Binary Tree Support Vector Machine Research Articles

Research on objective evaluation methods of labor education teaching for college students in higher education

Abstract Labor education in colleges and universities in the new era is the fundamental task of implementing the principle of establishing morality and educating people, as well as an essential hand in cultivating college students with all-round development of morality, intelligence, physicality, aesthetics, and labor. This paper focuses on the objective evaluation method of college students’ labor education to conduct research. Using the improved binary tree support vector machine multi-class classification algorithm, an evaluation model for college labor education based on a binary tree support vector machine has been constructed, and an evaluation system for labor education has been developed. The results show that the majors that have compulsory labor education courses, elective labor education courses, and have added labor education modules in their major courses are all art and sports, accounting for more than 50% on average. The percentages of liberal arts, science, economics and management, art and sports, medicine and science, and agriculture, which did not offer labor education-related courses, were 26.35%, 22.16%, 19.91%, 13.59%, 11.03%, and 13.19%, respectively. It is evident that labor education courses are not distributed equally in colleges and universities, and there are still some majors that do not offer labor education courses. In terms of labor education, most of the labor education through Civics and Political Science classes is provided to students. Managers are those who choose the most specialized courses. Students also tend to participate in bicultural education and social practice activities, accounting for 29.02% and 43.01%, respectively. It shows that the algorithm in this paper can objectively and effectively evaluate labor education in colleges and universities and identify the deficiencies in education.

Vocal music curriculum design and teaching quality evaluation in colleges and universities based on interactive structure model

Abstract Aiming at the current phenomenon of low teacher-student interaction in vocal music teaching, this paper combines the interaction structure model for the innovation and practice of college vocal music courses. Then, this paper uses Euclidean distance to improve the binary tree SVM multi-class classification algorithm, calculates the distance between the teaching samples of each vocal music course, classifies the samples for processing, and obtains the DBT-SVM multi-class classification algorithm. Finally, the DBT-SVM algorithm was used to establish an evaluation model for vocal course teaching quality in colleges and universities to assess the quality of vocal course teaching models. Each three-level index of teaching motivationality has an evaluation score above 80, which is a good level. The evaluation score for the pedagogical index is 89.44, which is almost excellent. The average score of the second-level index of interpersonal interaction is 74.52, which needs to be further improved. The teaching model of the college vocal music course proposed in this paper lays the foundation for the interactive transformation of vocal music teaching and provides a reference model for the improvement of the learning effect of college students’ vocal music courses.

Research on feature vector construction and classification recognition of typical mine microseismic waveforms

In the realm of mining seismology, the identification of microseismic waveforms is a critical step for enhancing the precision of microseismic event localization. Addressing the complexities and diverse sources of noise in subterranean mining environments, this study has developed a novel methodology for the automated categorization and recognition of characteristic microseismic waveforms. Initially, a comprehensive database for mining-induced microseismic waveforms was established, catering to their non-stationary and nonlinear attributes. Based on the distinct features of different microseismic waveforms, this study introduces an approach for classification and identification that relies on waveform characteristics. A feature selection model, KNN-LOO (K-Nearest Neighbor - Leave-One-Out), was employed to optimize the feature vectors, in addition to the utilization of Principal Component Analysis (PCA) for dimensionality reduction of the feature vectors. Ultimately, an enhanced BT-SVM (Binary Tree-Support Vector Machine) model was developed to accomplish the automated classification and identification of mining-induced microseismic waveforms. Experimental results indicate that, compared to conventional SVM classification methods, the proposed methodology not only exhibits theoretical and mechanistic advancements but also significantly boosts performance in practical applications. Specifically, the average misclassification rate for each category of waveforms is reduced to less than 5%, and the overall accuracy has increased by 20%. This considerably elevates both the accuracy and speed of microseismic waveform classification and recognition, thereby providing valuable insights for monitoring and analyzing microseismic activities within mining operations as well as enhancing the safety protocols for mineral extraction.

Research on Emotion Recognition Method of Flight Training Based on Multimodal Fusion

The emotional activities of the human body are mainly regulated by the autonomic nervous system, the central nervous system, and the advanced cognition of the human brain. This paper proposes an emotional state recognition method in pilot training tasks based on multimodal information fusion. A set of emotion perception recognition systems, a two-dimensional valence-arousal emotional model, and a multimodal information intelligent perception model were established based on the human-computer interaction mode during flight training; an intelligent perception system was designed to collect and intelligently perceive four kinds of peripheral physiological signals of pilots in real-time. And based on traditional machine learning models, a binary tree support vector machine was designed to optimize and improve the multimodal information co-integration decision model, which increased the accuracy of emotional state recognition in flight training by 37.58% on average. The experimental result showed that it realizes accurate monitoring and identification of real-time emotional state, helps to improve the effect of flight training and flight safety, maintains the efficiency of operation, and has important research significance and application prospects in the field of pilot training.

Optimization of One versus All-SVM using AdaBoost algorithm for rainfall classification and estimation from multispectral MSG data

In this paper, we implement the AdaBoost algorithm to optimize the classifications results of precipitations intensities carried out by One versus All strategy using Support Vector Machine (OvA-SVM). The model developed which combines the AdaBoost algorithm with a multiclass SVM is applied to images from the MSG (Meteosat Second Generation) satellite. Other variants to build multiclass SVMs, such as the OvO-SVM (One versus One SVM), SBT-SVM (Slant Binary Tree SVM) and DDAG-SVM (Decision Directed Acyclic Graph) are also implemented on which we tested the AdaBoost algorithm. The study showed that the AdaBoost algorithm performed better in the case of the OvA-SVM variant compared to the other variants.In order to evaluate the elaborated model, some classification techniques, such as the ECST Enhanced Convective Stratiform Technique (ECST), the SART where the Support vector machine, Artificial neural network and Random forest classifiers are combined, the Convective/Stratiform Rain Area Delineation Technique (CS-RADT) and the Random Forest technique (RFT) are applied. The classification results obtained show that AdaBoost with OvA-SVM (AdaOvA-SVM) presents very interesting performances where the evaluation parameters POD, POFD, FAR, BIAS, CSI and PC indicate the values 95.2%, 12.4%, 14.7%, 0.9, 88.1% and 96.5% respectively. Indeed, the AdaOvA-SVM technique has surpassed the CS-RADT, ECST and RFT techniques. As for the comparison with the SART, we noted that OvA-SVM presents very close results. The same trend was also observed when estimating precipitation. At the end of this study, it is shown that the AdaBoost algorithm performs better on a weak classifier or on a strong classifier operating in an unfavorable environment.

Evaluation of College English Teaching Quality Based on Improved BT-SVM Algorithm.

With the development of teaching evaluation program, colleges and universities have reformed according to the actual situation of the school. With the development of evaluation activities, many universities are eager to establish their own teaching quality evaluation system, so as to pre-evaluate the teaching quality of schools. SVM is one of the most widely used machine learning algorithms that enables efficient statistical learning with a very limited number of samples. Considering the excellent learning performance of SVM, it is very suitable for the teaching quality evaluation system. In this paper, we optimize the existing multiple classification algorithm for binary trees and propose a new method. Learning the popular teaching quality evaluation system in colleges and universities, the binary tree support vector machine classification algorithm, and design comparison experiment, the experimental results show that the evaluation model proposed in this paper has strong generalization ability and higher classification accuracy and better classification efficiency.

Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism

Fatigue driving is one of the main causes of traffic accidents today. In this study, a fatigue driving detection system based on a 3D convolutional neural network combined with a channel attention mechanism (Squeeze-and-Excitation module) is proposed. The model obtains information of multiple channels of grayscale, gradient and optical flow from the input frame. The temporal and spatial information contained in the feature map is extracted by three-dimensional convolution, after which the feature map is fed to the attention mechanism module to optimize the feature weights. EAR and MAR are used as fatigue analysis criteria and, finally, a full binary tree SVM classifier is used to output the four driving states. In addition, this study uses the frame aggregation strategy to solve the frame loss problem well and designs application software to record the driver’s status in real time while protecting the driver’s facial privacy and security. Compared with other classical fatigue driving detection methods, this method extracts features from temporal and spatial dimensions and optimizes the feature weights using the attention mechanism module, which significantly improves the fatigue detection performance. The experimental results show that 95% discriminative accuracy is achieved on the FDF dataset, which can be effectively applied to driving fatigue detection.

Evaluation of teaching quality based on binary tree support vector machine

In order to solve the reliability of the evaluation results of teaching quality in universities and colleges, an improved model of teaching evaluation based on the Support vector machine was put forward. In this model, the evaluator does not need to give an evaluation result of the teacher’s teaching quality, but gives the score of each evaluation index, and then calls the Support vector machine, automatic classification of teachers’ teaching quality. The experiment proves that the improved algorithm can improve the teaching quality evaluation accuracy and the result is better.

Emergency valve fault location based on improved optimal binary tree support vector machine

This paper presents a new algorithm IOBT SVM (improved optimal binary tree support vector machine) to locate the fault of the passenger train emergency valve and improve the test efficiency of the brake system. To reduce the classification classes and identify different faults, every two carriage faults are merged into one class and multiple sets of air pressure curve characteristics are extracted in sections. Furthermore, the structure of the classification tree is constructed from the leaf nodes to the root node combining with the class separability between every two classes. Every two most similar classes are selected in turn for classification and will be merged into one class after classification until the last class is left. Experimental results show that the structure of the classification tree generated by this algorithm is reasonable, which improves the efficiency and accuracy of emergency valve fault location.

Studies on the evaluation of college classroom teaching quality based on SVM multiclass classification algorithm

In order to improve the evaluation accuracy and efficiency of the quality of classroom teaching in colleges and universities, combined with the teaching process and evaluation system of colleges and universities, a multi-class classification algorithm is proposed, namely, the Distance binary tree support vector machine based on Euclidean distance (DBT-SVM) algorithm, and the algorithm is used to predict the quality evaluation of classroom teaching in colleges and universities. The algorithm uses the Euclidean distance of the center of the two nearest sample classes to influence the classification, so that the first isolated classes can be separated first. Experimentally, the algorithm can improve the efficiency and accuracy of classification and solve the problem of multi-class classification.

Remote Attestation for Intelligent Electronic Devices in Smart Grid Based on Trusted Level Measurement

To reduce the implementation complexity and excessive overhead caused by remote attestation, we propose a new remote attestation scheme based on trusted level measurement considering the limited system resources of the Intelligent electronic devices (IEDs) in smart grid. In this scheme, a lightweight trusted level measurement mechanism is designed, and a new dynamic weighted multi-classifier integration method based on Binary tree Support vector machine (BT-SVM) is proposed. The trusted level of configuration information and running state of an IED are measured through trusted third party. The measurement parameters and attestation period are dynamically adjusted according to different trusted levels of an IED, so that reducing computational overhead and communication delays in remote attestation process. Analysis and experiments show that the proposed scheme can enhance the security of IEDs in smart grid by occupying a small amount of computation and communication resources.

Remote Attestation for Intelligent Electronic Devices in Smart Grid Based on Trusted Level Measurement

To reduce the implementation complexity and excessive overhead caused by remote attestation, we propose a new remote attestation scheme based on trusted level measurement considering the limited system resources of the Intelligent electronic devices (IEDs) in smart grid. In this scheme, a lightweight trusted level measurement mechanism is designed, and a new dynamic weighted multi-classifier integration method based on Binary tree Support vector machine (BT-SVM) is proposed. The trusted level of configuration information and running state of an IED are measured through trusted third party. The measurement parameters and attestation period are dynamically adjusted according to different trusted levels of an IED, so that reducing computational overhead and communication delays in remote attestation process. Analysis and experiments show that the proposed scheme can enhance the security of IEDs in smart grid by occupying a small amount of computation and communication resources.

Detection of Winding Faults Using Image Features and Binary Tree Support Vector Machine for Autotransformer

Autotransformer (AT) is the most core power supply equipment, and overvoltage and short circuit (SC) fault may lead to winding deformation, which will have a negative impact on its insulation and even affect the operation of a train. The frequency response analysis (FRA) is widely used for detecting winding faults in a transformer. However, the direct measure of FRA for each split winding fails because the split windings are adopted to satisfy the impedance requirement of a high-speed railway, where the windings are connected inside the tank. A novel fault interpretation method based on image features and binary tree support vector machine (SVM) is proposed, which can get the condition of three windings in one measurement. Winding faults caused by different windings are simulated, including SC defect, axial deformation, and series capacitance variation, and the FRA curves are measured under various faults. Then, the features of the gray-level gradient co-occurrence matrix and the gray-level difference statistics are got from the polar plot of FRA. Finally, the image features are used as the inputs to the binary tree SVM for fault type and faulty winding classification. The results show that the proposed method has high accuracy for identifying fault type and faulty winding in AT.

Binary Tree SVM Based on Analytic Hierarchy Process and Its Application to Fault Diagnosis

Structure design has a great influence on classification accuracy of the binary tree support vector machines, to design the structure reasonably, a multi-class algorithm of the binary tree SVM based on analytic hierarchy process is proposed. Based on analytic hierarchy process (AHP), the model of an assessment system is established. First combine theoretical analysis with expert advice, make a comprehensive survey on several factors and confirm the weight of faults, then put the faults in the right order based on the weight, last the structure of the binary tree is designed. It is proved by the fault diagnosis of a motor that the method runs well in classification accuracy and generalization ability and is suitable for multi-class.

On-line fault diagnosis for proton exchange membrane fuel cells based on a fast electrochemical impedance spectroscopy measurement

In this paper, an on-line fault diagnosis method for proton exchange membrane fuel cells is proposed based on a fast electrochemical impedance spectroscopy measurement. Specifically, a fast electrochemical impedance spectroscopy measurement system is developed in order to reduce the measurement cost and time. The current pulse injection is used as the disturbance injection method, and the electrochemical impedance spectroscopy is obtained by the continuous wavelet transform and the maximum likelihood estimation. Then, the parameters of an improved equivalent circuit model are identified according to the electrochemical impedance spectroscopy, and three model parameters are selected as the features for fault diagnosis. On this basis, a multi-fault diagnosis algorithm is proposed based on the binary tree support vector machine classifier. Experimental results show that the proposed fault diagnosis method can realize accurate and fast on-line fault diagnosis for proton exchange membrane fuel cells.

Urban area GNSS in‐car‐jammer localization based on pattern recognition

A GNSS in-car-jammer poses a great threat to the security of navigation satellite signals for civilian applications. Traditional interference localization methods such as received signal strength (RSS), signal arrival angle (AOA), and signal arrival time difference (TDOA) have poor performance in urban environments due to line-of-sight (LoS) path obstruction and reflection. In this paper, a new interference localization method, which works on monitoring networks and follows the pattern recognition principle is proposed. The algorithm locates the jammer by learning the carrier-to-noise density ratio features that are extracted from the data of all monitoring nodes of the network with the means of binary tree support vector machine (SVM) classification technology. Its performance is tested by two simulated interference scenarios: the urban canyon and the urban residential area. Results show that the algorithm provides better localization reliability and smaller position errors compared with traditional localization methods.

Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for Valve Fault of Reciprocating Compressor

According to the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of reciprocating compressor valve based on the modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. Firstly, to eliminate the noise interference, the VMD method with superior anti-interference performance was utilized to a strong non-stationarity vibration signal for all fault states. To overcome the obstacle of the conventional multiscale entropy (MSE) may yield undefined entropy or an imprecise estimation of entropy by the large scale factor, the MMSE method provided for moving-average procedure by replacing mean-average coarse-grained procedure was developed to the vibration signals, and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and select the optimal sensitivity scale feature. Finally, binary tree of Support Vector Machine (BTSVM), suitable for small sample classification, was selected as the classifier to identify the reciprocating compressor valve fault type. By analyzing the experimental data, it can be shown that the method can effectively identify the fault type of reciprocating compressor valve.

The grade classification algorithm of breast tumor based on ultrasound RF signals

A novel efficient method based on the ultrasound radio frequency (RF) signals is proposed to distinguish the breast tumors grades. First, we utilize the multi-scale geometric characteristic of Shearlet transformation to extract the multi-scale and multi-directional features of ultrasound RF signal, and then reduce the high-dimensional Shearlet features by multi-scale directional binary pattern which can effectively preserve the sufficient discriminated information. At last, we draw on the feature difference between different grades of breast tumors to design a cascade binary tree SVM classifier which not only overcome the problem of sample quantity disequilibrium but also conform to the subjective diagnosis rule of sonographer. Extensive experiments on 928 breast ultrasound RF signals collected from the hospital demonstrate the effectiveness of the new proposed method and its precision, sensitivity, specificity, PPV, NPV and MCC are 89.29%, 75.62%, 94.54%, 97%, 98.3% and 81.01%, respectively.

A Novel Method for the Recognition of Air Visibility Level Based on the Optimal Binary Tree Support Vector Machine

As the traditional methods for the recognition of air visibility level have the disadvantages of high cost, complicated operation, and the need to set markers, this paper proposes a novel method for the recognition of air visibility level based on an optimal binary tree support vector machine (SVM) using image processing techniques. Firstly, morphological processing is performed on the image. Then, whether the region of interest (ROI) is extracted is determined by the extracted feature values, that is, the contrast features and edge features are extracted in the ROI. After that, the transmittance features of red, green and blue channels (RGB) are extracted throughout the whole image. These feature values are used to construct the visibility level recognition model based on optimal binary tree SVM. The experiments are carried out to verify the proposed method. The experimental results show that the recognition accuracies of the proposed method for four levels of visibility, i.e., good air quality, mild pollution, moderate pollution, and heavy pollution, are 92.00%, 92%, 88.00%, and 100.00%, respectively, with an average recognition accuracy of 93.00%. The proposed method is compared with one-to-one SVM and one-to-many SVM in terms of training time and recognition accuracy. The experimental results show that the proposed method can distinguish four levels of visibility at a relatively satisfactory level, and it performs better than the other two methods in terms of training time and recognition accuracy. This proposed method provides an effective solution for the recognition of air visibility level.

Cross-Voting SVM Method for Multiple Vehicle Classification in Wireless Sensor Networks.

A novel multi-class classification method named the voting-cross support vector machine (SVM) method was proposed in this study, for classifying vehicle targets in wireless sensor networks. The advantages and disadvantages of available methods were summarized, after a comparative analysis of commonly used multi-objective classification algorithms. To improve the classification accuracy of multi-class classification and ensure the low complexity of the algorithm for engineering implementation on wireless sensor network (WSN) nodes, a framework was proposed for cross-matching and voting on the category to which the vehicle belongs after combining the advantages of the directed acyclic graph SVM (DAGSVM) method and binary-tree SVM method. The SVM classifier was selected as the basis two-class classifier in the framework, after comparing the classification performance of several commonly used methods. We utilized datasets acquired from a real-world experiment to validate the proposed method. The calculated results demonstrated that the cross-voting SVM method could effectively increase the classification accuracy for the classification of multiple vehicle targets, with a limited increase in the algorithm complexity. The application of the cross-voting SVM method effectively improved the target classification accuracy (by approximately 7%), compared with the DAGSVM method and the binary-tree SVM method, whereas time consumption decreased by approximately 70% compared to the DAGSVM method.