• 한국소음진동공학회논문집
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• 제14권11호
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• pp.1161-1165
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• 2004

Railway noise Is one of the main causes of environmental impact. Whenever a new railway line is planned or a housing project near an existing railway is proposed, an estimate of the relevant noise levels is usually required. For this, it is necessary to quantify those parameters that affect the railway noise. Therefore we investigated the noise level which 107 high speed trains generated passing through the block of test railway track between Chunan and Chungwon. This paper shows the status and characteristics of the high speed rallway noise and an accurate prediction of the high speed railway noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문초록집
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• pp.356.2-356
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• 2002

Railway noise is one of the main causes of environmental impact. Whenever a new railway line is planned or a housing project near an existing railway is proposed, an estimate of the relevant levels is usually required. For this, it is necessary to quantify those parameters that affect the railway noise. Therefore we investigated the noise and vibration level which 107 high speed trains generated passing through the block of test railway track between Chunan and Chungwon. This paper presents the status and characteristics of the high speed railway noise and an accurate prediction of the high speed railway noise.

• 한국균학회소식:학술대회논문집
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• 한국균학회 2018년도 춘계학술대회 및 임시총회
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• pp.22-22
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• 2018

Fungal genome sequencing and assembly have been trivial in these days. Genome analysis relies on high quality of gene prediction and annotation. Automatic fungal genome annotation pipeline is essential for handling genomic sequence data accumulated exponentially. However, building an automatic annotation procedure for fungal genomes is not an easy task. FunGAP (Fungal Genome Annotation Pipeline) is developed for precise and accurate prediction of gene models from any fungal genome assembly. To make high-quality gene models, this pipeline employs multiple gene prediction programs encompassing ab initio, evidence-, and homology-based evaluation. FunGAP aims to evaluate all predicted genes by filtering gene models. To make a successful filtering guide for removal of false-positive genes, we used a scoring function that seeks for a consensus by estimating each gene model based on homology to the known proteins or domains. FunGAP is freely available for non-commercial users at the GitHub site (https://github.com/CompSynBioLab-KoreaUniv/FunGAP).

• 한국강구조학회 논문집
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• 제25권4호
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• pp.409-419
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• 2013

인장력을 받는 고력볼트로 체결된 T-stub는 지레작용력과 부재 사이의 접촉력 등의 영향을 받는다. 이러한 지레작용과 부재 사이의 접촉력 등이 고려된 설계식이 제안되지 않는 경우, 인장력을 받는 T-stub는 예측한 설계강도 보다 더 작은 강도에도 파괴될 가능성이 있다. 이를 방지하기 위하여 지금까지 많은 연구를 통하여 고력볼트로 체결된 T-stub의 지레작용력과 부재 사이의 접촉력 예측모델이 제안되었다. 그러나 아직도 우리나라에서는 이를 반영한 설계식의 제안이 이루어지고 있지 않다. 따라서 이 연구는 3차원 비선형 유한요소해석법을 적용하여 그동안 제안된 예측모델을 개선한 보다 정확한 지레작용력 및 접촉력 예측모델을 제안하고자 진행하였다. 3차원 비선형 유한요소 해석 결과, 이 연구에서 제안한 지레작용력 및 접촉력 예측모델은 기존의 예측모델보다 더 근사적인 예측값을 제공하였다.

• 한국측량학회지
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• 제30권6_2호
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• pp.623-629
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• 2012

During the construction of crossing engineering one of the important measures to ensure the safety of subway operation is the implementation of deformation surveying to the existing subway tunnel. Guangzhou new subway line 2 engineering which crosses the existing tunnel is taken as the background. How to achieve intelligent and automatic deformation surveying forecast during the subway tunnel construction process is studied. Because large amount of surveying data exists in the subway construction, deformation analysis is difficult and prediction has low accuracy, a subway intelligent deformation prediction model based on the PBIL and support vector machine is proposed. The PBIL algorithm is used to optimize the exact key parameters combination of support vector machine though probability analysis and thereby the predictive ability of the model deformation is greatly improved. Through applications on the Guangzhou subway across deformation surveying deformation engineering the prediction method's predictive ability has high accuracy and the method has high practicality. It can support effective solution to the implementation of the comprehensive and accurate surveying and early warning under subway operation conditions with the environmental interference and complex deformation.

• 한국재료학회지
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• 제33권7호
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• 대한전기학회논문지:전력기술부문A
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• 제49권1호
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• pp.37-42
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• 2000

The radio noise produced by corona discharge in high voltage transmission lines is one of the most important line design considerations. Therefore it is necessary for transmission line designers to pre-evaluate radio noise using prediction formulas or field test results. In this paper, more accurate and useful formulas for predicting radio noise during fair and foul weathers in high voltage AC transmission lines were proposed through comparison with the existing formulas. Also it was verified by comparing with the long-term measured data from operating lines that the proposed formulas are more accurate. The proposed prediction formulas are developed by the applications of nonlinear least squares optimization method to radio noise database collected from lines throughout the world.

• Computers and Concrete
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• 제6권2호
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• pp.155-165
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• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• 대한교통학회지
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• 제5권1호
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• pp.59-74
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• 1987

A prediction is an indispensable element to research of Social Science, especially in Regional planning, City planning, and Transportation planning. Since 1930s, varieties of prediction methods have been developed. In the 1980s, numerical models have been used by high-developed computers. even though the numerical models can be figured mathematically, it could not be applied practically due to it's expertness and complicateness. And even professional planners often can not use their ideas which are valuable experiences in prediction process, because they are not knowledgable for numerical models. The YSIM developed by author, is available as follows. i)Numerical modeling of professional experiences ii)Providing a foundation of large-scale model iii) Understanding of research object structure The YSIM make use of matrix to identify the system structure which is similar to the Cross Impact Method. To evaluated the YSIM availabilities, it is compared with the early developed methodologies such as KSIM, QSIM, and SPIN. As the result, it was confirmed that YSIM was more accurate in the prediction. The algorithms in YSIM is programmed for use of PCs.

• 한국전기전자재료학회논문지
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• 제31권7호
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• pp.450-454
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• 2018

Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.