• 한국진공학회:학술대회논문집
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• 한국진공학회 2015년도 제49회 하계 정기학술대회 초록집
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• pp.132-132
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• 2015

Virtual metrology (VM) model based on plasma information (PI) parameter for C4F8 plasma-assisted oxide etching processes is developed to predict and monitor the process results such as an etching rate with improved performance. To apply fault detection and classification (FDC) or advanced process control (APC) models on to the real mass production lines efficiently, high performance VM model is certainly required and principal component regression (PCR) is preferred technique for VM modeling despite this method requires many number of data set to obtain statistically guaranteed accuracy. In this study, as an effective method to include the 'good information' representing parameter into the VM model, PI parameters are introduced and applied for the etch rate prediction. By the adoption of PI parameters of b-, q-factors and surface passivation parameters as PCs into the PCR based VM model, information about the reactions in the plasma volume, surface, and sheath regions can be efficiently included into the VM model; thus, the performance of VM is secured even for insufficient data set provided cases. For mass production data of 350 wafers, developed PI based VM (PI-VM) model was satisfied required prediction accuracy of industry in C4F8 plasma-assisted oxide etching process.

• 한국항공우주학회지
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• 제43권5호
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• pp.449-455
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• 2015

Built-In-Test(이하: BIT)는 항공기 비행안전을 위해 반드시 필요한 기능으로 항공전자 장비의 경우 95% 이상의 높은 고장 진단능력을 요구하고 있다. BIT가 요구도에 명시된 고장 진단능력을 만족시키는지 확인하기 위해 BIT 성능분석이 필요하다. BIT 성능분석을 위해 FMECA (Failure Mode Effect Critical Analysis)에 기술된 고장모드를 활용하는 방법이 많이 사용되고 있으나, 본 논문에서는 분석 오류를 최소화할 수 있는 전자부품 기반의 BIT 성능분석 방법론을 소개한다. 또한, BIT 성능분석에서 제외될 수 있는 비행안전에 영향을 미치지 않는 전자부품 및 전자부품의 고장모드를 실제 개발사례에 적용하여 불필요한 BIT 기능 구현을 방지하고 정확한 BIT 성능분석을 수행할 수 있도록 하였다. BIT Demo를 수행하여 BIT 성능분석 결과와 실제 BIT 성능이 일치함을 확인하였다.

• 한국컴퓨터산업학회논문지
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• 제3권12호
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• pp.1651-1658
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• 2002

최근 들어 우리나라에서는 설치의 간편성과 높은 신뢰도를 가진 전력케이블의 사용이 증가하고 있다. 전력케이블은 출고 전에 IEEE std. 404-1993 시험을 거쳐 안정성을 확인하고 있지만 포설시 발생하는 접속부 내부의 결함으로 인하여 많은 문제가 발생하고 있다. 특히 불순물 혼입 또는 공극 발생시 고장율은 증가하게 된다. 부분방전 검출은 포설 후 전력케이블의 상태를 관측할 수 있는 유용한 방법이다. 본 연구에서는 부분방전 특성을 평가하고자 케이블 접속재인 EPR과 케이블 절연체인 XPLE 사이에 인공 결함을 발생시킨 후 데이터 취득 시스템을 이용하여 ${\varphi}$-q-n 특성을 검출하였으며, 부분방전의 정량적 해석을 위해 필요한 통계량인 최대방전전하량($q_{max}$), 평균방전 발생 펄스 수(${\={n}$), 평균 방전전하량(${\={q}$), ${\={n}$의 언밸런스 비와${\={q}$의 언밸런스 비를 계산하였다.

• 설비공학논문집
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• 제23권1호
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• pp.32-37
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• 2011

The present study has been conducted to propose an algorithm regarding real-time load estimation of a gas engine-driven heat pump. In the study, thermal load of an indoor unit is estimated in terms of air-side and refrigerant-side. The air-side estimation is based on a typical heat exchanger model and is found to be in good agreement with experimental data. When it comes to the refrigerant-side load, a pressure difference across a valve must be estimated. For the estimation, it is assumed to be proportional to a bigger pressure difference that is available either by measurement or by estimation. Relative good agreement between the air- and refrigerant-sides suggests that the assumption may be plausible for the load estimation. The summed flow rate of all of indoor units is in good agreement with the throughput of the compressor which are calculated from the manufacturer's software. Accordingly, estimated thermal loads are also in good agreement. The proposed algorithm may be further developed for improved control algorithm and fault diagnosis.

• 한국물환경학회지
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• 제24권1호
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• pp.91-98
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• 2008

This work was performed to develop a model possible to predict the influent flow and influent components, which are one of main disturbances causing process problems at the operation of municipal wastewater treatment plant. In this study, artificial neural network (ANN) was used in order to develop a model that was able to predict the influent flow, $COD_{Mn}$, SS, TN 1 day-ahead, 2day-ahead and 3 day ahead. Multi-layer feed-forward back-propagation network was chosen as neural network type, and tanh-sigmoid function was used as activation function to transport signal at the neural network. And Levenberg-Marquart (LM) algorithm was used as learning algorithm to train neural network. Among 420 data sets except missing data, which were collected between 2005 and 2006 at field plant, 210 data sets were used for training, and other 210 data sets were used for validation. As result of it, ANN model for predicting the influent flow and components 1-3day ahead could be developed successfully. It is expected that this developed model can be practically used as follows: Detecting the fault related to effluent concentration that can be happened in the future by combining with other models to predict process performance in advance, and minimization of the process fault through the establishment of various control strategies based on the detection result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.396-413
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• 2017

Dynamically checking the integrity of software at run-time is always a hot and difficult spot for trusted computing. Control-flow integrity is a basic and important safety property of software integrity. Many classic and emerging security attacks who introduce illegal control-flow to applications can cause unpredictable behaviors of computer-based systems. In this paper, we present a software-based approach to checking violation of control flow integrity at run-time. This paper proposes a high-performance and low-overhead software control flow checking solution, control flow checking at virtual edges (CFCVE). CFCVE assigns a unique signature to each basic block and then inserts a virtual vertex into each edge at compile time. This together with insertion of signature updating instructions and checking instructions into corresponding vertexes and virtual vertexes. Control flow faults can be detected by comparing the run-time signature with the saved one at compile time. Our experimental results show that CFCVE incurs only 10.61% performance overhead on average for several C benchmark programs and the average undetected error rate is only 9.29%. Compared with previous techniques, CFCVE has the characteristics of both high fault coverage and low memory and performance overhead.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권9호
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• pp.504-511
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• 2000

The reliability of system is to become a important concern in developed industry. The controller based on the reliability is so important position. PPC(Plant Protection Controller) is for plant protection and human life by fault detection and control action against the transient condition of plant. The protection system of the nuclear reactor and chemical reactor are representative of PPC. This paper presents analysis of PPC relaibility formal problem statement of optimal redundancy based on the reliability for PPC. And the problem is optimized by genetic algorithm, The genetic algorithms is useful algorithm in case of large searching complex gradient existence local minimum. The genetic algorithms is useful algorithm is case of large searching complex gradient existence local minimum. The ability and effectiveness of the proposed optimization is demonstrated by the target reliability of one channel. PPC. using the failure rate based on the MIL-HDBK-217

• 한국기계가공학회지
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• 제20권10호
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• pp.102-111
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• 2021

Maintainability is a major design parameter that includes availability as well as reliability in a RAM (reliability, availability, maintainability) analysis, and is an index that must be considered when developing a system. There is a lack of awareness of the importance of predicting and analyzing maintainability; therefore, it is dependent on past-experience data. To improve the utilization rate, maintainability must be managed as a key indicator to meet the user's requirements for failure maintenance time and to reduce life-cycle costs. To improve the maintainability-prediction accuracy in the detailed design stage, we present a maintainability-prediction method that applies Method B of the Military Standardization Handbook (MIL-HDBK-472) Procedure V, as well as a Korean maintainability-prediction software package that reflects the system complexity.

• 대한안전경영과학회지
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• 제23권3호
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• pp.27-33
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• 2021

The purpose of this study is to present a novel indicator for analyzing machine failure based on its idle time and productivity. Existing machine repair plan was limited to machine experts from its manufacturing industries. This study evaluates the repair status of machines and extracts machines that need improvement. In this study, F-RPN was calculated using the etching process data provided by the 2018 PHM Data Challenge. Each S(S: Severity), O(O: Occurence), D(D: Detection) is divided into the idle time of the machine, the number of fault data, and the failure rate, respectively. The repair status of machine is quantified through the F-RPN calculated by multiplying S, O, and D. This study conducts a case study of machine in a semiconductor etching process. The process capability index has the disadvantage of not being able to divide the values outside the range. The performance of this index declines when the manufacturing process is under control, hereby introducing F-RPN to evaluate machine status that are difficult to distinguish by process capability index.

• 한국융합학회논문지
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• 제13권3호
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• pp.23-31
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• 2022

본 연구는 H해운사에서 제공받은 Starcool사의 실제 냉동 컨테이너 운영데이터를 분석하였다. H사의 현장 전문가와 인터뷰를 통해 4가지 고장 알람 중 Critical 및 Fatal Alarm만 고장으로 정의하였고, 냉동 컨테이너 특성상 모든 변수를 사용하는 것은 비용측면에서 비효율을 초래하는 것을 확인하였다. 이에 본 연구는 특성 중요도 및 PCA 기법을 통한 냉동 컨테이너 고장 탐지 방법을 제시한다. 모델의 성능 향상을 위해 XGBoost, LGBoost 등과 같은 트리계열 모델을 통해 변수 중요도(Feature Importance)를 기반으로 변수 선택(Feature selcetion)을 하고 선택되지 않은 변수는 PCA를 사용하여 전체 변수의 차원을 축소시켜 각 모델별로 지도학습을 수행한다. 부스팅 기반의 XGBoost, LGBoost 기법은 본 연구에서 제안하는 모델의 결과가 62개의 모든 변수를 사용한 지도 학습의 결과보다 재현율(Recall)이 각각 0.36, 0.39씩 향상되는 되는 결과를 보였다.