scholarly journals Fault Diagnosis of a Rotor and Ball-Bearing System Using DWT Integrated with SVM, GRNN, and Visual Dot Patterns

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4806 ◽  
Author(s):  
Wen-Lin Chu ◽  
Chih-Jer Lin ◽  
Kai-Chun Kao
Keyword(s):  
Motor Control ◽  
Real Time ◽  
Ball Bearing ◽  
Warning System ◽  
Support Vector ◽  
Discrete Wavelet ◽  
General Regression Neural Networks ◽  
Experimental Process ◽  
Time Motor ◽  
General Regression

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

2021 ◽  
pp. 104-135
Author(s):  
Sharad Sarjerao Jagtap ◽  
Rajesh Kumar M.
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

scholarly journals Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks

2007 ◽  
Vol 19 (10) ◽  
pp. 2840-2864 ◽  
Author(s):  
Mingyu Zhong ◽  
Dave Coggeshall ◽  
Ehsan Ghaneie ◽  
Thomas Pope ◽  
Mark Rivera ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cross Validation ◽  
Smoothing Parameter ◽  
Support Vector ◽  
Optimal Value ◽  
General Regression Neural Networks ◽  
Data Statistics ◽  
Interpretable Models ◽  
General Regression ◽  
The Relationship

Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation or clustering. In this article, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.

scholarly journals A Fast Deploying Monitoring and Real-Time Early Warning System for the Baige Landslide in Tibet, China

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6619
Author(s):  
Yongbo Wu ◽  
Ruiqing Niu ◽  
Yi Wang ◽  
Tao Chen
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Prediction Method ◽  
Surface Displacement ◽  
Warning System ◽  
Early Warning Systems ◽  
Support Vector ◽  
Time Prediction ◽  
Svm Model

Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Baige landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Baige landslide with high accuracy.

CURRENCY CRISIS FORECASTING WITH GENERAL REGRESSION NEURAL NETWORKS

2006 ◽  
Vol 05 (03) ◽  
pp. 437-454 ◽  
Author(s):  
LEAN YU ◽  
KIN KEUNG LAI ◽  
SHOU-YANG WANG
Keyword(s):  
Currency Crisis ◽  
Warning System ◽  
Southeast Asian ◽  
General Regression Neural Network ◽  
Forecasting Model ◽  
Currency Exchange ◽  
General Regression Neural Networks ◽  
General Regression ◽  
High Degree ◽  
Exchange Rates Volatility

The main purpose of this study is to devise a general regression neural network (GRNN)-based currency crisis forecasting model for Southeast Asian economies based upon the disastrous 1997–1998 currency crisis experience. For this some typical indicators of currency exchange rates volatility are first chosen, then these indicators are input into GRNN for training, and finally the trained GRNN is used for future crisis prediction. To verify the effectiveness of the proposed currency crisis forecasting approach, four typical Southeast Asian currencies, Indonesian rupiah, Philippine peso, Singapore dollar and Thai baht, are selected. Meantime we compare its performance with those of other forecasting methods to evaluate the forecasting ability of the proposed approach. Empirical results obtained reveal that the proposed currency crisis forecasting model has a surprisingly high degree of accuracy in judging the currency crisis level of each country in specified time period, implying that our proposed approach can be used as a feasible currency crisis early-warning system to predict currency crisis level for other countries around the world.

scholarly journals Development of a real-time regional-inundation forecasting model for the inundation warning system

2013 ◽  
Vol 15 (4) ◽  
pp. 1391-1407 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Hsuan-Yu Lin ◽  
Yang-Ching Chou
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Control Point ◽  
Warning System ◽  
Computational Method ◽  
Support Vector ◽  
Forecasting Model ◽  
Time Data ◽  
Inundation Maps ◽  
Proposed Model

Accurate forecasts of the inundation depth are necessary for inundation warning and mitigation. In this paper, a real-time regional forecasting model is proposed to yield 1- to 3-h lead time inundation maps. First, the K-means based cluster analysis is developed to group the inundation depths and to indentify the control points. Second, the support vector machine is used as the computational method to develop the point forecasting module to yield inundation forecasts for each control point. Third, based on the forecasted depths and the geographic information, the spatial expansion module is developed to expand the point forecasts to the spatial forecasts. An actual application to Siluo Township, Taiwan, is conducted to demonstrate the advantage of the proposed model. The results indicate that the proposed model can provide accurate inundation maps for 1- to 3-h lead times. The accurate long lead time forecasts can extend the lead time to allow sufficient time to take emergency measures. Furthermore, the proposed model is an efficient process that can be trained rapidly with real-time data and is more suitable to be integrated with the decision support system. In conclusion, the proposed modeling technique is expected to be useful to support the inundation warning systems.

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