scholarly journals Development and Evaluation of Traffic Count Sensor with Low-Cost Light-Detection and Ranging and Continuous Wavelet Transform: Initial Results

2019 ◽  
Vol 2673 (11) ◽  
pp. 209-219
Author(s):  
Ravi Jagirdar ◽  
Joyoung Lee ◽  
Kitae Kim ◽  
Min-Wook Kang
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Supervised Machine Learning ◽  
Light Detection And Ranging ◽  
Support Vector ◽  
Continuous Wavelet ◽  
Light Detection ◽  
Distance Information ◽  
Traffic Count ◽  
Distance Data

This paper presents a cost-effective, non-intrusive, and easy-to-deploy traffic count data collection method using two-dimensional light-detection and ranging (LiDAR) technology. The proposed method integrates a LiDAR sensor, continuous wavelet transform (CWT), and support vector machine (SVM) into a single framework for traffic count. LiDAR is adopted since the technology is economical and easily accessible. Moreover, its 360° visibility and accurate distance information make it more reliable compared with radar, which uses electromagnetic waves instead of light rays. The obtained distance data are converted into the signals. CWT is employed to detect any deviation in distance profile, because of its efficiency in detecting modest changes over a period of time. SVM is one of the supervised machine learning tools for data classification and regression. In the methodology, the SVM is applied to classify the distance data points obtained from the sensor into detection and non-detection cases, which are highly complex. Proof-of-concept (POC) test is conducted in three different places in Newark, New Jersey, to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances in vehicle count collection, resulting in 83–94% accuracy. It is discovered that the accuracy of the proposed method is affected by the color of the exterior surface of a vehicle.

Multifault Diagnosis of Combined Hydraulic and Mechanical Centrifugal Pump Faults Using Continuous Wavelet Transform and Support Vector Machines

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Janani Shruti Rapur ◽  
Rajiv Tiwari
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Strong Association ◽  
Principal Component ◽  
Fault Classification ◽  
Support Vector ◽  
Operating Speed ◽  
Continuous Wavelet ◽  
Centrifugal Pumps ◽  
Multiple Faults

Abstract Centrifugal pumps (CPs) fail due to anomalies in fluid flow patterns and/or due to failure of mechanical subsystems in them. In this work, a technique built on the multiclass support vector machine (MSVM) is developed to identify multiple faults in the CP. In addition, the complex problem of fault combinations and their classification is dealt with in this work. The combination of features from motor line current sensors and accelerometers is used to train the algorithm. To take into account the transient as well as harmonic components of fault signatures, continuous wavelet transform (CWT) analysis is used. Thereafter, the most important information from the CWT coefficients is selected using the two proposed novel methods CWT-based on energy (BE)-MSVM and CWT-principal component analysis (PCA)-MSVM, which are BE as well as PCA, respectively. It is experimentally observed that faults in the CPs have a very strong association with its operating speed. Thus, in order to make the CP versatile in operation, it is important that the fault diagnosis methodology is also efficient at large speed range of CP operation. This work attempts to develop a fault classification methodology, which is independent of the CP operating speed.

scholarly journals Classification of Refrigerant Flow Noise of Air Conditioners Based on Continuous Wavelet Transform and Support Vector Machine

2020 ◽  
Vol 10 (11) ◽  
pp. 3959
Author(s):  
Un-Chang Jeong
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Support Vector ◽  
Continuous Wavelet ◽  
Air Conditioner ◽  
Time Frequency ◽  
Flow Noise ◽  
The Time Domain

This study proposes a classification method that uses the continuous wavelet transform and the support vector machine approach to classify refrigerant flow noises generated in an air conditioner. The air conditioning noise was identified as an abnormal signal by the use of the first- and second-order moments. The start and end times of refrigerant flow noises were identified by detecting the singularities of the continuous wavelet transform coefficient in the time domain and by means of listening to the measured sounds. Further, the time-frequency characteristics of refrigerant flow noise were analyzed with the continuous wavelet transform. For the support vector machine-based classification of refrigerant flow noise in an air conditioner, the grid search method was used to determine kernel hyperparameters. Five-fold cross validation was employed for the application of the support vector machine to the classification of air conditioner refrigerant noise. In addition, measured sound sources were modified based on classified refrigerant flow noise to compare the classification accuracy of a jury test with the results of the support vector machine.

scholarly journals Myopathy Detection and Classification Based on the Continuous Wavelet Transform

2019 ◽  
Vol 15 (4) ◽  
Author(s):  
Abdelali Belkhou ◽  
Abdelouahed Achmamad ◽  
Atman Jbari
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Diseases ◽  
Kernel Functions ◽  
Support Vector ◽  
Continuous Wavelet ◽  
K Nearest Neighbor ◽  
Signal Characteristics

Electromyography (EMG) is the study of the electrical activity of the muscle. This technique is often used in the diagnosis of neuromuscular diseases. Myopathy is one of these cases, which affect the muscle and causes many changes in the electromyography signal characteristics. This paper presents a new method for analysis and classification of normal and myopathy EMG signals based on continuous wavelet transform (CWT). Classification algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Discriminant Analysis (DA) and Naïve Bayes (NB) were used as classifiers in our study. Five Features were extracted from the continuous wavelet analysis and used as inputs to the mentioned classifiers. Comparison between different classification methods developed in this study was made by evaluation of their results based on multiple scalar performances, mainly accuracy, sensitivity, and specificity. Different combinations of features with different kernel functions were discussed to achieve better performances. Results showed that k-NN classifier achieved the best performances with an accuracy value of 93.68%.

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