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Cost-Sensitive Dual-Stream Residual Networks for Imbalanced Classification
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作者 Congcong Ma Jiaqi Mi +1 位作者 Wanlin Gao Sha Tao 《Computers, Materials & Continua》 SCIE EI 2024年第9期4243-4261,共19页
Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as indust... Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,etc.In imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority class.However,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high accuracy.Therefore,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual network.Firstly,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction capability.Next,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is devised.This loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ability.Finally,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,respectively.Furthermore,it achieves the highest precision in single-class recognition tasks for the minority class. 展开更多
关键词 Deep learning imbalanced data classification fault diagnosis cost-sensitivity
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A combined algorithm of K-means and MTRL for multi-class classification 被引量:2
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作者 XUE Mengfan HAN Lei PENG Dongliang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2019年第5期875-885,共11页
The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class cla... The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset. 展开更多
关键词 machine LEARNING multi-class classification K-MEANS MULTI-TASK RELATIONSHIP LEARNING (MTRL) OVER-FITTING
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Fault diagnosis using a probability least squares support vector classification machine 被引量:4
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作者 GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China 《Mining Science and Technology》 EI CAS 2010年第6期917-921,共5页
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines ... Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM. 展开更多
关键词 fault diagnosis PROBABILITY least squares support vector classification machine roller bearing
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Research on the Power System Fault Classification Based on HHT and SVM Using Wide-area Information 被引量:1
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作者 Yiran Guo Changqing Li +1 位作者 Yali Li Shibin Gao 《Energy and Power Engineering》 2013年第4期138-142,共5页
A power system fault classification method based on the Hilbert-Huang transformation (HHT) and support vector machine (SVM) is proposed in this paper. According to different types of faults taking place in area and th... A power system fault classification method based on the Hilbert-Huang transformation (HHT) and support vector machine (SVM) is proposed in this paper. According to different types of faults taking place in area and the outer area, this paper uses HHT to extract the instantaneous amplitude and Hilbert marginal spectrum of the current signal. Then a fault classifier consisting of a series of SVM classifiers that are optimized by using cross validation method is constructed. Finally, inputting the feature vector sets that are conversed by the HHT into the fault classifier, the fault type and locate the fault area will be distinguished. The simulation results show that this approach is very effective to classify the fault type especially when the sample is small. 展开更多
关键词 Power System fault classification HHT SVM Cross-validation
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Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve 被引量:1
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作者 Mudita Uppal Deepali Gupta +5 位作者 Divya Anand Fahd S.Alharithi Jasem Almotiri Arturo Mansilla Dinesh Singh Nitin Goyal 《Computers, Materials & Continua》 SCIE EI 2022年第7期1799-1814,共16页
The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to pred... The rapid expansion of Internet of Things(IoT)devices deploys various sensors in different applications like homes,cities and offices.IoT applications depend upon the accuracy of sensor data.So,it is necessary to predict faults in the sensor and isolate their cause.A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults.This technique identifies the faulty sensor and determines the correct working of the sensor.Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.Fault prediction in digital and analog sensors along with methods of sensor fault prediction are described.There are several advantages and disadvantages of sensor fault prediction methods and the fall curve technique.So,some solutions are provided to overcome the limitations of the fall curve technique.In this paper,a bibliometric analysis is carried out to visually analyze 63 papers fetched from the Scopus database for the past five years.Its novelty is to predict a fault before its occurrence by looking at the fall curve.The sensing of current flow in devices is important to prevent a major loss.So,the fall curves of ACS712 current sensors configured on different devices are drawn for predicting faulty or non-faulty devices.The analysis result proved that if any of the current sensors gets faulty,then the fall curve will differ and the value will immediately drop to zero.Various evaluation metrics for fault prediction are also described in this paper.At last,this paper also addresses some possible open research issues which are important to deal with false IoT sensor data. 展开更多
关键词 fault identification fault classification IoT sensor nodes analog sensors digital sensors fall curve
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Power Quality Disturbance Classification Method Based on Wavelet Transform and SVM Multi-class Algorithms 被引量:1
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作者 Xiao Fei 《Energy and Power Engineering》 2013年第4期561-565,共5页
The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wav... The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification. 展开更多
关键词 Power Quality DISTURBANCE classification WAVELET TRANSFORM SVM multi-class ALGORITHMS
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Support Vector Machine for mechanical faults classification 被引量:1
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作者 蒋志强 符寒光 李凌君 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第5期433-439,共7页
Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to patt... Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents an SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-fault SVM classifier based on binary clas- sifier is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency in mechanical system. It is suitable for on line diagnosis for mechanical system. 展开更多
关键词 Support Vector Machine (SVM) fault diagnosis Multi-fault classification Intelligent diagnosis
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Gear Transmission Fault Classification using Deep Neural Networks and Classifier Level Sensor Fusion 被引量:6
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作者 Min XIA Clarence W.DE SILVA 《Instrumentation》 2019年第2期101-109,共9页
Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working envir... Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors. 展开更多
关键词 fault classification fault DIAGNOSIS Convolutional NEURAL Networks GEAR Transmission DECISION FUSION
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A Situational Awareness Method for Initial Insulation Fault of Distribution Network Based on Multi-Feature Index Comprehensive Evaluation
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作者 Hao Bai Beiyuan Liu +3 位作者 Hongwen Liu Jupeng Zeng Jian Ouyang Yipeng Liu 《Energy Engineering》 EI 2024年第8期2191-2211,共21页
Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend o... Most ground faults in distribution network are caused by insulation deterioration of power equipment.It is difficult to find the insulation deterioration of the distribution network in time,and the development trend of the initial insulation fault is unknown,which brings difficulties to the distribution inspection.In order to solve the above problems,a situational awareness method of the initial insulation fault of the distribution network based on a multi-feature index comprehensive evaluation is proposed.Firstly,the insulation situation evaluation index is selected by analyzing the insulation fault mechanism of the distribution network,and the relational database of the distribution network is designed based on the data and numerical characteristics of the existing distribution management system.Secondly,considering all kinds of fault factors of the distribution network and the influence of the power supply region,the evaluation method of the initial insulation fault situation of the distribution network is proposed,and the development situation of the distribution network insulation fault is classified according to the evaluation method.Then,principal component analysis was used to reduce the dimension of the training samples and test samples of the distribution network data,and the support vector machine(SVM)was trained.The optimal parameter combination of the SVM model was found by the grid search method,and a multi-class SVM model based on 1-v-1 method was constructed.Finally,the trained multi-class SVM was used to predict 6 kinds of situation level prediction samples.The results of simulation examples show that the average prediction accuracy of 6 situation levels is above 95%,and the perception accuracy of 4 situation levels is above 96%.In addition,the insulation maintenance decision scheme under different situation levels is able to be given when no fault occurs or the insulation fault is in the early stage,which can meet the needs of power distribution and inspection for accurately sensing the insulation fault situation.The correctness and effectiveness of this method are verified. 展开更多
关键词 Distribution grid insulation degradation initial insulation fault multi-feature indices multi-class SVM situational level situational awareness
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GRAIN REDUCTION AND GROWTH IN FAULTINGAND NEW CLASSIFICATION OF FAULTED ROCKS
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作者 Duan Jiarui Kong Hua(Department of Geology, Central South University of Technology,Changsha 410083, China) 《Journal of Central South University》 SCIE EI CAS 1996年第1期46-48,共3页
New classification scheme about faulted rocks is proposed, according to the extent of grain reduction and growth and their sequence, and faulted rocks are classified as follows: (1) faulted rocks formed by the reducti... New classification scheme about faulted rocks is proposed, according to the extent of grain reduction and growth and their sequence, and faulted rocks are classified as follows: (1) faulted rocks formed by the reduction action mainly include breccia series, cataclasite series, tectonobutchite series, mylonite series; (2) faulted rocks formed by growth action is mainly tectonoschist (gneiss) series; (3) blastomylonite series formed by grain reduction first and then growth; (4) mylonitic schist (gneiss) series formed by crystal growth first and then grain reduction. All series can be further classified according to matrix contents. 展开更多
关键词 REDUCTION GROWTH faultING classification〖J
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Fault Classification and Localization in Power Systems Using Fault Signatures and Principal Components Analysis
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作者 Qais H. Alsafasfeh Ikhlas Abdel-Qader Ahmad M. Harb 《Energy and Power Engineering》 2012年第6期506-522,共17页
A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing... A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection, classification and localization. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power transmission system is presented. This work will extract phase current values during ( )th of a cycle to generate unique signatures. By utilizing principal component analysis (PCA) methods, this system will identify and classify any fault instantaneously. Also, by using the curve fitting polynomial technique with our index pattern obtained from the unique fault signature, the location of the fault can be determined with a significant accuracy. 展开更多
关键词 fault Detection and classification Protective RELAYING PCA PSCAD
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One-Variable Attack on the Industrial Fault Classification System and Its Defense
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作者 Yue Zhuo Yuri A.W.Shardt Zhiqiang Ge 《Engineering》 SCIE EI CAS 2022年第12期240-251,共12页
Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclu... Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns.However,these data-driven models are vulnerable to adversarial attacks;thus,small perturbations on the samples can cause the models to provide incorrect fault predictions.Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples.This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system:Only one variable can be perturbed to craft adversarial samples.Moreover,to hide the adversarial samples in the visualization space,a Jacobian matrix is used to guide the perturbed variable selection,making the adversarial samples in the dimensional reduction space invisible to the human eye.Using the one-variable attack(OVA)method,we explore the vulnerability of industrial variables and fault types,which can help understand the geometric characteristics of fault classification systems.Based on the attack method,a corresponding adversarial training defense method is also proposed,which efficiently defends against an OVA and improves the prediction accuracy of the classifiers.In experiments,the proposed method was tested on two datasets from the Tennessee–Eastman process(TEP)and steel plates(SP).We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets.For industrial fault classification systems,the attack success rate of our method is close to(on TEP)or even higher than(on SP)the current most effective first-order white-box attack method,which requires perturbation of all variables. 展开更多
关键词 Adversarial samples Black-box attack Industrial data security fault classification system
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Classification of Transmission Line Ground Short Circuit Fault Based on Convolutional Neural Network
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作者 Tao Guo Gang Tian +3 位作者 Zhimin Ao Xi Fang Lili Wei Fei Li 《Energy Engineering》 EI 2022年第3期985-996,共12页
Ground short circuit faults in current transmission lines are common in the power systems.In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines,a novel... Ground short circuit faults in current transmission lines are common in the power systems.In order to prevent the power system from aggravating the accident caused by short-circuit faults of transmission lines,a novel convolutional neural network(CNN)model is constructed to identify the short-circuit fault of the transmission line in the power system.The CNN model is mainly consisted of five convolutional layers,three max-pooling layers,one concatenate layer,one dropout layer,one fully connected layer,and a Softmax classifier.This method uses a fixed time window to intercept system short-circuit fault data,extracts the deep features of these data from the training samples,and then corresponds the extracted features to labels one-to-one.Finally,in PSCAD/EMTDC,the new England 10 machine 39 nodes are taken as an example to realize the simulation.The experimental results show that the CNN model can quickly and accurately identify the short-circuit fault types,and the optimal model accuracy rate reaches 99.95%.The results of this manuscript-have positive effect on enhancing the disaster prevention capability and the operation stability of transmission lines. 展开更多
关键词 Convolutional neural networks transmission line fault classification
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Fault Diagnosis Based on MultiKernel Classification and Information Fusion Decision
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作者 Mohammad Reza Vazifeh Pan Hao Farzaneh Abbasi 《Computer Technology and Application》 2013年第8期404-409,共6页
In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories (sub-populations) containing observa... In machine learning and statistics, classification is the a new observation belongs, on the basis of a training set of data problem of identifying to which of a set of categories (sub-populations) containing observations (or instances) whose category membership is known. SVM (support vector machines) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes fon^as the output, making it a non-probabilistic binary linear classifier. In pattern recognition problem, the selection of the features used for characterization an object to be classified is importance. Kernel methods are algorithms that, by replacing the inner product with an appropriate positive definite function, impticitly perform a nonlinear mapping 4~ of the input data in Rainto a high-dimensional feature space H. Cover's theorem states that if the transformation is nonlinear and the dimensionality of the feature space is high enough, then the input space may be transformed into a new feature space where the patterns are linearly separable with high probability. 展开更多
关键词 fault diagnosis wavelet-kernel information fusion multi classification.
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A Transmission Line Fault Classification Approach by Support Vector Machines
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作者 A.M. Ibrahim A.Y. Abdelaziz S.F. Mekhamer M. Ramadan 《Journal of Energy and Power Engineering》 2011年第3期268-274,共7页
This paper presents an approach for shunt faults detection and classification in transmission line using Support Vector Machine (SVM). The paper compares between using three line post-fault current samples for one-h... This paper presents an approach for shunt faults detection and classification in transmission line using Support Vector Machine (SVM). The paper compares between using three line post-fault current samples for one-half cycle and one-fourth cycle from the inception of the fault as inputs for SVM. Two SVMs are used, first SVMabc is used for faulty phase detection and second SVMg is used for ground detection. SVMs with polynomial kernel with different degrees are used to obtain the best classification score. The classification test results show that the proposed method is accurate and reliable. 展开更多
关键词 Transmission line protection fault detection fault classification support vector machine.
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Fault Diagnosis for Aero-engine Applying a New Multi-class Support Vector Algorithm 被引量:4
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作者 徐启华 师军 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2006年第3期175-182,共8页
Hierarchical Support Vector Machine (H-SVM) is faster in training and classification than other usual multi-class SVMs such as "1-V-R"and "1-V-1". In this paper, a new multi-class fault diagnosis algorithm based... Hierarchical Support Vector Machine (H-SVM) is faster in training and classification than other usual multi-class SVMs such as "1-V-R"and "1-V-1". In this paper, a new multi-class fault diagnosis algorithm based on H-SVM is proposed and applied to aero-engine. Before SVM training, the training data are first clustered according to their class-center Euclid distances in some feature spaces. The samples which have close distances are divided into the same sub-classes for training, and this makes the H-SVM have reasonable hierarchical construction and good generalization performance. Instead of the common C-SVM, the v-SVM is selected as the binary classifier, in which the parameter v varies only from 0 to 1 and can be determined more easily. The simulation results show that the designed H-SVMs can fast diagnose the multi-class single faults and combination faults for the gas path components of an aero-engine. The fault classifiers have good diagnosis accuracy and can keep robust even when the measurement inputs are disturbed by noises. 展开更多
关键词 support vector machine fault diagnosis multi-class classification
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A Novel Combinational Convolutional Neural Network for Automatic Food-Ingredient Classification 被引量:5
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作者 Lili Pan Cong Li +2 位作者 Samira Pouyanfar Rongyu Chen Yan Zhou 《Computers, Materials & Continua》 SCIE EI 2020年第2期731-746,共16页
With the development of deep learning and Convolutional Neural Networks(CNNs),the accuracy of automatic food recognition based on visual data have significantly improved.Some research studies have shown that the deepe... With the development of deep learning and Convolutional Neural Networks(CNNs),the accuracy of automatic food recognition based on visual data have significantly improved.Some research studies have shown that the deeper the model is,the higher the accuracy is.However,very deep neural networks would be affected by the overfitting problem and also consume huge computing resources.In this paper,a new classification scheme is proposed for automatic food-ingredient recognition based on deep learning.We construct an up-to-date combinational convolutional neural network(CBNet)with a subnet merging technique.Firstly,two different neural networks are utilized for learning interested features.Then,a well-designed feature fusion component aggregates the features from subnetworks,further extracting richer and more precise features for image classification.In order to learn more complementary features,the corresponding fusion strategies are also proposed,including auxiliary classifiers and hyperparameters setting.Finally,CBNet based on the well-known VGGNet,ResNet and DenseNet is evaluated on a dataset including 41 major categories of food ingredients and 100 images for each category.Theoretical analysis and experimental results demonstrate that CBNet achieves promising accuracy for multi-class classification and improves the performance of convolutional neural networks. 展开更多
关键词 Food-ingredient recognition multi-class classification deep learning convolutional neural network feature fusion
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Data fusion for fault diagnosis using multi-class Support Vector Machines 被引量:1
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作者 胡中辉 蔡云泽 +1 位作者 李远贵 许晓鸣 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第10期1030-1039,共10页
Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine... Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields. 展开更多
关键词 Data fusion fault diagnosis multi-class classification multi-class Support Vector Machines Diesel engine
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Engineering geological classification of the structural planes for hydroelectric projects in Emeishan Basalts 被引量:3
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作者 SUN Shu-qin HUANG Run-qiu +1 位作者 PEI Xiang-jun ZHAO Song-jiang 《Journal of Mountain Science》 SCIE CSCD 2016年第2期330-341,共12页
The scale and characteristics of rock mass are important indexes of the rock mass structural plane classification. This paper firstly analyzes the spatial distribution characteristics, the structural plane types (ori... The scale and characteristics of rock mass are important indexes of the rock mass structural plane classification. This paper firstly analyzes the spatial distribution characteristics, the structural plane types (original structural plane, tectonic structural plane and hypergenic structural plane) and the associated features of the Emeishan basalts and then studies the classification schemes of the built hydropower structure planes of different rock areas (the east district, the central district and the west district) in the Emeishan basalt distribution area, Southwest China. Based on the analysis and comparison of the scale and the engineering geological characteristics of the typical structure planes in the basalt hydroelectric Stations, the types of structural planes are used in the first order classification. The secondary order classification is made by considering the impact factors of rock mass quality, e.g., the state of the structural planes, infilling, joint opening, extending length, the grade of weathering and strength. The engineering geological classification for Emeishan basalt is proposed. Because there are no evidences of a large structure presenting in study area, the first-order (Ⅰ) controlling structural planes do not appear in the classification, there only appear Ⅱ, Ⅲ, Ⅳ and Ⅴ grade structural planes influencing the rock-mass quality. According to the different rock-block types in bedding fault zone, the second-grade (Ⅱ) structural planes consisted of bedding fault zone is further classified into Ⅱ1, Ⅱ2 and Ⅱ3. The third-grade (Ⅲ) structural planes constructed by intraformational faulted zones are not subdivided. According to the different characteristics of intrusion, alteration and weathering unloading structural planes, the Ⅳ grade structure plane is divided into Ⅳ1, Ⅳ2 and Ⅳ3. According to the development characteristics of joints and fractures, the V grade structure plane is divided into fracture Ⅴ1 and columnar joint Ⅴ2. In all, the structural planes are classified into four groups with nine subsets. The research proposes the engineering geological classification of the structural plane for the hydropower project in the Emishan basalts, and the result of the study has a potential application in similar regions. 展开更多
关键词 Emeishan basalt Hydroelectric project Structural plane Bedding fault zone Engineering geological classification
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Multi-Class Support Vector Machine Classifier Based on Jeffries-Matusita Distance and Directed Acyclic Graph 被引量:1
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作者 Miao Zhang Zhen-Zhou Lai +1 位作者 Dan Li Yi Shen 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2013年第5期113-118,共6页
Based on the framework of support vector machines (SVM) using one-against-one (OAO) strategy, a new multi-class kernel method based on directed aeyclie graph (DAG) and probabilistic distance is proposed to raise... Based on the framework of support vector machines (SVM) using one-against-one (OAO) strategy, a new multi-class kernel method based on directed aeyclie graph (DAG) and probabilistic distance is proposed to raise the multi-class classification accuracies. The topology structure of DAG is constructed by rearranging the nodes' sequence in the graph. DAG is equivalent to guided operating SVM on a list, and the classification performance depends on the nodes' sequence in the graph. Jeffries-Matusita distance (JMD) is introduced to estimate the separability of each class, and the implementation list is initialized with all classes organized according to certain sequence in the list. To testify the effectiveness of the proposed method, numerical analysis is conducted on UCI data and hyperspectral data. Meanwhile, comparative studies using standard OAO and DAG classification methods are also conducted and the results illustrate better performance and higher accuracy of the orooosed JMD-DAG method. 展开更多
关键词 multi-class classification support vector machine directed acyclic graph Jeffries-Matusitadistance hyperspcctral data
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