• Nuclear Engineering and Technology
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• 제52권9호
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• pp.2100-2106
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• 2020

This paper newly proposes an efficient analytic springback prediction method to predict the final dimensions of bent cylindrical tubes for a helical tube steam generator in a small modular reactor. Three-dimensional bending procedure is treated as a two-dimensional in-plane bending procedure by integrating the Euler beam theory. To enhance the accuracy of the springback prediction, mathematical representations of flow stress and elastic modulus for unloading are systematically integrated into the analytic prediction model. This technique not only precisely predicts the final dimensions of the bent helical tube after a springback, but also effectively predicts the various target radii. Numerical validations were performed for five different radii of helical tube bending by comparing the final radius after a springback.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제52권3호
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• pp.137-143
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• 2003

The degree of contamination on outdoor insulator is ons of the most importance factor to determine the pollution level of outdoor insulation, and the sea salt is known as the most dangerous pollutant. As shown through the preceding study, the generation of salt pollutant and the pollution degree of outdoor insulator have a close relation with meteorological conditions, such as wind velocity, wind direction, precipitation and so fourth. So, in this paper, we made an investigation on the prediction method, a statistical estimation technique for equivalent salt deposit density of outdoor insulator with multiple linear regression analysis. From the results of the analysis, we proved the superiority of the prediction method in which the variables had a very close(about 0.9) correlation coefficient. And the results could be applied to establish the Pollution Prediction System for power utilities, and the system could provide an invaluable information for the design and maintenance of outdoor insulation system.

• 정보통신설비학회논문지
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• 제10권2호
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• pp.74-79
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• 2011

The goal of this research is to reduce dynamic and static power consumption for a low power cache system. The proposed cache can achieve a low power consumption by using a drowsy and a way prediction mechanism. For reducing the static power, the drowsy technique is used at 4-way set associative cache. And for reducing the dynamic energy, one among four ways is selectively accessed on the basis of information in the Way-Line Prediction Unit (WLPU). This prediction mechanism does not introduce any additional delay though prediction misses are occurred. The WLPU can effectively reduce the performance overhead of the conventional drowsy caching by waking only a drowsy cache line and one way in advance. Our results show that the proposed cache can reduce the power consumption by about 40% compared with the 4-way drowsy cache.

• 한국통신학회논문지
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• 제33권10C호
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• pp.778-786
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• 2008

가장 최근에 표준화된 H.264 동영상 압축 부호화 기법에서는 많은 새로운 기술들을 적용하여 높은 부호화 성능을 보인다. 특히 최적의 인트라 예측 모드를 결정하기 위해 모든 예측 모드들에 대해 비트율 왜곡 최적(rate distortion optimization) 기법을 적용하기 때문에 많은 계산량을 필요로 한다. 본 논문에서는 분산을 이용하여 인트라 모드를 결정하고 영상의 edge들을 찾아내는 연산을 통해 블록의 방향성을 찾고 그 방향성을 이용하여 인트라 예측 모드 결정에 필요한 계산량을 감소시키는 효율적인 기법을 제안한다.

• 대한안전경영과학회지
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• 제7권1호
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• pp.199-210
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• 2005

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

• 전기학회논문지
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• 제60권7호
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• pp.1396-1403
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• 2011

It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3100-3116
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• 2012

Reversible data hiding enables the embedding of messages in a host image without any loss of host content, which is proposed for image authentication that if the watermarked image is deemed authentic, we can revert it to the exact copy of the original image before the embedding occurred. In this paper, we present an improved histogram-based reversible data hiding scheme based on prediction and sorting. A rhombus prediction is employed to explore the prediction for histogram-based embedding. Sorting the prediction has a good influence on increasing the embedding capacity. Characteristics of the pixel difference are used to achieve large hiding capacity while keeping low distortion. The proposed scheme exploits a two-stage embedding strategy to solve the problem about communicating peak points. We also present a histogram shifting technique to prevent overflow and underflow. Performance comparisons with other existing reversible data hiding schemes are provided to demonstrate the superiority of the proposed scheme.

• 대한공간정보학회지
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• 제24권1호
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• pp.99-107
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• 2016

대한민국은 화산재해로부터 안전한 지역으로 알려져 왔다. 그러나 최근의 관측 결과들은 한반도 최 북 단에 위치한 백두산이 더 이상 휴화산이 아님을 보여주고 있다. 백두산 화산이 폭발한다면 남한지역에서는 화산재에 의한 다양한 피해가 예상된다. 특히 공중의 화산재는 운항되는 비행기의 계기판 및 엔진을 마비시킴으로써 대형 항공사고를 유발할 수 있다. 따라서 화산재의 삼차원 확산을 예측하여 화산재가 있는 항로를 비행할 것으로 예상되는 비행기 운항을 중지시키는 것이 매우 중요하다고 할 수 있다. 본 논문에서는 화산재 확산 예측결과의 삼차원 가시화 기법을 다룬다. 우선 화산재 확산 예측 데이터의 취득에 대하여 소개한다. 확산 예측 데이터는 화산재 확산 시뮬레이션 프로그램인 Fall3D를 이용한다. 다음으로 세 가지 화산재 확산 예측결과의 가시화 기법을 제안한다. 첫 번째 기법은 '공중의 큐브' 방식으로 화산재의 입자 농도별로 다른 색을 가진 반투명 큐브를 공중에 배치하는 방식이다. 두 번째 기법은 '큐브안의 큐브'방식으로 '공중의 큐브' 방식을 개선하여 농도의 정도에 따라 큐브의 분할 정도를 달리하여 배치하는 방식이다. 마지막 방식은 '반투명 화산재 평면' 기법으로 화산재 농도를 가지고 있는 레이어 들을 적층하고 투명효과를 적용하는 방식이다. 본 논문에서 제시한 방법을 기반으로 사용자는 목적에 맞는 방식대로 화산재 확산 예측 결과를 삼차원 가시화 할 수 있을 것이다.

• 한국IT서비스학회지
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• 제11권1호
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.