• Computers and Concrete
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• 제32권2호
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• 산업경영시스템학회지
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• 제41권1호
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• pp.94-101
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• 2018

With the development of industrial technology and science, production and manufacturing facilities have been enhanced and improved, and the importance of the safety of workers has also been regulated and limited by various safety management methods. As a way to secure the safety of the workers from the production facility, the fail-safe and fool-proof methods are now being applied. Any possible insecure behavior and unsafe conditions can be removed by adopting the standards and specifications that are now secure the safety of workers and equipment. This research analyzes EN ISO 13849-1 international and European standards during CE certification. In order to secure acceptable reduced risks, the risk assessment process of ISO 12100 and the processes for reducing its risk are applied. In the current ISO 13849-1 standard, the criteria for the required performance level PLr (Required Performance Level) for the applicable risk and safety functions through the risk assessment are subjective and not subdivided. Therefore, the evaluation criteria are likely to cause judge's judgement error due to qualitative judgement. This research focuses on evaluation and acceptable performance level setting for the safety circuit of the equipment. We propose an objective and specific evaluation criteria to secure safety, and the proposed evaluation criteria are applied to the case study of the safety circuit for the equipment. In order to secure the safety of the entire safety circuit, the improvement of the MTTFd and DC level related to the SRP/CS (Safety-Related Parts of Control Systems)' lifetime is required for the future research.

• 한국항만경제학회지
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• 제39권2호
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• pp.129-141
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• 2023

본 연구는 오차교정모형을 활용해 건화물선과 유조선 일간 해상운임의 동태적 특성과 예측 정확도를 분석한다. 공적분된 시계열 자료의 오차를 계산하기 위해 본 연구는 공통 확률적 추세 모형(Common Stochastic Trend Model, CSTM 모형)과 벡터오차교정모형(Vector Error Correction Model, VECM 모형)을 활용한다. 먼저, CSTM 모형의 오차를 사용한 오차교정모형이 VECM 모형의 경우보다 교정계수(adjustment speed coefficient)가 경제학적 이론에 더 부합하는 결과를 보인다. 나아가 조정결정계수(adjR²) 측면에서도 CSTM 모형의 경우가 VECM 모형에 비해 모형 적합도가 큰 것으로 나타난다. 둘째, 예측 정확도를 판단하는 지표인 평균 절대 오차와 평균 절대 척도 오차를 살펴보면, CSTM 모형의 오차를 이용한 모형이 VECM 모형의 오차를 이용한 모형보다 총 15가지 경우 중에 12가지 경우에서 예측 정확도가 높은 것을 확인할 수 있다. 미래 연구주제로서 1) 두 가지 오차를 모두 활용하는 분석 및 예측 과제, 2) 원자재 및 에너지 자원 시장의 데이터를 추가하는 과제, 3) 오차항의 부호에 따라 교정계수를 다르게 추정하는 과제 등을 제시한다.

• The Journal of the Acoustical Society of Korea
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• 제16권1E호
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• pp.41-48
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• 1997

The very purpose of subband codec is the attainment of data rate compression through the use of quantizer and optimum bit allocation for each decimated signal. Yet the question of floating-point quantization effects in subband codec has received scant attention. There has been no direct focus on the analysis of quantization errors, nor on design with quantization errors embedded explicitly in the criterion. This paper provides a rigorous theory for the modelling, analysis and optimum design of the general M-band subband codec in the presence of the floating-point quantization noise. The floating-point quantizers are embedded into the codec structure by its equivalent multiplicative noise model. We then decompose the analysis and synthesis subband filter banks of the codec into the polyphase form and construct an equivalent time-invariant structure to compute exact expression for the mean square quantization error in the reconstructed an equivalent time-invariant structure to compute exact expression for the mean square quantization error in the reconstructed output. The optimum design criteria of the subband codec is given to the design of the analysis/synthesis filter bank and the floating-point quantizer to minimize the output mean square error. Specific optimum design examples are developed with two types of filter of filter banks-orthonormal and biorthogonal filter bank, along with their perpormance analysis.

• 한국공작기계학회논문집
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• 제18권2호
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1439-1448
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• 2022

Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expression using deep learning. Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquired from the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2) from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity prediction model was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied to train and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using the large-errored samples as a validation set, to determine whether the error was from the high bias of the folded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relative error<10%. Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the folded CV. Through an additional LOOCV, one more sample was correctly predicted, representing a prediction accuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172 correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively. Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity prediction using gene expression.

• 한국융합학회논문지
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• 제6권5호
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• pp.131-142
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• 2015

본 연구는 중국어 모어 화자 한국어 학습자의 쓰기에 나타난 오류 분석을 실시하여 오류의 원인을 살펴보고 이를 분석한 내용을 바탕으로 학습자들이 생산해 내는 오류에 효과적인 교육 방안을 제시하는데 있다. 본 연구의 분석 대상 자료는 '내가 생각하는 한국', '중국과 한국의 문화융합에 대하여', '친구' 등의 주제로 작문한 것을 바탕으로 모든 문장을 입력하고 이들 문장 중에서 어휘 오류를 추출하여 수정한 후, 본 연구자가 설정한 오류 유형의 범주에 의하여 재분류하였으며 분류한 오류의 빈도수를 작성하였다. 연구의 결과, 대치 오류 > 철자 오류 > 오형태 > 누락 오류 > 첨가 오류 의 순으로 오류의 유형이 나타났다. 오류 방지를 위한 교육 방안으로 문법적인 요소나 어휘의 형태적인 면을 제시할 경우에는 그 부분의 제약이 되는 점을 같이 제시해줘야 오류를 미연에 방지할 수 있을 것이다.

• 한국철도학회논문집
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• 제11권3호
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• pp.326-333
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• 2008

본 논문은 누적통과톤수에 의한 레일교체기준 개정을 위한 기초 데이터를 제시하는데 목적이 있다. 본 연구에서는 국외의 누적통과톤수에 의한 레일교체기준의 산정근거를 조사하였으며, 일본에서 조사된 레일용접부 결합유형 및 원인과 국내 철도운영기관인 서울메트로의 궤도유지관리이력을 조사하여 실제 레일절손과 누적통과톤수의 상관관계를 분석하였다. 또한, 누적통과톤수 기준치에 도래한 노후레일 용접부를 현장에서 발췌하여 레일용접부 휨시험을 수행하였다. 그 결과, 누적통과톤수와 레일절손간의 상관관계는 뚜렷하지 않았으며 레일용접부의 시공불량에 의한 절손사례가 많은 것으로 분석되었다. 또한, 노후레일휨시험 결과 신규레일용접부에 비해 파괴강도가 $17{\sim}18%$만이 저하된 것으로 나타나 레안교체기준에 도래한 노후레일은 사용성 측면에서 충분한 내구성 및 내하력을 확보하고 있는 것으로 분석되었다.

• 한국정보처리학회논문지
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• 제6권9호
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• pp.2384-2392
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• 1999

프로그램에서 요류가 발생되었을 때 프로그래머는 어떤 시험 사례(test case)를 통해 프로그램을 분석한다. 이처럼 현재 입력 값에 영향을 끼치는 모든 명령문들에 관련된 동적 슬라이싱(dynamic slicing)과 이를 구현하는 기술은 실제 테스팅 및 디버깅 분야에서 매우 중요하다고 할 것이다. 지금까지의 동적 슬라이싱은 슬라이싱 기준 변수가 1개 일 때의 경우에 대해서만 연구해 왔다. 그러나, 실제적인 테스팅 및 디버깅 분야에서는 슬라이싱 기준이 되는 변수가 2개 이상인 경우가 아주 많이 발생한다. 따라서 슬라이싱 기준 변수가 n 개 일 때 동적 프로그램 슬라이스(dynamic program slices)를 만드는 알고리즘을 제시하였고 프로그래밍 언어를 사용하여 동적 프로그램 슬라이싱 알고리즘을 프로그래밍한 뒤 예제 프로그램을 적용시켜 구현하였다. 구현 결과는 실행 이력에 대한 마킹 테이블(marking table)과 동적 종속 그래프로 나타내었다. 그리고, 본 논문에서 제시한 다중기준변수 동적 슬라이스 생성을 위한 마킹 알고리즘이 기존의 단일기준변수 기법보다 실제적인 테스팅 환경에서 더 우수함을 보였다.