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Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

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Chinese Journal of Chemical Engineering 2021年第8期34卷 128-137页

作者： Weipeng Lu Xuefeng Yan Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and TechnologyShanghai 200237China

Visual process monitoring is important in complex chemical *** address the high state separation of industrial data,we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis(BMWLDA).Then,we combine BMWLDA with self-organizing map(SOM)for visual monitoring of industrial operation *** can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature *** the discriminative feature vectors are used as the input to SOM,the training result of SOM can differentiate industrial operation states *** function improves the performance of visual *** stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis,approximate pairwise accuracy criterion,max–min distance analysis,maximum margin criterion,and local Fisher discriminant *** addition,the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

关键词： linear discriminant analysis Process monitoring Self-organizing map Feature extraction Continuous stirred tank reactor process

A Highly Accurate Dysphonia Detection System Using linear discriminant analysis

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Computer Systems Science & Engineering 2023年第3期44卷 1921-1938页

作者： Anas Basalamah Mahedi Hasan Shovan Bhowmik Shaikh Akib Shahriyar Department of Computer Engineering Umm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science and Engineering Khulna University of Engineering&TechnologyKhulna9203Bangladesh

The recognition of pathological voice is considered a difficult task for speech ***,otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%.To enhance detection accuracy and reduce processing speed of dysphonia detection,a novel approach is proposed in this *** have leveraged linear discriminant analysis(LDA)to train multiple Machine Learning(ML)models for dysphonia *** ML models are utilized like Support Vector Machine(SVM),Logistic Regression,and K-nearest neighbor(K-NN)to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients(MFCC),Fundamental Frequency(F0),Shimmer(%),Jitter(%),and Harmonic to Noise Ratio(HNR).The experiments were performed using Saarbrucken Voice Data-base(SVD)and a privately collected *** K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML *** to the experimental results,our proposed approach has a 70%increase in processing speed over Principal Component analysis(PCA)and performs remarkably well with a recognition accuracy of 95.24%on the SVD dataset surpassing the previous best accuracy of 82.37%.In the case of the private dataset,our proposed method achieved an accuracy rate of 93.37%.It can be an effective non-invasive method to detect dysphonia.

关键词： Dimensionality reduction dysphonia detection linear discriminant analysis logistic regression speech feature extraction support vector machine

A positioning method for maize seed laser-cutting slice using linear discriminant analysis based on isometric distance measurement

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Information Processing in Agriculture 2022年第2期9卷 224-232页

作者： Yingzi Wei Kanfeng Gu Longtian Tan School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110159China Advanced Manufacturing Technology Laboratory Shenyang Institute of AutomationChinese Academy of ScienceShenyang 110016China

It is necessary that vision system should aid laser-cutting manipulator to position the specified part of each maize seed for getting the slice breeding genotype analysis with high *** of trivial maize seeds should be recognized and positioned in a certain *** area ratio(CAR)is defined as the metric of pixel attribute.A large template of round mask is adopted for seed morphological detection to measure the CAR val*** get the feature points extracted from the seed image through the isometric mapping *** processes of linear discriminant analysis search the morphological data space to learn non-linear transformations to the space where data are linearly *** discriminant analysis utilizes the data directional distribution to position the major axis and distinguish different parts of maize *** labeling partition operation is applied for picking out the scattered pieces to be finely *** denoising process,the feature region could be recognized with accuracies by the synthetical *** experiments on a large amount of seeds demonstrate the effectiveness of proposed methods.

关键词： Feature regions Isometric mapping linear discriminant analysis Maize seed Molecular marker-assisted breeding technology

A Comparison of Two linear discriminant analysis Methods That Use Block Monotone Missing Training Data

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Open Journal of Statistics 2016年第1期6卷 172-185页

作者： Phil D. Young Dean M. Young Songthip T. Ounpraseuth Department of Information Systems Baylor University Waco TX USA Department of Statistical Science Baylor University Waco TX USA Department of Biostatistics University of Arkansas for Medical Sciences Little Rock AK USA

We revisit a comparison of two discriminant analysis procedures, namely the linear combination classifier of Chung and Han (2000) and the maximum likelihood estimation substitution classifier for the problem of classifying unlabeled multivariate normal observations with equal covariance matrices into one of two classes. Both classes have matching block monotone missing training data. Here, we demonstrate that for intra-class covariance structures with at least small correlation among the variables with missing data and the variables without block missing data, the maximum likelihood estimation substitution classifier outperforms the Chung and Han (2000) classifier regardless of the percent of missing observations. Specifically, we examine the differences in the estimated expected error rates for these classifiers using a Monte Carlo simulation, and we compare the two classifiers using two real data sets with monotone missing data via parametric bootstrap simulations. Our results contradict the conclusions of Chung and Han (2000) that their linear combination classifier is superior to the MLE classifier for block monotone missing multivariate normal data.

关键词： linear discriminant analysis Monte Carlo Simulation Maximum Likelihood Estimator Expected Error Rate Conditional Error Rate

Speech Emotion Recognition Based on linear discriminant analysis and Support Vector Machine Decision Tree

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Speech Emotion Recognition Based on Linear Discriminant Anal...

An Efficient Scheme to Defend Data-to-Control-Plane Saturation Attacks in Software-Defined Networking

第37届中国控制会议

作者： Jun-Wei Mao Yong He Zhen-Tao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

For speech emotion recognition,emotional feature set with high dimension may produce redundant features and influence the recognition *** solve this problem and obtain the optimal emotional feature subset of speech,a feature dimension reduction based on linear discriminant analysis is *** to the confusion degree between different basic emotions,an emotion recognition method based on support vector machine decision tree is *** on speaker-dependent speech emotion recognition using Chinese speech database from institute of automation of Chinese academy of sciences is performed and a speech emotion recognition system is presented,where standard feature sets of the INTERSPEECH and classic classifiers are used in comparative experiments *** results show that the proposal achieves 84.39%recognition accuracy on *** proposal,it would be fast and efficient to discriminate emotional states of diverse speakers from speech,and it would make it possible to realize the interaction between speaker and computer/robot in the future.

关键词： Emotion Recognition Speech linear discriminant analysis Support Vector Machine Decision Tree

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Journal of Computer Science & Technology 2022年第4期37卷 839-851页

作者： Xuan-Bo Huang Kai-Ping Xue Yi-Tao Xing Ding-Wen Hu Ruidong Li Qi-Bin Sun School of Cyber Science and Technology University of Science and Technology of ChinaHefei 230027China College of Science and Engineering Kanazawa UniversityKanazawa 920-1192Japan

Software-defined networking (SDN) decouples the data and control planes. However, attackers can lead catastrophic results to the whole network using manipulated flooding packets, called the data-to-control-plane saturation attacks. The existing methods, using centralized mitigation policies and ignoring the buffered attack flows, involve extra network entities and make benign traffic suffer from long network recovery delays. For these purposes, we propose LFSDM, a saturation attack detection and mitigation system, which solves these challenges by leveraging three new techniques: 1) using linear discriminant analysis (LDA) and extracting a novel feature called control channel occupation rate (CCOR) to detect the attacks, 2) adopting the distributed mitigation agents to reduce the number of involved network entities and, 3) cleaning up the buffered attack flows to enable fast recovery. Experiments show that our system can detect the attacks timely and accurately. More importantly, compared with the previous work, we save 81% of the network recovery delay under attacks ranging from 1,000 to 4,000 packets per second (PPS) on average, and 87% of the network recovery delay under higher attack rates with PPS ranging from 5,000 to 30,000.

关键词： software-defined networking(SDN) saturation attack fast recovery linear discriminant analysis

Gas sensor array based on carbon-based thin-film transistor for selective detection of indoor harmful gases

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Rare Metals 2024年第9期43卷 4401-4411页

作者： Can Liu Yu Sun Jia-Yi Guo Xiu-Lei Li Lu Tao Jin-Yong Hu Jue-Xian Cao Ping-Hua Tang Yong Zhang School of Electrical and Information Engineering Hunan Institute of EngineeringXiangtan 411104China Physics and Optoelectronics&Hunan Institute of Advanced Sensing and Information Technology Xiangtan UniversityXiangtan 411105China

The identification of indoor harmful gases is imperative due to their significant threats to human health and *** achieve accurate identification,an effective strategy of constructing a sensor array combined with the pattern recognition algorithm is ***-based thin-film transistors are selected as the sensor array unit,with semiconductor carbon nanotubes(CNTs)within the TFT channels modified with different metals(Au,Cu and Ti)for selective responses to NH_(3),H_(2)S and HCHO,*** accurate gas species identification,an identification mode that combines linear discriminant analysis algorithms and logistic regression classifier is *** test results demonstrate that by preprocessing the sensor array’s sensing data with the LDA algorithm and subsequently employing the LR classifier for identification,a 100%recognition rate can be achieved for three target gases(NH3,H2S and HCHO).This work provides significant guidance for future applications of chip-level gas sensors in the realms of the Internet of Things and Artificial Intelligence.

关键词： Carbon-based thin-film transistors Gas sensor arrays Semiconductor carbon nanotubes Multi-gas detection linear discriminant analysis

A machine vision-intelligent modelling based technique for in-line bell pepper sorting

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Information Processing in Agriculture 2023年第4期10卷 491-503页

作者： Khaled Mohi-Alden Mahmoud Omid Mahmoud Soltani Firouz Amin Nasiri Department of Agricultural Machinery Engineering Faculty of Agricultural Engineering and TechnologyUniversity of TehranIran Department of Biosystems Engineering and Soil Science University of TennesseeKnoxvilleTN 37996USA

The uniformity of appearance attributes of bell peppers is significant for consumers and food *** automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and *** to standards and export needs,the bell pepper should be graded based on maturity levels and size to five *** research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell *** perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that *** modelswere trained and evaluated through five-fold cross-validation *** optimum MLP classifier was compared with a linear discriminant analysis(LDA)*** results showed that the MLP outperforms the LDA *** processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line ***,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line *** performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,*** total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.

关键词： Bell pepper Sorting Image processing Machine vision Multilayer perceptron linear discriminant analysis

General moving objects recognition method based on graph embedding dimension reduction algorithm

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2009年第7期10卷 976-984页

作者： Yi ZHANG Jie YANG Kun LIU Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

Effective and robust recognition and tracking of objects are the key problems in visual surveillance systems. Most existing object recognition methods were designed with particular objects in mind. This study presents a general moving objects recognition method using global features of targets. Targets are extracted with an adaptive Gaussian mixture model and their silhouette images are captured and unified. A new objects silhouette database is built to provide abundant samples to train the subspace feature. This database is more convincing than the previous ones. A more effective dimension reduction method based on graph embedding is used to obtain the projection eigenvector. In our experiments, we show the effective performance of our method in addressing the moving objects recognition problem and its superiority compared with the previous methods.

关键词： Moving objects recognition Adaptive Gaussian mixture model Principal component analysis linear discriminant analysis Marginal Fisher analysis