• 한국통신학회논문지
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• 제36권1B호
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• pp.71-79
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• 2011

본 논문은 어플리케이션에 최적화된 임베디드 시스템 설계에 있어 효율적인 설계 공간을 탐색할 수 있도록 머신 기술 언어를 기반으로 한 컴파일드 코드 방식 시뮬레이터 생성 시스템을 제안한다. 제안된 시스템 event-driven 시뮬레이션의 융통성을 유지하면서 많은 시뮬레이션 시간을 소요하는 인스트럭션 펫치와 디코딩 과정을 정적으로 결정하여 빠른 수행시간을 갖는 컴파일드 코드 방식 시뮬레이터를 생성한다. 생성된 시뮬레이터는 임베디드 코어의 성능 측정을 위한 사이클 수준과 인스트럭션 수준의 시뮬레이션을 가진다. 구축된 컴파일드 코드 방식 시뮬레이터 생성기의 효율성을 확인하기 위해 JPEG 인코더 어플리케이션에 대한 아키텍처 탐색을 수행하였다. 제안된 시스템은 MIPS R3000 프로세서의 초기 임베디드 코어로 시작하여 어플리케이션에 최적화된 임베디드 코어를 얻어내었다. 이 과정에서 많은 시뮬레이션 시간이 요구되었다. 사이클 수준 컴파일드 코드 빙식 시뮬레이터는 event-driven 시뮬레이션의 정확성을 가지며 평균 21.7%의 향상된 시뮬레이션의 수행 속도를 보인다.

• 소성∙가공
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• 제18권4호
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• pp.354-360
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• 2009

Hot-forging process and die design were made for a large-scale compressor wheel of Ti-6Al-4V alloy by using the results of 2-D FEM simulation. The design integrated the geometry-controlled approach and the processing contour map based on the dynamic materials model and the flow stability criteria. In order to obtain the processing contour map of Ti-6Al-4V alloy, compression tests were carried out in the temperature range of $915^{\circ}C$ to $1015^{\circ}C$ and the strain rate range of $10^{-3}s^{-1}$ to $10s^{-1}$. In the die design of the compressor wheel using the rigid-plastic FEM simulation, forging dimensional accuracy, the capacity of the forging machine and defect-free forging were considered as main design factors. The microstructure of hot forged wheel using the designed die showed a typical alpha-beta structure without forging-defects.

• 한국농공학회논문집
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• 제66권4호
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• pp.51-57
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• 2024

This study was conducted on how to simulate runoff, which was done using existing physical models, using an LSTM (Long Short-Term Memory) model based on deep learning. Tancheon, the first tributary of the Han River, was selected as the target area for the model application. To apply the model, one water level observatory and four rainfall observatories were selected, and hourly data from 2020 to 2023 were collected to apply the model. River water level of the outlet of the Tancheon basin was simulated by inputting precipitation data from four rainfall observation stations in the basin and average preceding 72-hour precipitation data for each hour. As a result of water level simulation using 2021 to 2023 data for learning and testing with 2020 data, it was confirmed that reliable simulation results were produced through appropriate learning steps, reaching a certain mean absolute error in a short period time. Despite the short data period, it was found that the mean absolute percentage error was 0.5544~0.6226%, showing an accuracy of over 99.4%. As a result of comparing the simulated and observed values of the rapidly changing river water level during a specific heavy rain period, the coefficient of determination was found to be 0.9754 and 0.9884. It was determined that the performance of LSTM, which aims to simulate river water levels, could be improved by including preceding precipitation in the input data and using precipitation data from various rainfall observation stations within the basin.

• Journal of Animal Science and Technology
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• 제58권1호
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• pp.1.1-1.6
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• 2016

Background: Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker. Methods: In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel. Results: Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB. Conclusions: The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

• 한국전자파학회논문지
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• 제28권8호
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• pp.628-635
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• 2017

의약품은 인간의 건강 및 생명과 밀접한 관련이 있기 때문에 증상에 맞는 의약품을 처방받아 복용하는 것은 매우 중요한 문제이다. 더욱이 세계적으로 위조 의약품이 증가하는 상황에서 정품 의약품들을 정확하게 분류하는 기술은 점점 중요해진다. 그러나 의약품을 제대로 분류할 수 있는 전문적인 지식을 갖춘 인력이 제한적이라는 측면에서 의약품을 자동적으로 분류하는 기술이 필요하다. 본 논문에서는 휴대용 분광기를 이용하여 의약품의 근적외선 및 가시광선 스펙트럼을 추출하고, Support Vector Machine(SVM) 기법을 이용하여 추출한 스펙트럼 데이터를 학습시켜 분류하는 방법을 제안하였다. 모의실험을 통해 근적외선과 가시광선 스펙트럼 데이터를 사용하여 6종의 의약품을 학습시키고 분류하였을 때 평균적으로 99.9 %의 정확도를 얻었다. 또한 본 논문에서는 위조 의약품 검출을 위한 2단계 SVM 분류 기법을 제안하였으며, 이를 통해 정품과 위조 의약품을 구분하는 정확도가 향상되고, 처리속도가 개선되는 것을 확인하였다.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Journal of Electrical Engineering and Technology
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• 제11권1호
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• 산업경영시스템학회지
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• 제32권3호
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• pp.40-48
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• 2009

In this article, scheduling of a casting sequence is studied in a casting foundry which must deliver products according to the Just-in-time(JIT) production policy of a customer. When a foundry manufactures a variety of casts with an identical alloy simultaneously, it frequently faces the task of production scheduling. An optimal casting schedule should be emphasized in order to maximize the production rate and raw material efficiency under the constraints of limited resources; melting furnaces and operation time for a casting machine. To solve this practical problem-fulfilling the objectives of casting the assigned mixed orders for the highest raw material efficiency in a way specified by the customer's JIT schedule, we implement simple integer programming. A simulation to solve a real production problem in a typical casting plant proves that the proposed method provides a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Employing this simple methodology, a casting foundry having an automated casting machine can produce a mixed order of casts with a maximum furnace utilization within the due date, and provide them according to their customer's JIT inventory policy.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2002년도 Proceedings of the International Welding/Joining Conference-Korea
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• pp.402-407
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• 2002

Laminate tooling process is a fast and simple method to make metal tools directly for various molding processes such as injection molding in rapid prototyping field. Metal sheets are usually cut, stacked, aligned and joined with brazing or soldering. Through the joining process, all of the metal sheet layers should be rigidly joined. When joining process parameters are not appropriate, there would be defects in the layers. Among various types of defects, non-bonded gaps of the tool surface are of great importance, because they directly affect the surface quality and dimensional accuracy of the final products. If a laminate tool with defects has to be abandoned, it could lead to great loss of time and cost. Therefore a repair method for non-bonded gaps of the surface is essential and has important meaning for rapid prototyping. In this study, a rapid laminate tooling system composed of a CO2 laser, a furnace, and a milling machine was developed. Metal sheets were joined by furnace brazing, dip soldering and adhesive bonding. Joined laminate tools were machined by a high-speed milling machine to improve surface quality. Also, repair brazing and soldering methods of the laminates using the $CO_2$ laser system have been investigated. ill laser repair process, the beam duration, beam power and beam profile were of great importance, and their effects were simulated by [mite element methods. The simulation results were compared with the experimental ones, and optimal parameters for laser repair process were investigated.