The Prediction and Error Correction of Physiological Sign During Exercise Using Bayesian Combined Predictor and Naive Bayesian Classifier

Mesicek

Mobile Information Systems

2021

A technology must be developed to automatically identify extreme stress states of children who cannot properly express their emotions when recognizing dangerous situations, which threaten the safety of children, in real time. This study presents a stress-state identification model for children based on machine learning, biometric data, a smart band for collecting biometric data, and a mobile application for monitoring the stress state of the child classified. In addition, through an experiment comparing a dataset using only voice data and a dataset using both voice and heart rate data, we aimed to verify the effectiveness of the combination of the two biosignal datasets. As a result of the experiment, the SVM model showed the highest performance with an accuracy of 88.53% for the dataset using both voice data and heart rate data. The results of this study presented strong implications for the possibility of automating the stress-state identification of a child, and it is expected that the developed method can be used to take preventive measures for dangerous situations to children.

Section: Data Representation and Preprocessing In This Study N Instance(s) And Tmentioning

confidence: 99%

Modeling of Child Stress-State Identification Based on Biometric Information in Mobile Environment

Mesicek

Mobile Information Systems

2021

IET Generation Trans & Dist

“…The fault features of the feeder are extracted automatically by the alternating operation of multi-stage convolution and pooling. The feature vectors obtained by CNN are input to SVM, naive Bayesian classifier (NBC) [15], extreme learning machine (ELM) [16], and random forest (RF) [17] to complete fault identification. Meanwhile, the ability of the proposed approach to obtain fault feature vectors is verified.…”

Section: Introductionmentioning

confidence: 99%

Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network

Hou

Guo

2021

In order to extract the fault characteristics of the distribution power system automatically, a new method for fault identification based on parameter optimized variational mode decomposition (VMD) and convolutional neural network (CNN) is proposed. First, the VMD is used to decompose electric signals of the main transformer, including the current of the low-voltage inlet line, bus voltage, and so on. The obtained intrinsic mode functions should be arranged in sequence from low to high according to the central frequency. The VMD can obtain the time-frequency matrices. In order to decide the decomposition layers of the VMD adaptively, the partial mean of multi-scale permutation entropy is used to optimize it. Then, the time-frequency matrixes can be converted into pixel matrices by pseudo-colour coding. The pixel matrices which are appropriately compressed can be used as input of CNN. The CNN can autonomously extract the eigenvectors of fault. Finally, the classification algorithms of support vector machine (SVM), naive Bayesian classifier (NBC), extreme learning machine (ELM), and random forest (RF) are used to train and test the feature vectors extracted by CNN to verify the effectiveness of the method. The experiments show that the proposed method has excellent stability, fast convergence speed, and high precision. The technique can effectively complete the fault identification of the distribution network.

ICST Transactions on Scalable Information Systems

“…Most of methods extract single features with manually feature selection and lack multi-feature fusion. Classification methods mainly adopt machine learning classification methods such as linear discriminant method, Bayesian classifier and support vector machine [9,10].…”

Section: Introductionmentioning

confidence: 99%

Channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition

Yang¹

2018

In order to solve the problems of weak generalization ability and low classification accuracy in motor imagery EEG signal classification, this paper proposes a channel space weighted fusion-oriented feature pyramid network for motor imagery EEG signal recognition. First, the short-time Fourier transform is used to obtain the EEG time-frequency map. Then, it builds a new feature pyramid network(FPN). The attention mechanism module is integrated into the FPN module, and the channel spatial weighted fusion-oriented feature pyramid network is proposed. This new structure can not only learn the weight of important channel features in the feature map, but also learn the representation of important feature areas in the network layers. Meanwhile, Skip-FPN module is added into the network structure, which fuses more details of EEG signals through short connections. The Dropout layer is added to prevent network training from over-fitting. In the classification model, we improve the AdaBoost algorithm to automatically update the base learner according to the classification error rate. Finally, the proposed model is used to classify the test data and the Kappa value is used as the evaluation index. Compared with the state-of-the-art motor image EEG signal recognition methods, the proposed method achieves better performance on the BCI Competition IV 2b data set. It has good generalization ability and can improve the classification effect.