• 한국기계가공학회지
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• 제20권5호
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• pp.1-7
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• 2021

In recent years, automated process technology has allowed for the rapid manufacturing of metal parts. Maintaining high product quality is of vital importance during the production of these parts. Surface defects occurring after processing can compromise their assembly precision and performance. In this study, a deburring tool was developed that can remove burrs generated from drilling. Through the evaluation of processing, burrs were completely removed at entrance and exit surfaces. Therefore, this newly developed deburring tool shows better performance than deburring tools currently in use.

• 반도체디스플레이기술학회지
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• 제17권1호
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• pp.50-53
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• 2018

Cyclic measurement system using relay circuit for organic light emitting diode (OLED) was demonstrated. The OLED characterization such as current-voltage, impedance, and capacitance-voltage is performed in sequence, repetitively and automatically under full control of the personnel computer (PC) without changing the connection of cables. Owing to in situ degradation by cyclic measurement, the time dependence of the data can give good information on the reliability factor of the OLED devices. Therefore, both performance and reliability of the OLEDs can be evaluated, with no manual operation during the entire process.

• 한국기계가공학회지
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• 제18권6호
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• pp.19-25
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• 2019

We have developed a tool that uses finite element analysis (FEA) to rapidly evaluate a shaft-bearing system of machine tools. We extracted commercial data on suitable clamping units and defined the inner profile of the shaft to avoid needing direct user input to define the profile. We use a splitting algorithm to convert the shaft into beam elements with two diameters and length. To validate the tool, we used it to design and evaluate a shaft-bearing system and found that our tool automated the construction of an FE system model in a commercial FEA package as well as the static stiffness evaluation; both tasks were completed in seconds, demonstrating a significant reduction from the minutes normally required to complete these tasks manually.

• 반도체디스플레이기술학회지
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• 제19권4호
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• pp.126-129
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• 2020

We introduce a lifetime assessment technique using deep learning algorithm with complex electrical parameters such as resistivity, permittivity, impedance parameters as integrated indicators for predicting the degradation of the organic molecules. The evaluation system consists of fully automated in-situ measurement system and multiple layer perceptron learning system with five hidden layers and 1011 perceptra in each layer. Prediction accuracies are calculated and compared depending on the physical feature, learning hyperparameters. 62.5% of full time-series data are used for training and its prediction accuracy is estimated as r-square value of 0.99. Remaining 37.5% of the data are used for testing with prediction accuracy of 0.95. With k-fold cross-validation, the stability to the instantaneous changes in the measured data is also improved.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.92-97
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• 2022

Plastics are classified into 7 types such as PET (PETE), HDPE, PVC, LDPE, PP, PS, and Other for separation and recycling. Recently, large corporations advocating ESG management are replacing them with bioplastics. Incineration and landfill of disposal of plastic waste are responsible for air pollution and destruction of the ecosystem. Because it is not easy to accurately classify plastic materials with the naked eye, automated system-based screening studies using various sensor technologies and AI-based software technologies have been conducted. In this paper, NIR scanning devices considering the NIR wavelength characteristics that appear differently for each plastic material and a system that can identify the type of plastic by learning the NIR spectrum data collected through it. The accuracy of plastic material identification was evaluated through a decision tree-based SVM model for multiclass classification on NIR spectral datasets for 8 types of plastic samples including biodegradable plastic.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2022년도 제34회 한글 및 한국어 정보처리 학술대회
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• pp.526-530
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• 2022

자동 쓰기 평가 연구는 쓰기 답안지를 채점하는데 드는 시간과 비용을 절감할 수 있어, 교육 분야에서 큰 관심을 가지고 있다. 본 연구의 목적은 쓰기 답안지의 문서 구조를 효과적으로 학습하여 평가하고, 문장단위의 피드백을 제공하는데 있다. 그 방법으로는 문장 레벨에서 한국어 Sentence-BERT 모델을 활용하여 각 문장을 임베딩하고, LSTM 어텐션 모델을 활용하여 문서 레벨에서 임베딩 문장을 모델링한다. '한국어 쓰기 텍스트-점수 구간 데이터'를 활용하여 해당 모델의 성능 평가를 진행하였으며, 다양한 KoBERT 기반 모델과 비교 평가를 통해 제안하는 모델의 방법론이 효과적임을 입증하였다.

• 한국정밀공학회지
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• 제12권5호
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• pp.25-32
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• 1995

In highly automated milling process, in-process monitoring of the malfunction is indispensable to ensure efficient cutting operation. Among many malfunctions in milling process, chatter vibration deteriorates surface finish, tool life and productivity. In this study, the monitoring system of chatter vibration for face milling process is proposed and experimentally estimated. The monitoring system employs two types of sensor such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are extracted in time domain for the input patterns of neural network to reduce time delay in signal processing state. The resultes of experimental evaluation show that the system works well over a wide range of cutting conditions.

• 한국정밀공학회지
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• 제14권10호
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• pp.58-67
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• 1997

Generally, die polishing process occupies about 30 .approx. 50% of the whole die manufacturing time. However, die polshing has not been automated yet, since it needs a great deal of experience and skill. This study aims at development of an expert system for die polishing which gives such optimal parameters as tool and polishing conditions. Through experiments, polishing characteristics such as surface roughness, stock removal and scratch were analyzed quantitatively for each polishing tool, and a knowledge base for the expert system was established. Evaluation tests show that the developed system works well to suggest the optimal polishing conditions and it is very promising.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2005년도 춘계학술대회 논문집
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• pp.359-367
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• 2005

컨테이너 물동량의 증가에 의한 생산성 향상이 필요아 인건비 등의 비용 절감의 이점으로 인해 국내외적으로 컨테이너 터미널의 자동화가 추진되고 있다. 이에 따라 기존의 수동 야드 크레인과는 다른 자동화 야드 크레인인 RMG의 특성을 고려한 새로운 장치 전략이 필요하다. 본 논문에서는 교차 불가능한 2대의 RMG를 사용하는 수평배치블록을 대상으로 작업 생산성을 평가할 수 있는 시뮬레이션 모델을 개발하고 두 가지 컨테이너 장치 전략을 실험하였다. 첫 번째 장치 전략은 기존 장치장 공간 계획과 유사하게 하나의 선박에 대한 본선 작업 컨테이너들을 가능한 모으고, 본선 작업과 반입출 작업을 각각의 크레인에 전담시키는 집중화 전략이다. 두 번째 장치 전략은 두 크레인이 한 선박에 대한 본선 작업을 번갈아 수행함으로써 본선 작업의 효율을 높일 수 있도록 블록 공간을 둘로 나누고 각 구역별로 담당할 크레인을 할당하는 분산화 전략이다. 시뮬레이션 실험 결과 집중화 전략은 양하와 반출이 동시에 발생하는 수입용 블록에 유리하였으며, 분산화 전략은 적하와 반입이 동시에 발생하는 수출용 블록에 효과적인 것으로 확인되었다.