scholarly journals Ventilation Diagnosis of Angle Grinder Using Thermal Imaging

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2853
Author(s):  
Adam Glowacz
Keyword(s):  
Thermal Imaging ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Thermal Images ◽  
Feature Vectors ◽  
Air Inlet ◽  
Electrical Devices ◽  
Recognition Efficiency ◽  
Inlet Angle

The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences—Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix V or PCA of matrix V or histogram of matrix V. Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes (TRAG) was in the range of 98.5–100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.

scholarly journals Clinical Trial on the Characteristics of Zheng Classification of Pulmonary Diseases Based on Infrared Thermal Imaging Technology

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Jin-xia Ni ◽  
Si-hua Gao ◽  
Yu-hang Li ◽  
Shi-lei Ma ◽  
Tian Tian ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Pulmonary Disease ◽  
Thermal Imaging ◽  
Relative Temperature ◽  
Infrared Thermal Imaging ◽  
Imaging Technology ◽  
Thermal Images ◽  
Diagnosis Rate ◽  
Infrared Thermal Images

Zheng classification study based on infrared thermal imaging technology has not been reported before. To detect the relative temperature of viscera and bowels of different syndromes patients with pulmonary disease and to summarize the characteristics of different Zheng classifications, the infrared thermal imaging technology was used in the clinical trial. The results showed that the infrared thermal images characteristics of different Zheng classifications of pulmonary disease were distinctly different. The influence on viscera and bowels was deeper in phlegm-heat obstructing lung syndrome group than in cold-phlegm obstructing lung syndrome group. It is helpful to diagnose Zheng classification and to improve the diagnosis rate by analyzing the infrared thermal images of patients. The application of infrared thermal imaging technology provided objective measures for medical diagnosis and treatment in the field of Zheng studies and provided a new methodology for Zheng classification.

scholarly journals Classification of fruits and vegetables using ResNet model.

2021 ◽  
Author(s):  
P. Sukhetha ◽  
N. Hemalatha ◽  
Raji Sukumar
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Agricultural Field ◽  
K Nearest Neighbor ◽  
New Techniques ◽  
Learning Techniques ◽  
A Current

Abstract Agriculture is one of the important parts of Indian economy. Agricultural field has more contribution towards growth and stability of the nation. Therefore, a current technologies and innovations can help in order to experiment new techniques and methods in the agricultural field. At Present Artificial Intelligence (AI) is one of the main, effective, and widely used technology. Especially, Deep Learning (DL) has numerous functions due to its capability to learn robust interpretations from images. Convolutional Neural Networks (CNN) is the major Deep Learning architecture for image classification. This paper is mainly focus on the deep learning techniques to classify Fruits and Vegetables, the model creation and implementation to identify Fruits and Vegetables on the fruit360 dataset. The models created are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), ResNet Pretrained Model, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP). Among the different models ResNet pretrained Model performed the best with an accuracy of 95.83%.

scholarly journals Classification of EEG Signals using Nonlinear Features and Preprocessing Techniques

2021 ◽  
Vol 10 (5) ◽  
pp. 297-301
Author(s):  
Saneesh Cleatus T ◽  
Dr. Thungamani M
Keyword(s):  
Brain Tumor ◽  
Nearest Neighbor ◽  
Processing Technique ◽  
Support Vector ◽  
Statistical Features ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Nonlinear Features

In this paper we study the effect of nonlinear preprocessing techniques in the classification of electroencephalogram (EEG) signals. These methods are used for classifying the EEG signals captured from epileptic seizure activity and brain tumor category. For the first category, preprocessing is carried out using elliptical filters, and statistical features such as Shannon entropy, mean, standard deviation, skewness and band power. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used for the classification. For the brain tumor EEG signals, empirical mode decomposition is used as a pre-processing technique along with standard statistical features for the classification of normal and abnormal EEG signals. For epileptic signals we have achieved an average accuracy of 94% for a three-class classification and for brain tumor signals we have achieved a classification accuracy of 98% considering it as a two class problem.

scholarly journals Studi Komparasi Metode Machine Learning untuk Klasifikasi Citra Huruf Vokal Hiragana

2021 ◽  
Vol 5 (3) ◽  
pp. 905
Author(s):  
Muhammad Afrizal Amrustian ◽  
Vika Febri Muliati ◽  
Elsa Elvira Awal
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Image Classification ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Comparative Study

Japanese is one of the most difficult languages to understand and read. Japanese writing that does not use the alphabet is the reason for the difficulty of the Japanese language to read. There are three types of Japanese, namely kanji, katakana, and hiragana. Hiragana letters are the most commonly used type of writing. In addition, hiragana has a cursive nature, so each person's writing will be different. Machine learning methods can be used to read Japanese letters by recognizing the image of the letters. The Japanese letters that are used in this study are hiragana vowels. This study focuses on conducting a comparative study of machine learning methods for the image classification of Japanese letters. The machine learning methods that were successfully compared are Naïve Bayes, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor. The results of the comparative study show that the K-Nearest Neighbor method is the best method for image classification of hiragana vowels. K-Nearest Neighbor gets an accuracy of 89.4% with a low error rate.

scholarly journals Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

2020 ◽  
Vol 18 (1) ◽  
pp. 75
Author(s):  
M. Jupri ◽  
Riyanarto Sarno
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Fuzzy Ahp ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Time Required ◽  
Mining Algorithms

The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

scholarly journals Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 2005 ◽  
Author(s):  
Jiaying Deng ◽  
Wenhai Zhang ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
K Nearest Neighbor ◽  
Mode Decomposition

To avoid power supply hazards caused by cable failures, this paper presents an approach of incipient cable failure recognition and classification based on variational mode decomposition (VMD) and a convolutional neural network (CNN). By using VMD, the original current signal is decomposed into seven modes with different center frequencies. Then, 42 features are extracted for the seven modes and used to construct a feature vector as input of the CNN to classify incipient cable failure through deep learning. Compared with using the original signals directly as the CNN input, the proposed approach is more efficient and robust. Experiments on different classifiers, namely, the decision tree (DT), K-nearest neighbor (KNN), BP neural network (BP) and support vector machine (SVM), and show that the CNN outperforms the other classifiers in terms of accuracy.

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