Lubrication Leakage Alarm of Wind Power Gearbox Based on K-nearest Neighbor and Back Propagation Neural Network

2013 ◽  
Vol 12 (15) ◽  
pp. 3152-3157
Author(s):  
Chun-Yao Lee ◽  
Chih-Ju Chou ◽  
Chun-Chi Chen ◽  
Ryan Liu ◽  
I. Hsiang Tse ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
K Nearest Neighbor

scholarly journals Health Monitoring for Balancing Tail Ropes of a Hoisting System Using a Convolutional Neural Network

2018 ◽  
Vol 8 (8) ◽  
pp. 1346 ◽  
Author(s):  
Ping Zhou ◽  
Gongbo Zhou ◽  
Zhencai Zhu ◽  
Chaoquan Tang ◽  
Zhenzhi He ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Health Monitoring ◽  
Nearest Neighbor ◽  
Back Propagation ◽  
Feature Space ◽  
Batch Size ◽  
K Nearest Neighbor ◽  
Hoisting System

With the arrival of the big data era, it has become possible to apply deep learning to the health monitoring of mine production. In this paper, a convolutional neural network (CNN)-based method is proposed to monitor the health condition of the balancing tail ropes (BTRs) of the hoisting system, in which the feature of the BTR image is adaptively extracted using a CNN. This method can automatically detect various BTR faults in real-time, including disproportional spacing, twisted rope, broken strand and broken rope faults. Firstly, a CNN structure is proposed, and regularization technology is adopted to prevent overfitting. Then, a method of image dataset description and establishment that can cover the entire feature space of overhanging BTRs is put forward. Finally, the CNN and two traditional data mining algorithms, namely, k-nearest neighbor (KNN) and an artificial neural network with back propagation (ANN-BP), are adopted to train and test the established dataset, and the influence of hyperparameters on the network diagnostic accuracy is investigated experimentally. The experimental results showed that the CNN could effectively avoid complex steps such as manual feature extraction, that the learning rate and batch-size strongly affected the accuracy and training efficiency, and that the fault diagnosis accuracy of CNN was 100%, which was higher than that of KNN and ANN-BP. Therefore, the proposed CNN with high accuracy, real-time functioning and generalization performance is suitable for application in the health monitoring of hoisting system BTRs.

scholarly journals A diagnostic genomic signal processing (GSP)-based system for automatic feature analysis and detection of COVID-19

2020 ◽  
Author(s):  
Safaa M Naeem ◽  
Mai S Mabrouk ◽  
Samir Y Marzouk ◽  
Mohamed A Eldosoky
Keyword(s):  
Nearest Neighbor ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Effective Parameters ◽  
Wuhan City ◽  
Genomic Signal Processing ◽  
Chinese Province ◽  
Genomic Signal

Abstract Coronavirus Disease 2019 (COVID-19) is a sudden viral contagion that appeared at the end of last year in Wuhan city, the Chinese province of Hubei, China. The fast spread of COVID-19 has led to a dangerous threat to worldwide health. Also in the last two decades, several viral epidemics have been listed like the severe acute respiratory syndrome coronavirus (SARS-CoV) in 2002/2003, the influenza H1N1 in 2009 and recently the Middle East respiratory syndrome coronavirus (MERS-CoV) which appeared in Saudi Arabia in 2012. In this research, an automated system is created to differentiate between the COVID-19, SARS-CoV and MERS-CoV epidemics by using their genomic sequences recorded in the NCBI GenBank in order to facilitate the diagnosis process and increase the accuracy of disease detection in less time. The selected database contains 76 genes for each epidemic. Then, some features are extracted like a discrete Fourier transform (DFT), discrete cosine transform (DCT) and the seven moment invariants to two different classifiers. These classifiers are the k-nearest neighbor (KNN) algorithm and the trainable cascade-forward back propagation neural network where they give satisfying results to compare. To evaluate the performance of classifiers, there are some effective parameters calculated. They are accuracy (ACC), F1 score, error rate and Matthews correlation coefficient (MCC) that are 100%, 100%, 0 and 1, respectively, for the KNN algorithm and 98.89%, 98.34%, 0.0111 and 0.9754, respectively, for the cascade-forward network.

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

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.

scholarly journals Identification of Cherry Leaf Disease Infected by Podosphaera Pannosa via Convolutional Neural Network

2019 ◽  
Vol 10 (2) ◽  
pp. 98-110 ◽  
Author(s):  
Keke Zhang ◽  
Lei Zhang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Back Propagation ◽  
Support Vector ◽  
Automatic Identification ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leaf Disease

The cherry leaves infected by Podosphaera pannosa will suffer powdery mildew, which is a serious disease threatening the cherry production industry. In order to identify the diseased cherry leaves in early stage, the authors formulate the cherry leaf disease infected identification as a classification problem and propose a fully automatic identification method based on convolutional neural network (CNN). The GoogLeNet is used as backbone of the CNN. Then, transferred learning techniques are applied to fine-tune the CNN from pre-trained GoogLeNet on ImageNet dataset. This article compares the proposed method against three traditional machine learning methods i.e., support vector machine (SVM), k-nearest neighbor (KNN) and back propagation (BP) neural network. Quantitative evaluations conducted on a data set of 1,200 images collected by smart phones, demonstrates that the CNN achieves best precise performance in identifying diseased cherry leaves, with the testing accuracy of 99.6%. Thus, a CNN can be used effectively in identifying the diseased cherry leaves.

Method of Wind Power Prediction Based on ANN and Improvements

2014 ◽  
Vol 521 ◽  
pp. 143-146 ◽  
Author(s):  
Shao Shuai Song ◽  
Ran Li
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Good Effect ◽  
Power Prediction ◽  
Learning Speed ◽  
Wind Power Prediction ◽  
Artificial Neural Network Ann

This paper puts emphasis on studying on artificial neural network (ANN) method. Following that a model of wind power prediction is established based on back-propagation neural network. In order to improve the learning speed of ANN, a revised BP algorithm is adopted by using variable step and the combination of GA and BP algorithm. This method has a good effect in practice.

scholarly journals Automatic Detection and Staging of Lung Tumors using Locational Features and Double-Staged Classifications

2019 ◽  
Vol 9 (11) ◽  
pp. 2329 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Chuchart Pintavirooj
Keyword(s):  
Lung Cancer ◽  
Nearest Neighbor ◽  
Treatment Options ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Classification Performance ◽  
Clinical Staging ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Experimental Findings

Lung cancer is a life-threatening disease with the highest morbidity and mortality rates of any cancer worldwide. Clinical staging of lung cancer can significantly reduce the mortality rate, because effective treatment options strongly depend on the specific stage of cancer. Unfortunately, manual staging remains a challenge due to the intensive effort required. This paper presents a computer-aided diagnosis (CAD) method for detecting and staging lung cancer from computed tomography (CT) images. This CAD works in three fundamental phases: segmentation, detection, and staging. In the first phase, lung anatomical structures from the input tomography scans are segmented using gray-level thresholding. In the second, the tumor nodules inside the lungs are detected using some extracted features from the segmented tumor candidates. In the last phase, the clinical stages of the detected tumors are defined by extracting locational features. For accurate and robust predictions, our CAD applies a double-staged classification: the first is for the detection of tumors and the second is for staging. In both classification stages, five alternative classifiers, namely the Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Ensemble Tree (ET), and Back Propagation Neural Network (BPNN), are applied and compared to ensure high classification performance. The average accuracy levels of 92.8% for detection and 90.6% for staging are achieved using BPNN. Experimental findings reveal that the proposed CAD method provides preferable results compared to previous methods; thus, it is applicable as a clinical diagnostic tool for lung cancer.

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