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- [일반] 제15회 전자신문 대학(원)생 ICT논문 공모대제전
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- 작성일 2023-08-10
- 조회수 2027
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- [연구] 석사과정 백승효, SCIE 논문지(MDPI Processes/Q2) 게재
- 석사과정 백승효 학생(지도교수 : 정종필)의 연구(YOLOv7-Based Anomaly Detection Using Intensity and NG Types in Labeling in Cosmetic Manufacturing Processes)가 MDPI Processes(Impact Factor: 3.5 (2022); 5-Year Impact Factor: 3.4 (2022))에 게재됐다. https://doi.org/10.3390/pr11082266 / https://www.mdpi.com/2227-9717/11/8/2266 논문요약 - The advent of the Fourth Industrial Revolution has revolutionized the manufacturing sector by integrating artificial intelligence into vision inspection systems to improve the efficiency and quality of products. Supervised-learning-based vision inspection systems have emerged as a powerful tool for automated quality control in various industries. During visual inspection or final inspection, a human operator physically inspects a product to determine its condition and categorize it based on their know-how. However, the know-how-based visual inspection process is limited in time and space and is affected by many factors. High accuracy in vision inspection is highly dependent on the quality and precision of the labeling process. Therefore, supervised learning methods of 1-STAGE DETECTION, such as You Only Look Once (YOLO), are utilized in automated inspection to improve accuracy. In this paper, we proposed a labeling method that achieves the highest inspection accuracy among labeling methods such as NG intensity and NG intensity when performing anomaly detection using YOLOv7 in the cosmetics manufacturing process.
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- 작성일 2023-07-27
- 조회수 3123
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- [연구] 석사과정 유요셉, SCIE 논문지(MDPI Processes/Q2) 게재
- 석사과정 유요셉 학생(지도교수 : 정종필)의 연구(Estimating APC Model Parameters for Dynamic Intervals Determined Using Change-Point Detection in Continuous Processes in the Petrochemical Industry)가 MDPI Processes(Impact Factor: 3.5 (2022); 5-Year Impact Factor: 3.4 (2022))에 게재됐다. https://doi.org/10.3390/pr11082229 / https://www.mdpi.com/2227-9717/11/8/2229 논문요약 - Several papers have proven that advanced process controller (APC) systems can save more energy in the process than proportional-integral-differential (PID) controller systems. Therefore, implementing an APC system is ultimately beneficial for saving energy in the plant. In a typical APC system deployment, the APC model parameters are calculated from dynamic data intervals obtained through the plant test. However, depending on the proficiency of the APC engineer, the results of the plant test and the APC model parameters are implemented differently. To minimize the influence of the APC engineer and calculate universal APC model parameters, a technique is needed to obtain dynamic data without a plant test. In this study, we utilize time-series data from a real petrochemical plant to determine dynamic intervals and estimate APC model parameters, which have not been investigated in previous studies. This involves extracting the data of the dynamic intervals with the smallest mean absolute error (MAE) by utilizing statistical techniques such as pruned exact linear time, linear kernel, and radial basis function kernel of change-point detection (CPD). After that, we fix the hyper parameters at the minimum MAE value and estimate the APC model parameters by training with the data from the dynamic intervals. The estimated APC model parameters are applied to the APC program to compare the APC model fitting rate and verify the accuracy of the APC model parameters in the dynamic intervals obtained through CPD. The final validation of the model fitting rates demonstrates that the identification of the dynamic intervals and the estimation of the APC model parameters through CPD show high accuracy. We show that it is possible to estimate APC model parameters from dynamic intervals determined by CPD without a plant test.
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- 작성일 2023-07-25
- 조회수 2747
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- [연구] 석사과정 이대환, SCIE 논문지(MDPI Sensors/Q1) 게재
- 석사과정 이대환 학생(지도교수 : 정종필)의 연구(Few-Shot Learning-Based Light-Weight WDCNN Model for Bearing Fault Diagnosis in Siamese Network)가 MDPI Sensors(Impact Factor: 3.9 (2022); 5-Year Impact Factor: 4.1 (2022))에 게재됐다. DOI: https://doi.org/10.3390/s23146587 or https://www.mdpi.com/1424-8220/23/14/6587 논문요약 - 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 computationally expensive. Research that only considers accuracy is inefficient since manufacturing sites change rapidly. Therefore, in this study, we propose a few-shot learning model that can effectively learn with small data. In addition, a Depthwise Separable Convolution layer that can effectively reduce parameters is used together. In order to find an efficient model, the optimal hyperparameters were found by adjusting the number of blocks and hyperparameters, and by using a Depthwise Separable Convolution layer for the optimal hyperparameters, it showed higher accuracy and fewer parameters than the existing model.
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- 작성일 2023-07-22
- 조회수 3424
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- [연구] 석사과정 이민엽, SCIE 논문지(MDPI Processes/Q2) 게재
- 석사과정 이민엽 학생(지도교수 : 정종필)의 연구(Machine Learning-Based Prediction of Controlled Variables of APC Systems Using Time-Series Data in the Petrochemical Industry)가 MDPI Processes(Impact Factor: 3.5 (2022); 5-Year Impact Factor: 3.4 (2022))에 게재됐다. https://doi.org/10.3390/pr11072091 / https://www.mdpi.com/2227-9717/11/7/2091 논문요약 - For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term forecasting with small data, where many systems are being applied to improve production efficiency. The problem considered in this study is the problem of predicting time-series Controlled Variables (CV) with machine learning, which is necessary to utilize an Advanced Process Control (APC) system in a petrochemical plant. In an APC system, the most important aspect is the prediction of the controlled variables and how the predicted values of the controlled variables should be modified to be in the user’s desired range. In this study, we focused on predicting the controlled variables. Specifically, we utilized various machine learning techniques to predict future controlled variables based on past controlled variables, Manipulated Variables (MV), and Disturbance Variables (DV). By using a time delay as a parameter and adjusting its value, you can analyze the relationship between past and future data and improve forecasting performance. Currently, the APC system is controlled through mathematical modeling and research, The time-series data of controlled variables, manipulated variables, and disturbance variables are predicted through machine learning models to compare performance and measure accuracy. It is becoming important to change from mathematical prediction models to data-based machine learning predictions. The R-Squared (R2) and Mean Absolute Percentage Error (MAPE) metric results of this study demonstrate the feasibility of introducing an APC system using machine learning models in petrochemical plants.
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- 작성일 2023-07-13
- 조회수 2941
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- [연구] 석사과정 송하섭, SCIE 논문지(MDPI applied sciences/Q2) 게재
- 석사과정 송하섭 학생(지도교수: 정종필)의 연구(Production Planning Forecasting System Based on M5P Algorithms and Master Data in Manufacturing Processes)가 MDPI applied sciences(Impact Factor: 2.7 (2022); 5-Year Impact Factor: 2.9 (2022))에 게재됐다. https://www.mdpi.com/2076-3417/13/13/7829 / https://doi.org/10.3390/app13137829 논문요약 - With the increasing adoption of smart factories in manufacturing sites, a large amount of raw data is being generated from manufacturers’ sensors and Internet of Things devices. In the manufacturing environment, the collection of reliable data has become an important issue. When utilizing the collected data or establishing production plans based on user-defined data, the actual performance may differ from the established plan. This is particularly so when there are modifications in the physical production line, such as manual processes, newly developed processes, or the addition of new equipment. Hence, the reliability of the current data cannot be ensured. The complex characteristics of manufacturers hinder the prediction of future data based on existing data. To minimize this reliability problem, the M5P algorithm, is used to predict dynamic data using baseline information that can be predicted. It combines linear regression and decision-tree-supervised machine learning algorithms. The algorithm recommends the means to reflect the predicted data in the production plan and provides results that can be compared with the existing baseline information. By comparing the existing production plan with the planning results based on the changed master data, it provides data results that help production management determine the impact of work time and quantity and confirm production plans. This means that forecasting data directly affects production capacity and resources, as well as production times and schedules, to help ensure efficient production planning.
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- 작성일 2023-07-04
- 조회수 3543
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- [일반] 2023 SKKU 대학원생 논문대상 공모사업 시행안내
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- 작성일 2023-06-12
- 조회수 3009
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- [학생실적] 성균관대학교 스마트팩토리융합학과 김명수·김지형 졸업생, 중소벤처기업부 장관상 수상
- [한국강사신문 안상현 기자] 성균관대학교(총장 유지범)는 소프트웨어융합대학 스마트팩토리융합학과 김명수, 김지형 졸업생 및 학과 참여기업 ㈜사이버테크프랜드가 2022년도 중소기업 인력양성대학 사업 유공자에 선정되었다고 밝혔다. 중소기업 인력양성대학 사업 유공자는 중소기업 인력양성 대학 사업을 통해 중소기업 인력양성 및 청년 일자리 창출에 기여한 사람을 선정하게 되며 선정 분야는 참여학교 교수(교사포함), 참여기업 대표, 지원인력, 참여학생 등 4가지 부문에서 선정한다. 위 선정 분야 중 참여학생 부분에서 사업에 성실히 참여한 김명수, 김지형 졸업생은 참여학생 부문에서 중소벤처기업부 장관 표창을 수상했다. 김명수 졸업생은 정부 R&D사업 추진/수행 6건(스마트공장 등대공장 총 사업비 24억 등) 및 저널 논문지 2건(SCI 1건, KCI 1건), 특허 출원 및 등록 3건(출원 2건, 등록 1건) 등 기술 개발 및 사업화 업무와 중소기업 발전에 이바지하였으며, 김지형 졸업생은 디지털 트윈 및 XR 원천기술 연구를 통해 국내 제조, 에너지 분야에 새로운 경제적 부가가치를 창출하고 중소기업 경쟁력 향상에 기여한 공로를 인정받아 이번 유공자로 선정되었다. 한편, 참여기업 대표 부문에서는 학과 사업에 5년째 참여하여 산학협력 프로젝트 등을 수행하고 있는 ㈜사이버테크프랜드의 김정혁 대표가 수상했다. 출처 : 한국강사신문(https://www.lecturernews.com)
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- 작성일 2023-03-22
- 조회수 3552