Jongpil Jeong 교수

In machine learning, data augmentation is the process of generating synthetic data samples that will be used to train the model to improve the performance of the machine learning model. Data augmentation has been shown to improve the generalization capabilities of models and is particularly popular in the field of computer vision, which is to deal with image data. In contrast, data augmentation is less widely used in the field of time-series data, such as time-series classification, than in comp

In this paper, we propose a new user authentication (UA) scheme based on one-time password (OTP) protocol using smart cards for home networks. The proposed scheme is to authenticate home users who uses home devices. Several techniques using technology based on biometrics, passwords, certificates, and smart cards can be used for user authentication in the similar environments. However, such user authentication techniques must be examined before being employed in an environment where home devices

Global warming caused by air pollution and ecosystem changes are very important. Pollutants such as harmful gases and fine dust generated in industrial sites are the main causes of air pollution. Strict control of emissions of air pollutants from industrial sites is required. In this paper, we propose a real-time air pollution index measurement platform using 5G wireless network and blockchain. The proposed platform breaks the existing passive method and collects data in real time through IoT se

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep lea

In the manufacturing process, equipment failure is directly related to productivity, so predictive maintenance plays a very important role. Industrial parks are distributed, and data heterogeneity exists among heterogeneous equipment, which makes predictive maintenance of equipment challenging. In this paper, we propose two main techniques to enable effective predictive maintenance in this environment. We propose a 1DCNN-Bilstm model for time series anomaly detection and predictive maintenance o

This paper reports on the use of reinforcement learning technology for optimizing mobile robot paths in a warehouse environment with automated logistics. First, we compared the results of experiments conducted using two basic algorithms to identify the fundamentals required for planning the path of a mobile robot and utilizing reinforcement learning techniques for path optimization. The algorithms were tested using a path optimization simulation of a mobile robot in same experimental environment

Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem

The problem of energy depletion has brought wind energy under consideration to replace oil- or chemical-based energy. However, the breakdown of wind turbines is a major concern. Accordingly, unsupervised learning was performed using the vibration signal of a wind power generator to achieve an outlier detection performance of 97%. We analyzed the vibration data through wavelet packet conversion and identified a specific frequency band that showed a large difference between the normal and abnormal

Rolling bearings are undoubtedly one of the key components in rotating machines. Bearing failure can adversely affect not only on mechanical failures, but also on operation schedules, and production processes. Therefore, the fault diagnosis of the bearing is a very important field. Recently, many machine learning and deep learning researches have been conducted and deep learning approaches usually have shown better results. Although, deep learning approaches have shown good results, most of open

In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault diagnosis. However, at manufacturing sites, it is impossible to have enough training samples to represent all fault types under all operating conditions. In addition, most studies consider only accuracy, and models are complex and computationall

It is important to maintain the temperature uniformly in the calcining process at the manufacturing site. However, it is not easy to maintain a constant temperature. To predict the temperature, it is necessary to predict the life of the heating element in the calcining process. The breakdown time of the process should be reduced by replacing the heating element at an appropriate time before the life of the heating element is reached. In this paper, to predict the temperature of the firing furnac

With the advent of the 4th Industrial Revolution, research on anomaly detection in the manufacturing process using deep learning and machine vision is being actively conducted. There have been various attempts to innovate the manufacturing site by adopting advance information technologies such as machine vision, machine learning, and deep learning in many manufacturing processes. However, there have been no cases of designing and implementing these technologies at the mask manufacturing site, wh

Wearing a hard hat can effectively improve the safety of workers on a construction site. However, workers often take off their helmets because they have a weak sense of safety and are uncomfortable, and this action poses a large danger. Workers not wearing hard hats are more likely to be injured in accidents such as human falls and vertical falls. Therefore, the detection of wearing a helmet is an important step in the safety management of a construction site, and it is urgent to detect helmets

Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using th

By monitoring a hydraulic system using artificial intelligence, we can detect anomalous data in a manufacturing workshop. In addition, by analyzing the anomalous data, we can diagnose faults and prevent failures. However, artificial intelligence, especially deep learning, needs to learn much data, and it is often difficult to get enough data at the real manufacturing site. In this paper, we apply augmentation to increase the amount of data. In addition, we propose real-time monitoring based on a