI control control systems Robustness Process control kernel Feedback Seeds Gauss

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A fault diagnosis methodology for nuclear power plants based on kernel principle component analysis and quadratic support vector machine

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ANNALS OF NUCLEAR ENERGY 2023年 181卷

作者： Wang, Yiqing Sun, Peiwei Xi An Jiao Tong Univ Sch Nucl Sci & Technol Shaanxi Key Lab Adv Nucl Energy & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

The safety of nuclear power plant operation can be improved efficiently by developing the fault diagnosis ap-plications. However, adopting single technique may restrict the flexibility and accuracy of the fault diagnosis module. This paper proposes a new fault diagnosis methodology for nuclear power plants based on kernel principle component analysis (KPCA) and Quadratic Support Vector Machine (QSVM). The nonlinearity be-tween variables is analyzed and the importance of variables are quantified by standard deviations. Sensor faults are distinguished from system faults using contribution charts. In pre-process stage of classification, five normalization modes are compared to achieve better performance of classifiers. For fault identification, samples are transformed to QSVM classifier and the diagnosis results are compared with other classifiers. To further evaluate the performance, three typical case conditions are tested. QSVM outperforms other classifiers on all types of system faults. The findings are beneficial for fault diagnosis of nuclear power plant.

关键词： Fault diagnosis kernel principle component analysis Quadratic support vector machine Normalization mode Classifier

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RADAR EMITTER SIGNALS CLASSIFICATION USING kernel principle component analysis AND FUZZY SUPPORT VECTOR MACHINES

RADAR EMITTER SIGNALS CLASSIFICATION USING KERNEL PRINCIPLE ...

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2007 International Conference on Wavelet analysis and Pattern Recognition (ICWAPR 2007), vol.3

作者： MING-QIU REN YUAN-QING ZHU YAN MAO JUN HAN Department of Electronic Countermeasures Wuhan Radar Academy Wuhan China

In this paper, a novel approach based on QTFDs and kernel principle component analysis (KPCA) is proposed to extract features of radar emitter signals. Then, these discriminative and low dimensional features achieved were fed to a Support Vector Machines (SVMs) based on FCM (fuzzy c-means) clustering for multi-class pattern recognition. Experimental results show that the proposed methodology was efficient for the different complex radar emitter signals detection and classification.

关键词： Radar emitter signal classification kernel principle component analysis FCM clustering Support vector machines

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Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis

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JOURNAL OF VIBROENGINEERING 2014年第1期16卷 57-69页

作者： Wang, Bo Liu, Shulin Zhang, Hongli Jiang, Chao Shanghai Univ Coll Mech Engn & Automat Shanghai 200072 Peoples R China Chuzhou Univ Coll Mech & Elect Engn Chuzhou 239000 Peoples R China

In order to improve the speed and accuracy of rolling bearing fault diagnosis on small samples, a method based on relevance vector machine (RVM) and kernel principle component analysis (KPCA) is proposed. Firstly, the wavelet packet energy of the vibration signal is extracted with the wavelet packet transform, which is used as fault feature vectors. Secondly, the dimension of feature vectors is reduced in order to weaken the correlation between the features. The important principal components are selected using KPCA as the new feature vectors under the criterion that the cumulative variance is greater than 95 %. Finally, the faults of rolling bearing are diagnosed through combining KPCA with RVM. Simulation experimental indicates the advantages of the presented method. Moreover, the proposed approach is applied to diagnoses rolling bearing fault. The results show that wavelet packet energy can express rolling bearing fault features accurately, KPCA can reduce the dimension of feature vectors effectively and the proposed method has better performance in the speed of fault diagnosis than the method based on support vector machine (SVM), which supplies a strategy of fault diagnosis for rolling bearing. In this paper, the performance of the proposed method is also compared with other diagnostic methods.

关键词： rolling bearing relevance vector machine kernel principle component analysis fault diagnosis

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One-class kernel subspace ensemble for medical image classification

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2014年第1期2014卷 1-13页

作者： Zhang, Yungang Zhang, Bailing Coenen, Frans Xiao, Jimin Lu, Wenjin Yunnan Normal Univ Minist Educ Key Lab Educ Informalizat Nationalities Kunming 650500 Peoples R China Univ Liverpool Dept Comp Sci Liverpool L69 3BX Merseyside England Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou 215123 Peoples R China Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

关键词： Breast cancer diagnosis Biopsy image One-class classifier kernel principle component analysis Classifier ensemble

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FESeR: A data-driven framework to enhance sensor reliability for the system condition monitoring

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MICROELECTRONICS RELIABILITY 2016年 64卷 681-687页

作者： Liu, Liansheng Peng, Yu Liu, Datong Harbin Inst Technol Sch Elect Engn & Automat Harbin 150080 Peoples R China

The collected system information is critical for condition monitoring (CM) which is mainly implemented by utilizing various types of sensors. Hence, the reliability of sensors directly influences the evaluation result of CM. One type of data-driven framework to enhance sensor reliability is realized in this article. To be specific, the combination of sensor selection method and data anomaly detection is achieved by information theory and kernel principle component analysis (KACA). The sensors which can provide more valuable information for system CM are selected. The correlations among sensors are analysed by mutual information. Finally, the data anomaly detection is conducted by utilizing the correlations among sensor data sets and KPCA. The effectiveness is proved by employing sensor data sets from National Aeronautics and Space Administration Ames Research Center. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Condition monitoring Mutual information kernel principle component analysis Anomaly detection Sensor selection

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