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This study aims to rapidly and accurately identify the origin of spearmint. A feature wavelength extraction was proposed to combine the sequential fusion of Kernel Principal Component Analysis (KPCA) with Kernel Locality Preserving Projections (KLPP) and Wilks' Λ statistics. Effective discrimination was realized among the five origins of spearmint. Firstly, the three-dimensional fluorescence data was collected from the 300 spearmint samples in five locations. The interferences of Rayleigh and Raman scattering were removed from the original spectra by triangular interpolation. Savitzky-Golay (SG) was used to smooth and then eliminate the noise. Secondly, the preprocessed three-dimensional fluorescence spectrum matrix was merged into the data matrix, according to the emission wavelength. KPCA, KPCA + KLPP, KPCA + Wilks Λ statistics, and KPCA + KLPP + Wilks Λ statistics were used to extract the feature wavelengths of the three-dimensional fluorescence spectrum matrix after preprocessing. Among them, KPCA and KPCA + Wilks Λ statistics were used to extract 40 wavelengths of feature emission and eight wavelengths of feature excitation, respectively. KPCA+KLPP and KPCA + KLPP + Wilks Λ statistics were to extract 50 wavelengths of feature emission and 10 wavelengths of feature excitation, respectively. Thirdly, the spectral values of feature emission wavelength were converted into row vectors, according to the wavelength order of feature excitation. Four 300-row spectral matrices of feature wavelength were then obtained from the 300 samples. Furthermore, Fisher discriminant analysis (FDA) was applied to the spectral matrix of feature wavelengths for data separable fusion for the separable fisher discriminant (FD) variables. The first four FD variables were selected with a cumulative discriminant ability of 99% as the input vectors of the discriminant model for the spearmint origins. Finally, the support vector machine (SVM) was used to analyze the four FD variables. The FDA + SVM identification model showed that there was a four-feature wavelength extraction. The correct rates for the training set were 92.00%, 97.78%, 95.11%, and 100%, respectively, and the correct rates for the test set were 92.00%, 96.00%, 94.67%, and 100%, respectively. The results show that both KLPP and Wilks Λ statistics improved the performance of KPCA. The sequential fusion significantly improved the overall feature extraction, in order to better remove the redundant information of spectral data for the high correct of identification. Therefore, the sequential fusion of KPCA + KLPP + Wilks Λ statistics + FDA + SVM can be expected to effectively identify the origin of spearmint. The extraction of feature wavelength with sequential fusion of KPCA + KLPP + Wilks Λ statistics can effectively reduce the redundancy of three-dimensional fluorescence spectral data for the informative features of the original fluorescence data. Better identification was achieved in the five kinds of spearmint origins. In addition, these research findings can lay a foundation for the subsequent quantitative analysis of the important components in spearmint by three-dimensional fluorescence spectroscopy. [ABSTRACT FROM AUTHOR]

The paper proposes a method based on kernel principal component analysis (KPCA) and multi-scale temporal convolution network (MTCN) for identifying faults in lithium-ion batteries, which is crucial for ensuring the safe and stable operation of energy-storage systems. Lithium-ion batteries are the primary component of energy storage units. The method involves the following steps: First, fault data are normalized, and KPCA is used for dimensionality reduction and single fault detection to reduce computational complexity and improve data reliability. According to the different types of overcharge faults and unknown faults, KPCA is used to reduce the data from the original dimension to 2 or 4 dimensions. The KPCA model is trained using the normal data corresponding to the two groups of fault data, and the fault data are inputted as the test data. The results show that the SPE statistic and the T2 statistic considerably exceed the control limit, verifying the reliability of the data. Then, the data are labeled according to the fault type: the overcharge data are labeled as D0 (normal) and D1 (fault), and the unknown fault data as F0 (normal) and F1 (fault). The labeled data are divided into training and test sets according to a specific proportion. Afterward, the MTCN model is trained with the training dataset, and its hyperparameters are optimized with the frost algorithm to improve model accuracy. Finally, the trained MTCN model is used to classify the test dataset. The method is validated on two groups of data: overcharge fault data and unknown fault data. The results show that the frost algorithm can optimize the hyperparameters after about 20 iterations. Compared with LSTM and CNN, which are also optimized by the frost algorithm, MTCN achieves a higher classification accuracy, reaching 99.265% and 99.688%, on the overcharge fault dataset and unknown fault dataset, respectively, while maintaining comparable performance to Xception and ResNet50. Additionally, to verify the influence of the training data amount, the training and test sets are divided according to different proportions, and the three algorithms are tested. KPCA verifies the reliability of charging fault and unknown fault data, and the results show that MTCN has the highest classification accuracy, especially on the overcharge fault dataset. Owing to the low dimensionality of the original data set, LSTM and CNN exhibit poor classification performance. In contrast, MTCN can extract more temporal information, achieving high classification accuracy. These results demonstrate the effectiveness and superiority of the method in fault diagnosis of lithium-ion batteries.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A prediction model based on ikPCA-FABAS-KELM is proposed to improve the short-term wind power prediction by traditional data-driven machine learning models. First, the principal component analysis is improved and the reversible kernel Principal Component Analysis (ikPCA) is proposed to reduce the complexity of input data while ensuring data features, with the purpose to advance the model in running speed. Second, the individual attraction strategies for Firefly Algorithm (FA) are used to improve the Beetle Antennae Search (BAS) thus a FABAS algorithm is proposed. Finally, the FABAS algorithm is used to optimize the regularization parameter C and kernel parameters γ of the Kernel Extreme Learning Machine (KELM),which can reduce the impact of manual parameter setting on blind model training thus improve model prediction accuracy. The simulation results show that the proposed model effectively improves the short-term wind power prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BackgroundNeutrons/Gamma (n/γ) discrimination is critical for neutron detection in the presence of γ radiation and traditional pulse shape discrimination methods suffer from unstable discrimination accuracy.PurposeThis study aims to implement a machine-learning method that combines the kernel principal component analysis (KPCA), marine predator algorithm (MPA), and extreme learning machine (ELM) is proposed to improve the n/γ discrimination efficiency and accuracy against the traditional pulse shape discrimination methods.MethodsThe KPCA was used to reduce the dimensionality of the pulse signal characteristics of neutrons and gamma rays. Owing to the randomness in the ELM input layer weight and hidden layer bias, the MPA was employed to optimize the foregoing factors to improve the n/γ discrimination accuracy of the ELM. Finally, experimental data of Pu-C neutron source using BC-501A liquid scintillator detector were applied to effectiveness comparison of training and test with and without KPCA dimensionality reduction.ResultsComparison results reveal that the average discrimination accuracy of the KPCA-MPA-ELM is as high as 99.07%, which is 12.19%, 2.52%, and 1.56% higher than those of the ELM, MPA-ELM, and KPCA-ELM models, respectively. Compared with the charge comparison method and pulse gradient analysis method, the accuracy is improved by 1.80% and 5.91%, respectively.ConclusionsThe proposed model has a simple structure, exhibits good stability, hence be applied to handling high-dimensional data with good discrimination and generalization ability.

Copyright of Journal of Mechanical Strength / Jixie Qiangdu is the property of Zhengzhou Research Institute of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Brewing is the property of China Brewing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, in network optimization, network quality evaluation and prediction are generally based on communities, and a flexible evaluation system for POI network quality was proposed following the research idea of “research on dimensionality increase and implementation of dimensionality reduction”.However, it involves many network KPI, resulting in a relatively complex evaluation system for POI network comprehensive quality and low prediction accuracy.In order to improve the prediction accuracy of POI network quality, KPCA was used to compress the correlation of input variables of BP neural network, the structure of BP neural network was simplified, and then the connection weights and threshold parameters of BP neural network were optimized through GA.Compared with the prediction results of traditional BP neural network, the prediction accuracy is improved by 10.9%, and the mean square error performance is significantly reduced, it can play a better supporting role in the prediction of POI network quality.

Aiming at the problem of insufficient labeled samples in the process of rolling bearing fault diagnosis, a rolling bearing fault diagnosis model based on semi supervised Laplace score（SSLS） and kernel principal component analysis（KPCA） is proposed by combining with the idea of feature selection and secondary mining. SSLS applies the semi supervised idea to the Laplace score feature selection method, uses a small number of labeled samples and a large number of unlabeled samples, and combines KPCA to excavate fault features for a second time. At the same time, particle swarm optimization-based support vector machine（PSO-SVM） algorithm is used for fault classification. Finally, the model is applied to the process of experimental data analysis. The results show that the model can not only reduce the workload of sample marking, but also maintain a high accuracy in rolling bearing fault classification, which verifies the effectiveness and engineering practicability of the model.

In order to detect abnormal wind turbine generator and reduce the occurrence of outages, a deep learning framework combining K-CNN and N-GRU is proposed based on multi-dimensional sensor parameters recorded in real wind farm SCADA system, and a wind turbine generator state prediction model is established. Firstly, the correlation of state parameters was analyzed by Pearson correlation coefficient, and then the one-dimensional fusion parameters were weighted by weight coefficient. Secondly, to solve the problem of ignoring shallow features in traditional feature extraction, CNN was used to extract the features of one-dimensional fusion parameters in layers, and KPCA was used to reduce the feature extraction results of different layers to one dimension. Then, to solve the problem of parameter optimization of the traditional GRU algorithm, the neural network architecture search was used to improve the GRU algorithm, and the N-GRU model was obtained. The feature extraction results after dimensionality reduction were input into N-GRU for prediction and reconstruction error was obtained, then the state evaluation was realized by setting the alarm threshold. Finally, a 2 MW wind turbine in a wind farm in Xinjiang was taken as an example to verify the model validity and model accuracy.

ObjectiveA ship's system is a complex mechanism composed of multiple pieces of equipment. Due to the dynamic and non-linear characteristics of the parameters of each component, fault diagnosis is complicated. This paper proposes a dynamic feature fusion method for performing efficient fault diagnosis on the system.MethodsFractal theory, dynamic theory and the kernel principal component analysis (KPCA) method are used to reconstruct, map and filter the system state data, and obtain the principal component characteristic data matrix, square prediction error (SPE) and corresponding control limits. An offline monitoring model based on the health data of a marine diesel engine intake and exhaust system is then constructed and used to diagnose and analyze ship system faults. In order to verify the validity of the model, the fault data of the intake and exhaust system of a marine diesel engine is selected for verification and analysis. ResultsThe results show that this method can effectively realize the accurate analysis of a system's dynamic nonlinear state data and efficient analysis and diagnosis of faults, with better fault diagnosis performance than the KPCA and support vector machine (SVM) method.ConclusionsThe method proposed in this paper can realize the detection and diagnosis of marine diesel engine intake and exhaust system failures, and improve the reliability and safety of system operation.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rockburst is one of the major disasters in irrigation works and mining, and its accurate prediction is important. The stress coefficient ae/e„ brittleness coefficient cre/e,, and elastic energy index Wet are chosen to be classification prediction indicators. Support vector machine ( SVM) model based on optimization of grey wolf optimization ( GWO) is proposed. The data are processed by kernel principal component analysis ( KPCA). KPCA — GWO — SVM model for rockburst prediction is established. The forecasting results show good classification performance. The estab-lished model is applied in Dongguashan Copper Mine and compared with BP neural network model. The result shows that the KPCA — GWO — SVM model is an effective tool for high-precision classification of rockburst intensity. [ABSTRACT FROM AUTHOR]

In order for the support vector machine to accurately identify the surface defects of wood veneer and improve the quality of wood veneer, an efficient and accurate recognition model of kernel principal component analysis (KPCA) gravity search algorithmsupport vector machine (GSA-SVM) for veneer defects was proposed. Considering the redundant effect between image feature data, KPCA method was used to reduce the dimension of original feature data, and GSA optimized the penalty factor C and kernel parameter g of support vector machine (SVM) to establish KPCA-GSA-SVM wood veneer defect recognition model. Based on the three features of color, texture and shape, the raw data set of samples with live knots, dead knots and cracks as the research objects, 8 main features (1 color feature, 1 texture feature and 6 shape features) were selected as the basis for wood veneer recognition. The wood veneer identification model was learned, trained, predicted and analyzed, and the identification effect was compared with the KPCA-PSO-SVM identification model composed of the traditional particle swarm parameter optimization algorithm (PSO). The results showed that the recognition rate of live knots, dead knots and cracks of the wood veneer recognition model based on KPCA-GSA-SVM was 100%, 96. 78% and 100%, which were 21. 62%, 0. 63% and 7. 41% higher than that of KPCA-PSO-SVM, and the overall time was shortened by 7. 26 s, it can be seen that the prediction recognition rate, recognition speed and stability were higher than the former. The research conclusions identify the veneer defects from a new perspective, which is helpful for the identification and development of wood veneer defects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Novelty detection is an important research issue in the field of machine learning.Till now,there exist lots of novelty detection approaches.As a commonly used kernel method,kernel principal component analysis(KPCA)has been successfully applied to deal with the problem of novelty detection.However,the traditional KPCA based novelty detection method is very sensitive to noise.If there exist noise in the given training samples,the detection performance of KPCA based novelty detection method may be decreased.To enhance the anti-noise ability of KPCA based novelty detection method,a maximum correntropy criterion(MCC)based novelty detection method is proposed.Correntropy in information theoretic learning is utilized to substitute the 2-norm based measure in KPCA based novelty detection method.By adjusting the width parameter of the correntropy function,the adverse effect of noise can be alleviated.The half-quadratic optimization technique is used to solve the optimization problem of the proposed method.The local optimal solution can thus be obtained after a few iterations.Moreover,the algorithmic description of the proposed method is provided,and the computational complexity of the corresponding algorithm is analyzed.Experimental results on the 16 UCI benchmark data sets demonstrate that the proposed method obtains better anti-noise and generalization performance in comparison with the other four related approaches.

In the real industrial process, some process variables are independent of other variables, a fault detection method of related and independent variable based on kernel principal component analysis and support vector machine (KPCA-SVM) is proposed to detect these independent variables separately from related variables. Firstly, a variable division strategy based on mutual information is applied to divide the process variables into related variables and independent variables by calculating the mutual information between variables. Then, KPCA and SVM models are established in the related variable space and the independent variable space to monitor the test data. Compared with the traditional KPCA and SVM methods, the KPCA-SVM method combines the advantages of KPCA in detecting related variables and SVM methods in detecting independent variables, and improves the fault detection performance of KPCA and SVM methods. Finally, the KPCA-SVM method is applied to the Tennessee-Eastman (TE) industrial process for fault detection, and compared with KPCA, kernel entropy component analysis (KECA) and SVM methods. The results show that the proposed KPCA-SVM method has a good detection effect and improves the detection effect of multiple faults, among which the detection effect of minor fault 5 is significantly improved, which further verifies the effectiveness of the KPCA-SVM method. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For improving the accuracy of gas emission prediction, an algorithm using kernel principal component analysis (KPCA) with auto-regressive integrated moving average model (ARIMA) was proposed. The time series expression method of gas outflow quantity is used, and the obtained data is smoothed by using difference method. The original data which affects gas outflow is reduced by kernel principal component analysis. The feature of gas emission quantity is extracted from the reduced data, and a sliding average gas emission prediction model with autoregressive integral is established. The ACF and PACF are used to optimize the model parameters, and the d-order difference results, autoregression coefficient p, and sliding average order q are synthesized. In order to verify the validity of the method, the optimized prediction model of gas emission is validated experimentally. The prediction results are compared with three models: third-order exponential smoothing, BPNN, GM(1,1). MAE, MRE and MSE of the model are better than that of other models. The experimental results show that the proposed gas emission prediction method has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The inaccurate life prediction caused by multiple degradation information can be easily appeared during the operation of complex aero-engine,a multi-information fusion life prediction model based on kernel principal component analysis(KPCA)and bidirectional long short-term memory(BLSTM)neural network is proposed.Firstly,the kernel principal component method is used to perform dimensionality reduction and information fusion on the multi-dimensional degraded data set to obtain a low-dimensional feature data set that can characterize equipment degradation.Then,the BLSTM neural network is used to predict the remaining useful life(RUL)of aero-engine with multi-dimensional degradation information to obtain the mapping relationship between the monitoring data and the remaining life.Finally,the C-MAPSS aero-engine degradation data set is used to simulate and verify the proposed multi-information fusion life prediction model,and the results are compared with other three models.The results show that the proposed KPCA-BLSTM neural network can predict RUL under multi-dimensional degradation information accurately,the error and score of the proposed model are better than the other three models,and the model has higher prediction accuracy.

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the process of electrical experiment teaching, manual evaluation of the data measured by students is cumbersome, which affects the quality and efficiency of teaching. In this paper, an improved KNN (K-nearest neighbors) classification algorithm, namely M-KPCA-KNN based on mean shift, margin and KPCA (kernel principal component analysis) is proposed to judge whether the data measured by students is correct or not and the cause of the error. Firstly, the algorithm KPCA is uesd to reduce dimension number of the high-dimensional experimental data, then mean shift vector is adopted to find the densest locations of different types of data, and the safety margin among the boundary of different types of data is set. Finally, sets the weight of the k data points closest to the sample to be tested to calculate the weight sum of each category, and the category of the largest weight sum is regarded as the category of the sample to be tested. Compared with the existing KNN algorithms, the M-KPCA-KNN algorithm not only improves the classification accuracy, but also reduces the time complexity. [ABSTRACT FROM AUTHOR]

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Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of water inrush source identification of mine water disaster, KPCA method is used to reduce the dimension of original data, and cuckoo search algorithm (CS) is used to optimize the penalty factor C and kernel parameter g of support vector machine (SVM), and a mine water inrush source discrimination model based on KPCA-CS-SVM is established. Seven main influencing factors are selected as the basis for the discrimination of water inrush sources. The KPCA-CS-SVM model is used for learning, training and predictive analysis, and compared with the discrimination effect of the KPCA-GA-SVM and KPCA-PSO-SVM models. The results show that the discrimination results of KPCA-CS-SVM model are consistent with the actual results, and the prediction results are consistent. The accuracy, discrimination speed and stability are higher than that of the other two models. The research conclusion is helpful to predict the disaster of mine water inrush by distinguishing the source of water inrush from a new angle. [ABSTRACT FROM AUTHOR]

Low-rate denial-of-service (LDoS) attack is a new type of attack mode for TCP protocol.Characteristics of low average rate and strong concealment make it difficult for detection by traditional DoS detecting methods.According to characteristics of LDoS attacks,a new LDoS queue future was proposed from the router queue,the kernel principal component analysis (KPCA) method was combined with neural network,and a new method was present to detect LDoS attacks.The method reduced the dimensionality of queue feature via KPCA algorithm and made the reduced dimension data as the inputs of neural network.For the good sell-learning ability,BP neural network could generate a great LDoS attack classifier and this classifier was used to detect the attack.Experiment results show that the proposed approach has the characteristics of effectiveness and low algorithm complexity,which helps the design of high performance router.

To improve cutting tool wear condition monitoring accuracy,wear condition monitoring method based on artificial bee colony-BP neutral network is proposed. Force sensor and vibration sensor are used to design monitoring platform. Matching pursuit algorithm is used to denoise for the signal. Characteristic parameters are extracted from time domain,frequency domain,and time-frequency domain. These characteristic parameters are reduced dimensionally by kernel principal component analysis,and fifteen parameters are ensured at last. Cutting tool wear condition recognition method based on artificial bee colony-BP neutral network algorithm is presented, and artificial bee colony algorithm is used to optimize neutral network parameters. The experimental result proves that the recognition accuracy is 78. 75% by BP neutral network and 100% by optimized BP neutral network algorithm respectively.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve convergence speed and accuracy of short-term traffic flow prediction, a method of KPCA-KELM combining kernel principal component analysis (KPCA) and kernel extreme learning machine (KELM) was proposed. By the KPCA method, the nonlinear principal elements of model input in the feature space were effectively extracted to realize the data pre-processing. In the KELM method, the nodes number of network hidden layer was not set, and the output weight of network was calculated by the regularized least squares algorithm to achieve good promotion with extremely fast learning speed. The advantages of KPCA and KELM were combined in the proposed method. The traffic flow prediction data measured by the ITS Research Group of the University of Washington in Seattle and the Beijing Traffic Administration were tested, and the KPCA-KELM method was compared with single KELM, LSSVM, SVM, KPCA-LSSVM, KPCA-SVM and other prediction methods. The experimental results show that the convergence speed and prediction accuracy of the proposed method are better than those of other comparison methods. For the single-step prediction of the measured traffic volume data of Beijing Transportation Administration, the prediction accuracy of the KPCA-KELM method is 1.991 3 higher than that of the KELM method. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To overcome the shortage of the new samples existing in Laplacian eigenmaps, this paper proposed face recognition with Laplacian eigenmaps based on 2D-KPCA, namely, the 2D-KPCA + LE algorithm. First of all,different from the kernel two-dimensional principal component analysis( K2DPCA), the 2DPCA applied to the training sample space. So the algorithm not only could retain the structural information of sample space, but also could obtain a low-rank projection matrix by decorrelation. Then it used the KPCA to extract nonlinear features. But the kernel function needed a lot of storage. The algorithm utilized Laplacian eigenmaps to reduce dimensions again. Experimental results in ORL and FERET face databases show that the 2D-KPCA + LE has higher recognition rate and lower the complexity of the algorithm than other manifold learning methods. [ABSTRACT FROM AUTHOR]

Multivariate statistical process monitoring methods such as PCA and ICA are always based on a variety of assumptions. If the constraints of these methods have not been considered when selecting these methods, wrong conclusions will be obtained and the rate of high leaking and false alarm will be increased. To solve the constraints problem of these methods in application, a data characteristic analysis method was proposed to test the correlation of the variables automatically, in which parameter optimization was conducted and the set of linear variables was eliminated sequentially. The simulation illustrated that the proposed method can select appropriate modelling method automatically according to data characteristics and applicable conditions of the methods, which has considerable practical value. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To analyze online reviews effectively and provide valuable information to both consumers and companies, this paper proposed data modeling and measure system for online product reviews. Firstly, this paper proposed the identifying method based on the KPCA-LS-SVM (kernel principal component analysis least squares support vector machine) model for fake reviews problem. Meanwhile, the paper solved the problem of review data validation analysis by ordinal logistic probability model for the problem of review data validation analysis. At last, experiments were conducted on the real dataset. The results show that it not only can effectively classify fake online reviews, but also improve discriminant validity of the data efficiently. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)