scholarly journals Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis

2019 ◽  
Vol 9 (7) ◽  
pp. 1462 ◽  
Author(s):  
Wan-Ju Lin ◽  
Shih-Hsuan Lo ◽  
Hong-Tsu Young ◽  
Che-Lun Hung
Keyword(s):  
Neural Networks ◽  
Surface Roughness ◽  
Fourier Transform ◽  
Deep Learning ◽  
Fast Fourier Transform ◽  
Short Term Memory ◽  
Intelligent System ◽  
Milling Process ◽  
Vibration Signals ◽  
One Dimensional

The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

scholarly journals Learners Demographics Classification on MOOCs During the COVID-19: Author Profiling via Deep Learning Based on Semantic and Syntactic Representations

2021 ◽  
Vol 6 ◽  
Author(s):  
Tahani Aljohani ◽  
Alexandra I. Cristea
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Short Term Memory ◽  
Methodological Approach ◽  
High Accuracy ◽  
Directional Model ◽  
Textual Representations ◽  
The One

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. In this paper, we seek to improve Learner Profiling (LP), i.e. estimating the demographic characteristics of learners in MOOC platforms. We have focused on examining models which show promise elsewhere, but were never examined in the LP area (deep learning models) based on effective textual representations. As LP characteristics, we predict here the employment status of learners. We compare sequential and parallel ensemble deep learning architectures based on Convolutional Neural Networks and Recurrent Neural Networks, obtaining an average high accuracy of 96.3% for our best method. Next, we predict the gender of learners based on syntactic knowledge from the text. We compare different tree-structured Long-Short-Term Memory models (as state-of-the-art candidates) and provide our novel version of a Bi-directional composition function for existing architectures. In addition, we evaluate 18 different combinations of word-level encoding and sentence-level encoding functions. Based on these results, we show that our Bi-directional model outperforms all other models and the highest accuracy result among our models is the one based on the combination of FeedForward Neural Network and the Stack-augmented Parser-Interpreter Neural Network (82.60% prediction accuracy). We argue that our prediction models recommended for both demographics characteristics examined in this study can achieve high accuracy. This is additionally also the first time a sound methodological approach toward improving accuracy for learner demographics classification on MOOCs was proposed.

scholarly journals Stock Pattern Classification from Charts using Deep Learning Algorithms

2020 ◽  
Vol 3 (1) ◽  
pp. 445-454
Author(s):  
Celal Buğra Kaya ◽  
Alperen Yılmaz ◽  
Gizem Nur Uzun ◽  
Zeynep Hilal Kilimci
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Pattern Classification ◽  
Short Term Memory ◽  
Stock Exchange ◽  
Learning Techniques ◽  
Proposed Model ◽  
Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

scholarly journals Automatic Petrol and Diesel Engine Sound Identification Based on Machine Learning Approaches

2019 ◽  
Vol 130 ◽  
pp. 01011
Author(s):  
Halim Frederick ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Machine Learning ◽  
Fourier Transform ◽  
Diesel Engine ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Mechanical Energy ◽  
Petrol Engine ◽  
Support Vector ◽  
Learning Approaches ◽  
Feature Extraction Method

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs).

One Dimensional Fourier Transform on Deep Learning for Industrial Welding Quality Control

2019 ◽  
pp. 174-185 ◽  
Author(s):  
Ander Muniategui ◽  
Jon Ander del Barrio ◽  
Xabier Angulo Vinuesa ◽  
Manuel Masenlle ◽  
Aitor García de la Yedra ◽  
...  
Keyword(s):  
Quality Control ◽  
Fourier Transform ◽  
Deep Learning ◽  
Welding Quality ◽  
One Dimensional

scholarly journals Detection of Gearbox lubrication Using PSO-Based WKNN

2013 ◽  
Vol 13 (3) ◽  
pp. 108-114 ◽  
Author(s):  
Chun-Yao Lee ◽  
Cheng-Chien Kuo ◽  
Ryan Liu ◽  
I-Hsiang Tseng ◽  
Lu-Chen Chang
Keyword(s):  
Fourier Transform ◽  
Particle Swarm Optimization ◽  
Fast Fourier Transform ◽  
Frequency Spectrum ◽  
Relative Frequency ◽  
Classification Model ◽  
Detection Accuracy ◽  
Swarm Optimization ◽  
Vibration Signals ◽  
Noise Interference

This paper proposes an optimization classification model, which combines particle swarm optimization (PSO) with weighted knearest neighbors (WKNN), namely PWKNN. The model optimizes the weight and k parameter of WKNN to improve the detection accuracy of gearbox lubrication levels. In the experiment, the current signals of the generator are measured, and the relative frequency spectrum of the measured signals is illustrated by using fast Fourier transform (FFT). The features from the spectrum are extracted, and then the optimal weight and k parameter of WKNN are obtained by using PSO. The average detection accuracy of gearbox lubrication levels is 96% by using PWKNN, which the result shows that the proposed PWKNN can efficiently detect the lubrication level of gearboxes. The experiment also shows that the performance of the proposed PWKNN by using the current signals of the generator is superior to that by using typical vibration signals of a gearbox. In addition, the accuracy can reach 95.4% even in environments with 20 dB noise interference.

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