scholarly journals Core looseness fault identification model based on Mel spectrogram-CNN

2021 ◽  
Vol 2137 (1) ◽  
pp. 012060
Author(s):  
Ping He ◽  
Yong Li ◽  
Shoulong Chen ◽  
Hoghua Xu ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Convolution Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Recognition Model ◽  
Model Based ◽  
Fault Recognition ◽  
Transformer Core ◽  
Identification Model

Abstract In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guangpeng Fan ◽  
Feixiang Chen ◽  
Danyu Chen ◽  
Yan Li ◽  
Yanqi Dong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Test Data ◽  
Smartphone Application ◽  
Geological Survey ◽  
Data Set ◽  
Recognition Model ◽  
Communication Device ◽  
Rock Lithology

In the geological survey, the recognition and classification of rock lithology are an important content. The recognition method based on rock thin section leads to long recognition period and high recognition cost, and the recognition accuracy cannot be guaranteed. Moreover, the above method cannot provide an effective solution in the field. As a communication device with multiple sensors, smartphones are carried by most geological survey workers. In this paper, a smartphone application based on the convolutional neural network is developed. In this application, the phone’s camera can be used to take photos of rocks. And the types and lithology of rocks can be quickly and accurately identified in a very short time. This paper proposed a method for quickly and accurately recognizing rock lithology in the field. Based on ShuffleNet, a lightweight convolutional neural network used in deep learning, combined with the transfer learning method, the recognition model of the rock image was established. The trained model was then deployed to the smartphone. A smartphone application for identifying rock lithology was designed and developed to verify its usability and accuracy. The research results showed that the accuracy of the recognition model in this paper was 97.65% on the verification data set of the PC. The accuracy of recognition on the test data set of the smartphone was 95.30%, among which the average recognition time of the single sheet was 786 milliseconds, the maximum value was 1,045 milliseconds, and the minimum value was 452 milliseconds. And the single-image accuracy above 96% accounted for 95% of the test data set. This paper presented a new solution for the rapid and accurate recognition of rock lithology in field geological surveys, which met the needs of geological survey personnel to quickly and accurately identify rock lithology in field operations.

scholarly journals FRMN - A Face Recognition Model Based on Convolutional Neural Network

2019 ◽  
Vol 585 ◽  
pp. 012101
Author(s):  
Shifeng Shang ◽  
Haiyan Liu ◽  
Qiang Qu ◽  
Guannan Li ◽  
Jie Cao
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Model ◽  
Model Based

scholarly journals Skin Cancer Detection using CNN Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 45-49
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Well Being ◽  
Training Data ◽  
Data Sets ◽  
Data Set ◽  
Sequential Model ◽  
Pigmented Lesions ◽  
Testing Data

The project “Disease Prediction Model” focuses on predicting the type of skin cancer. It deals with constructing a Convolutional Neural Network(CNN) sequential model in order to find the type of a skin cancer which takes a huge troll on mankind well-being. Since development of programmed methods increases the accuracy at high scale for identifying the type of skin cancer, we use Convolutional Neural Network, CNN algorithm in order to build our model . For this we make use of a sequential model. The data set that we have considered for this project is collected from NCBI, which is well known as HAM10000 dataset, it consists of massive amounts of information regarding several dermatoscopic images of most trivial pigmented lesions of skin which are collected from different sufferers. Once the dataset is collected, cleaned, it is split into training and testing data sets. We used CNN to build our model and using the training data we trained the model , later using the testing data we tested the model. Once the model is implemented over the testing data, plots are made in order to analyze the relation between the echos and loss function. It is also used to analyse accuracy and echos for both training and testing data.

scholarly journals Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM

2022 ◽  
Vol 11 (1) ◽  
pp. 1-8
Author(s):  
Hao Li ◽  
Junyan Han ◽  
Shangqing Li ◽  
Hanqing Wang ◽  
Hui Xiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Driving Behavior ◽  
Data Sets ◽  
Short Term ◽  
Behavior Recognition ◽  
Term Memory ◽  
Recognition Model ◽  
Driving Behaviors

Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by preprocessing the data. A recognition model based on a convolutional neural network combined with a long short-term memory network was constructed to extract depth features from data sets and recognize abnormal driving behaviors. The test results show that the accuracy of the model based on CNN-LSTM can reach 95.22%, and the performance indexes can reach more than 94%. Compared with the recognition model constructed only by CNN or LSTM, this model has higher recognition accuracy.

scholarly journals Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

2021 ◽  
Vol 13 (04) ◽  
pp. 30-40
Author(s):  
Jiangyong Liu ◽  
Ning Liu ◽  
Huina Song ◽  
Ximeng Liu ◽  
Xingen Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Load Identification ◽  
Spatial Feature ◽  
Model Based ◽  
Identification Model

Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs

2021 ◽  
Author(s):  
Cansu Görürgöz ◽  
Kaan Orhan ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
True Positive Rate ◽  
Turnaround Time ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Region Detection ◽  
Positive Rate

Objectives: The present study aimed to evaluate the performance of a Faster Region-based Convolutional Neural Network (R-CNN) algorithm for tooth detection and numbering on periapical images. Methods: The data sets of 1686 randomly selected periapical radiographs of patients were collected retrospectively. A pre-trained model (GoogLeNet Inception v3 CNN) was employed for pre-processing, and transfer learning techniques were applied for data set training. The algorithm consisted of: (1) the Jaw classification model, (2) Region detection models, and (3) the Final algorithm using all models. Finally, an analysis of the latest model has been integrated alongside the others. The sensitivity, precision, true-positive rate, and false-positive/negative rate were computed to analyze the performance of the algorithm using a confusion matrix. Results: An artificial intelligence algorithm (CranioCatch, Eskisehir-Turkey) was designed based on R-CNN inception architecture to automatically detect and number the teeth on periapical images. Of 864 teeth in 156 periapical radiographs, 668 were correctly numbered in the test data set. The F1 score, precision, and sensitivity were 0.8720, 0.7812, and 0.9867, respectively. Conclusion: The study demonstrated the potential accuracy and efficiency of the CNN algorithm for detecting and numbering teeth. The deep learning-based methods can help clinicians reduce workloads, improve dental records, and reduce turnaround time for urgent cases. This architecture might also contribute to forensic science.

scholarly journals Improvement of Ancient Shui Character Recognition Model Based on Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 33080-33087
Author(s):  
Hongshuai Zhao ◽  
Haozhen Chu ◽  
Yuanyuan Zhang ◽  
Yu Jia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Recognition Model ◽  
Model Based
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