• 한국경영과학회지
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• 제31권1호
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• pp.15-24
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• 2006

In simulation input modeling, it is important to identify a probability distribution to represent the input process of interest. In this paper, an appropriate sample size is determined for parameter estimation associated with some typical probability distributions frequently encountered in simulation input modeling. For this purpose, a statistical measure is proposed to evaluate the effect of sample size on the precision as well as the accuracy related to the parameter estimation, square rooted mean square error to parameter ratio. Based on this evaluation measure, this sample size effect can be not only analyzed dimensionlessly against parameter's unit but also scaled regardless of parameter's magnitude. In the Monte Carlo simulation experiments, three continuous and one discrete probability distributions are investigated such as ; 1) exponential ; 2) gamma ; 3) normal ; and 4) poisson. The parameter's magnitudes tested are designed in order to represent distinct skewness respectively. Results show that ; 1) the evaluation measure drastically improves until the sample size approaches around 200 ; 2) up to the sample size about 400, the improvement continues but becomes ineffective ; and 3) plots of the evaluation measure have a similar plateau pattern beyond the sample size of 400. A case study with real datasets presents for verifying the experimental results.

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.2197-2204
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• 2015

In recent years, medical image diagnosis has growing significant momentous in the medicinal field. Brain and lung image of patient are distorted with salt and pepper noise is caused by moving the head and chest during scanning process of patients. Reconstruction of these images is a most significant field of diagnostic evaluation and is produced clearly through techniques such as linear or non-linear filtering. However, restored images are produced with smaller amount of noise reduction in the presence of huge magnitude of salt and pepper noises. To eliminate the high density of salt and pepper noises from the reproduction of images, a new efficient fuzzy based median filtering algorithm with a moderate elapsed time is proposed in this paper. Reproduction image results show enhanced performance for the proposed algorithm over other available noise reduction filtering techniques in terms of peak signal -to -noise ratio (PSNR), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), image enhancement factor (IMF) and structural similarity (SSIM) value when tested on different medical images like magnetic resonance imaging (MRI) and computer tomography (CT) scan brain image and CT scan lung image. The introduced algorithm is switching filter that recognize the noise pixels and then corrects them by using median filter with fuzzy two-sided π- membership function for extracting the local information.

• 한국통신학회논문지
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• 제31권10C호
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• pp.991-999
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• 2006

이 논문은 라플라스 밀도 함수에 대한 최적 흩 양자기의 지지역의 증가는 양자점의 개수와 대수적인 관계가 있음을 보여준다. 구체적으로, 분산이 1인 라플라스 밀도함수에 대해서 양자정의 개수 N이 증가할 때 최적 양자기의 경계값에 의해 결정되는 지지역과 $\frac 3{\sqrt{2}}1n\frac N 2$의 비율이 1로 수렴함을 보여준다. 또한 극한 상한값을 유도하여 최적 지지역의 로그적 증가가 그 값을 초과하지 않음을 보였다. 이 결과들로부터 이전부터 경험적으로 연구되어 온 최적 지지역의 로그 증가를 확인 할 수 있다.

• Journal of Information Technology Applications and Management
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• 제28권4호
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• pp.13-20
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• 2021

In this paper, I was applied the life distribution following linear failure rate distribution, Lindley distribution and Burr-Hatke exponential distribution extensively used in the arena of software reliability and were associated the reliability possessions of the software using the nonhomogeneous Poisson process with finite failure. Furthermore, the average value functions of the life distribution are non-increasing form. Case of the linear failure rate distribution (exponential distribution) than other models, the smaller the estimated value estimation error in comparison with the true value. In terms of accuracy, since Burr-Hatke exponential distribution and exponential distribution model in the linear failure rate distribution have small mean square error values, Burr-Hatke exponential distribution and exponential distribution models were stared as the well-organized model. Also, the linear failure rate distribution (exponential distribution) and Burr-Hatke exponential distribution model, which can be viewed as an effectual model in terms of goodness-of-fit because the larger assessed value of the coefficient of determination than other models. Through this study, software workers can use the design of mean square error, mean value function as a elementary recommendation for discovering software failures.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 한국공간구조학회논문집
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• 제24권2호
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권5호
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• pp.543-550
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• 2000

블럭 정합 알고리즘의 VLSI 구현시 복잡도를 줄이고, 수행 속도를 높이기 위하여 새로운 정합 기준인 적응적 비트 축소 MAD(adaptive bit-reduced mean absolute difference:ABRMAD)를 제안한다. ABRMAD는 기존의 MAD에서 화소의 모든 비트를 비교하는 대신, 화소를 구성하는 중요한 비트만을 고려하여 축소된 화소 값을 비교하여 움직임 벡터를 찾는다. 실험을 통하여, 제안한 정합 기준은 기존의 MAD 정합 기준에 비하여 낮은 하드웨어 복잡도를 가지면서 MSE(mean square error) 측면에서 유사한 성능을 가짐을 보인다. 또한 기존의 비트 수 축소형 정합 기준인 DPC(difference pixel counting), BBME(binary-matching with edge-map), 그리고 BPM(bit-plane matching)과 비교하여 같은 수의 비트를 사용하였을 경우 좋은 MSE 성능을 가짐을 보인다.

• 한국통신학회논문지
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• 제22권7호
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• pp.1468-1476
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• 1997

상태축약심볼열추정(RSSE: Reduced State Sequence Estimation) 수신기는 비터비 복호기와 채널 추정기로 구성된다. 이동통신과 같이 채널이 변하는 환경에서는 적응 채널추정기(adaptive channel estimator)로 채널의 변화를 계속적으로 추정해야 한다. 일반적으로 사용되는 채널 추정기는 임시결정된 비터비 복호기의 출력을 사용하여 채널을 추정 하는데, 비터비 복호기에서 잘못된 결정을 내릴 경우 이로 인해 오류전파(error propagation)가 발생할 수있다. 본 논문에서는 좀더 정확한 채널 추정과 오류전파를 막기 위해 경로 메모리를 사용하는 새로운 채널추정기를 사용한다. 이 채널 추정기는 비터비 복호기의 여러 경로중에서 가장 작은 경로를 선택하여 그 경로상의 신호를 이용하여 채널 추정을 행한다. 그리고 채널 추정기의 적응 알고리듬으로서 LMS(Least Mean Square)알고리듬과 Recursive Least Square(RLS) 알고리듬을 사용하여 비교한다. 실험 결과를 통해 제안된 채널 추정기를 사용하는 RSSE 수신기가 기존의 채널 추정기를 사용하는 RSSE 수신기에 비해 더 나은 성능을 나타내는 것을 볼 수있으며, 페이딩이 존재하는 이동통신 환경에서는 LMS 알고리듬이 적합하지 않음을 알 수있다.

• 음성과학
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• 제10권2호
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• pp.77-84
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• 2003

In this research, we seek the beginning of the speech and detect the stationary speech region using lip information. Performing running average of the estimated speech signal in the stationary region, we reduce the effect of musical noise which is inherent to the conventional MlMSE (Minimum Mean Square Error) speech enhancement algorithm. In addition to it, SFM (Spectral Flatness Measure) is incorporated to reduce the speech signal estimation error due to speaking habit and some lacking lip information. The proposed algorithm with Wiener filtering shows the superior performance to the conventional methods according to MOS (Mean Opinion Score) test.