scholarly journals Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach

2021 ◽  
Vol 11 (15) ◽  
pp. 6959
Author(s):  
Zaky Dzulfikri ◽  
Pin-Wei Su ◽  
Chih-Yung Huang
Keyword(s):  
Neural Network ◽  
Metric Learning ◽  
Training Data ◽  
Probability Method ◽  
Learning Approach ◽  
Test Results ◽  
Complex Signals ◽  
Intelligent Monitoring ◽  
Deep Metric Learning

Stamping processes remain crucial in manufacturing processes; therefore, diagnosing the condition of stamping tools is critical. One of the challenges in diagnosing stamping tool conditions is that traditionally, the tools need to be visually checked, and the production processes thus need to be halted. With the development of Industry 4.0, intelligent monitoring systems have been developed by using accelerometers and algorithms to diagnose the wear classification of stamping tools. Although several deep learning models such as the convolutional neural network (CNN), auto encoder (AE), and recurrent neural network (RNN) models have demonstrated promising results for classifying complex signals including accelerometer signals, the practicality of those methods are restricted due to the flexibility of adding new classes and low accuracy when faced to low numbers of samples per class. In this study, we applied deep metric learning (DML) methods to overcome these problems. DML involves extracting meaningful features using feature extraction modules to map inputs into embedding features. We compared the probability method, the contrastive method, and a triplet network to determine which method was most suitable for our case. The experimental results revealed that, compared with other models, a triplet network can be more effectively trained with limited training data. The triplet network demonstrated the best test results of the compared methods in the noised test data. Finally, when tested using unseen class, the triplet network and the probability method demonstrated similar results.

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Feature Learning Approach for Facial Recognition Using Deep Metric Learning

2020 ◽  
Vol 17 (9) ◽  
pp. 4125-4130
Author(s):  
Gaurav Karkal ◽  
K. Dhanush Reddy ◽  
Kaushik Singh ◽  
Nikith Hosangadi ◽  
Annapurna P. Patil
Keyword(s):  
Neural Network ◽  
Facial Recognition ◽  
Metric Learning ◽  
Feature Learning ◽  
Human Interaction ◽  
Single Image ◽  
Whole Process ◽  
Knn Classifier ◽  
Deep Metric Learning ◽  
Trained Neural Network

Standard deep learning in the context of facial recognition involves inputting a single image and outputting a label for that image. Deep metric learning distinguishes itself by outputting a real valued feature vector instead of a single label. The usage of deep metric learning has revolutionised facial recognition, making it very accurate and reliable. This paper exhibits the accuracy and reliability of the facial recognition model using deep metric learning in the application of an automated attendance system. The paper presents a non-intrusive attendance system which uses the described neural network to recognize faces and record attendance. The system uses the pre-trained neural network to generate embeddings for faces, using a method known as the triple training step, which is described in the paper. These embeddings are generated from a collection of photos per person. After the embeddings are generated, the system is ready to perform facial recognition on sample photos. CNN is used for facial detection in the sample group photos. Once the faces are detected, a KNN classifier is used for recognizing faces. Finally after the faces are recognized, the attendance for each recognized student is marked in the database. Thus, the whole process of attendance was automated without the requirement of human interaction.

scholarly journals Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network

2021 ◽  
Vol 11 (22) ◽  
pp. 10558
Author(s):  
Nguyen Minh Trieu ◽  
Nguyen Truong Thinh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Processing Speed ◽  
High Efficiency ◽  
Processing System ◽  
Training Data ◽  
Labor Costs ◽  
Quality Classification ◽  
Dragon Fruit

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.

scholarly journals Identification of Rice Variety Using Geometric Features and Neural Network

2019 ◽  
Vol 13 (3) ◽  
pp. 301
Author(s):  
Wahyu Srimulyani ◽  
Aina Musdholifah
Keyword(s):  
Neural Network ◽  
Rice Variety ◽  
Physical Characteristics ◽  
Training Data ◽  
Test Results ◽  
Trial And Error ◽  
Rice Varieties ◽  
Segmentation Evaluation ◽  
The Neural Network

 Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.

scholarly journals Deep Rule-Based Approach for the Classification of Wagon Bogie Springs Condition

2021 ◽  
Author(s):  
Carlos Manuel Viriato Neto ◽  
Luca Garcia Honorio ◽  
Eduardo Aguiar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Approach ◽  
Data Set ◽  
Rule Based ◽  
New Model ◽  
Railway Company ◽  
Rule Based Approach ◽  
Classifier Learning

This paper focuses on the new model of classification of wagon bogie springs condition through images acquired by a wayside equipment. As such, we are discussing the application of a deep rule-based (DRB) classifier learning approach to achieve ahigh classification of a bogie, and check if they either have spring problems or not. We use a pre-trained VGG19 deep convolutional neural network to extract the attributes from images to be used as input to the classifiers. The performance is calculated based on the data set composed of images provided by a Brazilian railway company. The presented results of the report demonstrate the relative performance of applying the DRB classifier to the questions raised.

scholarly journals Sistem Pengelompokan Siswa Berdasarkan Tingkat Kedisiplinan Menggunakan Metode Naïve Bayes Classifier

2021 ◽  
Vol 9 (2) ◽  
pp. 50
Author(s):  
Budi Hartanto ◽  
Sri Tomo
Keyword(s):  
Test Data ◽  
Educational Process ◽  
Training Data ◽  
Student Discipline ◽  
Test Results ◽  
Bayes Classifier ◽  
Bayes Method ◽  
Important Thing ◽  
Class Test

Discipline is a very important thing in the educational process. Discipline will succeed if it is applied to students correctly. Student discipline is that every student follows every rule and order that has been set by the school. At SMK Muhammadiyah 2 Sukoharjo student discipline. Declining discipline at SMK Muhammadiah 2 Sukoharjo is marked by the increase in points of violation from students. The purpose of this study was to apply the nave Bayes method in the classification of student discipline levels at SMK Muhammadiyah 2 Sukoharjo. With this information will be obtained that can be used for information on which students need to be given Counseling Guidance to provide direction and guidance to students. The attributes used are cases of fights, not attending apples, not carrying out picket, not entering without explanation, arriving late, noisy in class. Test results with 490 records with a portion of 75% training data and 25% test data. And produces an accuracy of 76%.

scholarly journals Semi-Supervised Deep Metric Learning Networks for Classification of Polarimetric SAR Data

2020 ◽  
Vol 12 (10) ◽  
pp. 1593
Author(s):  
Hongying Liu ◽  
Ruyi Luo ◽  
Fanhua Shang ◽  
Xuechun Meng ◽  
Shuiping Gou ◽  
...  
Keyword(s):  
Real World ◽  
Metric Learning ◽  
Feature Learning ◽  
Linear Mapping ◽  
Layer By Layer ◽  
Learning Networks ◽  
Distance Metric ◽  
Linear Projection ◽  
Deep Metric Learning

Recently, classification methods based on deep learning have attained sound results for the classification of Polarimetric synthetic aperture radar (PolSAR) data. However, they generally require a great deal of labeled data to train their models, which limits their potential real-world applications. This paper proposes a novel semi-supervised deep metric learning network (SSDMLN) for feature learning and classification of PolSAR data. Inspired by distance metric learning, we construct a network, which transforms the linear mapping of metric learning into the non-linear projection in the layer-by-layer learning. With the prior knowledge of the sample categories, the network also learns a distance metric under which all pairs of similarly labeled samples are closer and dissimilar samples have larger relative distances. Moreover, we introduce a new manifold regularization to reduce the distance between neighboring samples since they are more likely to be homogeneous. The categorizing is achieved by using a simple classifier. Several experiments on both synthetic and real-world PolSAR data from different sensors are conducted and they demonstrate the effectiveness of SSDMLN with limited labeled samples, and SSDMLN is superior to state-of-the-art methods.

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