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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

linear discriminant analysis and Kernel Vector Quantization for Mandarin Digits Recognition

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Journal of Beijing Institute of Technology 2004年第4期13卷 385-388页

作者：赵军辉谢湘匡镜明 School of Information Science and Technology Beijing Institute of Technology Beijing100081 China

linear discriminant analysis and kernel vector quantization are integrated into vector quantization based speech recognition system for improving the recognition accuracy of Mandarin digits. These techniques increase the class separability and optimize the clustering procedure. Speaker-dependent (SD) and speaker-independent (SI) experiments are performed to evaluate the performance of the proposed method. The experiment results show that the proposed method is capable of reaching the word error rate of 3.76% in SD case and 6.60 % in SI case. Such a system can be suitable for being embedded in personal digital assistant(PDA), mobile phone and so on to perform voice controlling such as digit dialing, calculating, etc.

关键词： linear discriminant analysis kernel vector quantization speech recognition

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

linear discriminant analysis of the Wavelet Domain Features for Automatic Classification of Human Chromosomes

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Linear Discriminant Analysis of the Wavelet Domain Features ...

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

2008 9th International Conference on Signal Processing(ICSP’2008)

作者： M.Javan Roshtkhari S.Kamaledin Setarehdan Control and Intelligent Processing Center of Excellence Faculty of Electrical and Computer EngineeringUniversity of TehranTehranIran

Karyotyping is a common method in *** classification of the chromosomes within the microscopic images is the first step in designing an automatic karyotyping *** is a difficult task especially if the chromosome is highly curved within the *** paper introduces a new Wavelet Transform based linear discriminant analysis based feature vector for discriminating both normal and automatically straightened chromosomes in group E.A three layer feed-forward perceptron neural network,which is trained by means of the backpropagation algorithm,is used to classify the input chromosome into one of the three classes in the group *** tested on a data set of 303 highly curved chromosomes after automatically straightening by a previously reported method by the authors of current article [1] an average correct classification rate of 99.3% was obtained.

关键词： linear discriminant analysis Wavelet analysis Wavelet domain Humans Biological cells Microscopy Wavelet transforms Vectors Feedforward systems Neural networks

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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：黄轩博薛开平幸一滔胡定文李睿栋孙启彬 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

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

Benchmarking Performance of Document Level Classification and Topic Modeling

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Computers, Materials & Continua 2022年第4期71卷 125-141页

作者： Muhammad Shahid Bhatti Azmat Ullah Rohaya Latip Abid Sohail Anum Riaz Rohail Hassan Department of Computer Science COMSATS University IslamabadLahorePakistan Department of Communication Technology and Network Faculty of Computer ScienceUniversiti Putra MalaysiaSelangor43400Malaysia Othman Yeop Abdullah Graduate School of Business(OYAGSB) Universiti Utara Malaysia(UUM)Kuala Lumpur50300Malaysia

Text classification of low resource language is always a trivial and challenging *** paper discusses the process of Urdu news classification and Urdu documents *** is one of the most famous spoken languages in *** implementation of computational methodologies for text classification has increased over ***,Urdu language has not much experimented with research,it does not have readily available datasets,which turn out to be the primary reason behind limited research and applying the latest methodologies to the *** overcome these obstacles,a mediumsized dataset having six categories is collected from authentic Pakistani news *** is a rich but complex *** processing can be challenging for Urdu due to its complex features as compared to other *** frequency-inverse document frequency(TFIDF)based term weighting scheme for extracting features,chi-2 for selecting essential features,and linear discriminant analysis(LDA)for dimensionality reduction have been *** matrix and cosine similarity measure have been used to identify similar documents in a collection and find the semantic meaning of words in a document FastText model has been *** training-test split evaluation methodology is used for this experimentation,which includes 70%for training data and 30%for testing ***-of-the-art machine learning and deep dense neural network approaches for Urdu news classification have been ***,we trained Multinomial Naïve Bayes,XGBoost,Bagging,and Deep dense neural *** and deep dense neural network outperformed the other *** experimental results show that deep dense achieves 92.0%mean f1 score,and Bagging 95.0%f1 score.

关键词： Deep neural network machine learning natural language processing TFIDF sparse matrix cosine similarity classification linear discriminant analysis gradient boosting