• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.351-353
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• 2006

Preventive maintenance can avail the generating unit to reduce cost and gain more profit in a competitive supply-side power market. so, it is necessary to perform reliability analysis on the systems in which reliability is essential. In this paper, FMECA assessment adopted using real historical failure data in Korean power plants for apply RCM analytical method. The stochastic FMECA is an engineering analysis and a core activity performed by reliability engineers to review the effects of probable failure modes of generating unit and assemblies of the power system on system performance. Optimal RCM schedule which is considered the severity level of each generating unit and failure probability from failure prediction of generating unit can be planned using proposed FMECA with IOE index.

• Smart Structures and Systems
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• v.14 no.4
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• pp.559-567
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• 2014

The model of unit dynamic reliability of repairable k/n (G) system with unit strength degradation under repeated random shocks has been developed according to the stress-strength interference theory. The unit failure number is obtained based on the unit failure probability which can be computed from the unit dynamic reliability. Then, the transfer probability function of the repairable k/n (G) system is given by its Markov property. Once the transfer probability function has been obtained, the probability density matrix and the steady-state probabilities of the system can be retrieved. Finally, the dynamic reliability of the repairable k/n (G) system is obtained by solving the differential equations. It is illustrated that the proposed method is practicable, feasible and gives reasonable prediction which conforms to the engineering practice.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.183-193
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• 2024

Recurrent Neural Network technology that learns past patterns and predicts future patterns using technology for recognizing and classifying objects is being applied to various industries, economies, and languages. And research for practical use is making a lot of progress. However, research on the application of Recurrent Neural Networks for evaluating and predicting the safety of mechanical structures is insufficient. Accurate detection of external load applied to the outside is required to evaluate the safety of mechanical structures. Learning of Recurrent Neural Networks for this requires a large amount of load data. This study applied the Gated Recurrent Unit technique to examine the possibility of load learning and investigated the possibility of applying a stacked Auto Encoder as a way to secure load data. In addition, the usefulness of learning mechanical loads was analyzed with the Gated Recurrent Unit technique, and the basic setting of related functions and parameters was proposed to secure accuracy in the recognition and prediction of loads.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.5
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• pp.165-172
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• 2001

Wheel bearing units were almost exclusively used for car front wheel, where the two ball rows are directly side by side with integrated rubber seal. The seal is of important for wheel bearing units due to the adverse environmental conditions with mud and splash water. The seal of wheel bearing units was designed to have geometry with multi lips, which elastic lip contacts and deforms with bearing. The equation of reaction force for deformed lip as cantilever beam was previously used for seal lip design. But it's result was not useful because deflection of the beam differs from lip's. In this study, deformed shape of the lip was assumed to and order function which is more similar to lip deformation and made the equation for reaction force prediction. The Reaction forces from each other equations were compared with results by FEA to prove usefulness of new equation.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.371-387
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• 1999

For possibly nonstationary autoregressive moving average, modeling based on the original observations rather than the differenced observations is considered. Under this scheme, sample autocorrelation functions, parameter estimates, model diagnostic statistics, and prediction are all computed from the original data instead of the differenced data. The methods and results established under stationarity of data are shown to naturally extend to the nonstationarity of one autoregressive unit root. The sample ACF and PACF can be used for ARMA order determination. The BIC order is strongly consistent. The parameter estimates are asymptotically normal. The portmanteau statistic has chi-square distribution. The predictor is asymptotically equivalent to that based on the differenced data.

• Transactions of Materials Processing
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• v.20 no.4
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• pp.284-290
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• 2011

The front end bending(FEB) behavior of material that usually occurs in plate rolling is investigated. In this paper, a rollbite profile map approach that systematically predicts the FEB slope is presented. It is based on the concurrent use of shape factors and reduction ratios to ensure an accurate value of the FEB and its slope. In order to obtain the unit roll-bite profile map, the FEB slope model was decomposed into a temperature deviation component and a roll-velocity deviation component. By mapping the results of a series of finite element analyses to the unit functions of the roll-bite profile map, it was possible to obtain a realistic prediction of the FEB slope applicable to an actual plate rolling process. Thereby, the usefulness of the present approach is clearly demonstrated.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.160-162
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• 2018

본 논문에서는 압축 영역에서 열 영상을 이용한 딥러닝 기반의 객체 검출 방법을 제안한다. 비디오 압축 표준인 High Efficiency Video Coding(HEVC)를 이용하여 부보화된 비트스트림으로부터 Intra Prediction Mode(IPM), Prediction Unit Size(PUS), Transform Unit Size(TUS)를 추출하고 3 채널 영상으로 변환하고 객체 검출 네트워크인 YOLO 에 입력으로 넣어주어 최종적으로 객체의 위치 및 객체의 종류를 예측한다. 실험결과로써 복원된 열 영상과 검출된 결과를 주관적으로 보여줌으로써 압축영역에서 열영상을 이용한 객체 검출이 가능함을 보인다.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.129-132
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• 2002

AE64000 is the 64-bit high-performance microprocessor that ADC Co. Ltd. is developing for an embedded environment. It has a 5-stage pipeline and uses Havard architecture with a separated instruction and data caches. It also provides SIMD-like DSP and FP operation by enabling the 8/16/32/64-bit MAC operation on 64-bit registers. AE64000 processor implements the EISC ISA and uses the instruction folding mechanism (Instruction Folding Unit) that effectively deals with LERI instruction in EISC ISA. But this unit makes branch prediction behavior difficult. In this paper, we designs a branch predictor optimized for AE64000 Pipeline and develops a AES4000 simulator that has cycle-level precision to validate the performance of the designed branch predictor. We makes TAC(Target address cache) and BPT(branch prediction table) seperated for effective branch prediction and uses the BPT(removed indexed) that has no address tags.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.523-533
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• 2022

Several prediction model of penetration rate (PR) of tunnel boring machines (TBMs) have been focused on applying to design stage. In construction stage, however, the expected PR and its trends are changed during tunneling owing to TBM excavation skills and the gap between the investigated and actual geological conditions. Monitoring the PR during tunneling is crucial to rescheduling the excavation plan in real-time. This study proposes a sequential prediction method applicable in the construction stage. Geological and TBM operating data are collected from Gunpo cable tunnel in Korea, and preprocessed through normalization and augmentation. The results show that the sequential prediction for 1 ring unit prediction distance (UPD) is R²≥0.79; whereas, a one-step prediction is R²≤0.30. In modeling algorithm, a gradient boosted regression tree (GBRT) outperformed a least square-based linear regression in sequential prediction method. For practical use, a simple equation between the R² and UPD is proposed. When UPD increases R² decreases exponentially; In particular, UPD at R²=0.60 is calculated as 28 rings using the equation. Such a time interval will provide enough time for decision-making. Evidently, the UPD can be adjusted depending on other project and the R² value targeted by an operator. Therefore, a calculation process for the equation between the R² and UPD is addressed.

• Journal of Korean Critical Care Nursing
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• v.8 no.1
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• pp.71-79
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• 2015

Purpose: The purpose of this study was to compare the applicability of the Charlson Comorbidity Index (CCI) and Acute Physiology, Age, Chronic Health Evaluation III (APACHE III) to the prediction of the healthcare outcomes of intensive care unit (ICU) patients. Methods: This research was performed with 136 adult patients (age>18 years) who were admitted to the ICU between May and June 2012. Data were measured using the CCI score with a comorbidity index of 19 and the APACHE III score on the standard of the worst result with vital signs and laboratory results. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under an ROC curve (AUC). Calibration was performed using logistic regression. Results: The overall mortality was 25.7%. The mean CCI and APACHE III scores for survivors were found to be significantly lower than those of non-survivors. The AUC was 0.835 for the APACHE III score and remained high, at 0.688, for the CCI score. The rate of concordance according to the CCI and the APACHE III score was 69.1%. Conclusion: The route of admission, days in ICU, CCI, and APACHE III score are associated with an increased mortality risk in ICU patients.