Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

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ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519411004101 ◽

2011 ◽

Vol 11 (04) ◽

pp. 897-915 ◽

Cited By ~ 18

Author(s):

ROSHAN JOY MARTIS ◽

CHANDAN CHAKRABORTY

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Normal Sinus Rhythm ◽

Optimization Technique ◽

Back Propagation ◽

Principal Component ◽

Disease Diagnosis ◽

Back Propagation Neural Network ◽

Polynomial Kernel ◽

Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

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Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Energies ◽

10.3390/en12020218 ◽

2019 ◽

Vol 12 (2) ◽

pp. 218 ◽

Cited By ~ 5

Author(s):

Nan Wei ◽

Changjun Li ◽

Jiehao Duan ◽

Jinyuan Liu ◽

Fanhua Zeng

Keyword(s):

Deep Learning ◽

Natural Gas ◽

Load Forecasting ◽

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

Support Vector ◽

Weather Factors ◽

Original Dataset ◽

Proposed Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

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Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

Journal of Spectroscopy ◽

10.1155/2020/6631234 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Yinglin Yang ◽

Xin Zhang ◽

Jianwei Yin ◽

Xiangyang Yu

Keyword(s):

Near Infrared ◽

Nearest Neighbor ◽

Diffuse Reflectance Spectrum ◽

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

Support Vector ◽

Site Classification ◽

Classification Models ◽

K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

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Study on Mid-Infrared Transmittance Spectroscopy for Fast Measurement of Crude Fat Content in Fish Feeds Based on BPNN and LS-SVM

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.460-461.816 ◽

2011 ◽

Vol 460-461 ◽

pp. 816-820 ◽

Cited By ~ 1

Author(s):

Feng Le Zhu ◽

Yong He

Keyword(s):

Back Propagation ◽

Principal Component ◽

Back Propagation Neural Network ◽

Fat Content ◽

Support Vector ◽

Mid Infrared ◽

Infrared Transmittance ◽

Fish Feeds ◽

Infrared Spectrometer ◽

New Variables

The crude fat content in fish feeds was determined using mid-infrared transmittance spectroscopy and chemometrics fast and non-destructively. A total of 225 samples were prepared for spectra collecting from a FT/IR-4000 Fourier Transform Infrared Spectrometer (400-4000cm-1). Principal component analysis (PCA) was carried out and spectral data were compressed into several new variables, which can explain the most variance of original spectra. The first six PCs were used as inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) to create the calibration models. Compared with BPNN, a slightly better prediction precision was achieved based on LS-SVM with correlation coefficient (R) = 0.9757 and root mean square error for prediction (RMSEP) = 0.2579. The overall results indicated that mid-infrared spectroscopy incorporated to chemometrics was promising for the accurate assessment of crude fat content in fish feeds.

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Identification of Similar Chinese Congou Black Teas Using an Electronic Tongue Combined with Pattern Recognition

Molecules ◽

10.3390/molecules24244549 ◽

2019 ◽

Vol 24 (24) ◽

pp. 4549 ◽

Cited By ~ 4

Author(s):

Danyi Huang ◽

Zhuang Bian ◽

Qinli Qiu ◽

Yinmao Wang ◽

Dongmei Fan ◽

...

Keyword(s):

Discriminant Analysis ◽

Prediction Models ◽

Back Propagation ◽

Electronic Tongue ◽

Principal Component ◽

Black Tea ◽

Back Propagation Neural Network ◽

Support Vector ◽

Good Prediction ◽

Grade Prediction

It is very difficult for humans to distinguish between two kinds of black tea obtained with similar processing technology. In this paper, an electronic tongue was used to discriminate samples of seven different grades of two types of Chinese Congou black tea. The type of black tea was identified by principal component analysis and discriminant analysis. The latter showed better results. The samples of the two types of black tea distributed on the two sides of the region graph were obtained from discriminant analysis, according to tea type. For grade discrimination, we determined grade prediction models for each tea type by partial least-squares analysis; the coefficients of determination of the prediction models were both above 0.95. Discriminant analysis separated each sample in region graph depending on its grade and displayed a classification accuracy of 98.20% by cross-validation. The back-propagation neural network showed that the grade prediction accuracy for all samples was 95.00%. Discriminant analysis could successfully distinguish tea types and grades. As a complement, the models of the biochemical components of tea and electronic tongue by support vector machine showed good prediction results. Therefore, the electronic tongue is a useful tool for Congou black tea classification.

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HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽

10.3390/s20030660 ◽

2020 ◽

Vol 20 (3) ◽

pp. 660 ◽

Cited By ~ 1

Author(s):

Fang Liu ◽

Liubin Li ◽

Yongbin Liu ◽

Zheng Cao ◽

Hui Yang ◽

...

Keyword(s):

Kernel Method ◽

Back Propagation ◽

Vibration Signal ◽

Back Propagation Neural Network ◽

Industrial Applications ◽

Performance Degradation ◽

Support Vector ◽

Krill Herd Algorithm ◽

Krill Herd ◽

Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Applied Sciences ◽

10.3390/app8091632 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1632 ◽

Cited By ~ 2

Author(s):

Zahra Rezaei ◽

Ali Selamat ◽

Arash Taki ◽

Mohd Mohd Rahim ◽

Mohammed Abdul Kadir ◽

...

Keyword(s):

Vulnerable Plaque ◽

Plaque Rupture ◽

Back Propagation ◽

Texture Features ◽

Local Binary Patterns ◽

Relevant Information ◽

Back Propagation Neural Network ◽

Common Mechanism ◽

Support Vector ◽

Virtual Histology

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

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