• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.157-158
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• 2019

발전소 관리의 단기 전력 수요에 대한 정확한 예측은 전력 시스템의 안전하고 효율적인 작동을 보장하는데 필수적이다. 따라서 본 연구는 가우스 커널 함수 네트워크 (GKFNs)의 심층 구조를 이용하여 일일 최대 전력 수요를 예측하는 새로운 방법을 제시한다. 제안 된 GKFN의 깊이 구조는 표준 GKFN에 비해 예측 정확도를 향상시킨다. 한국의 일일 최대 전력 수요를 예측하기위한 시뮬레이션은 제안 된 예측 모델이 GKFN 모델, k-NN 및 SVR과 같은 다른 예측 모델에 비해 예측 성능에 이점이 있음을 보여준다. GKFN의 제안된 심층 구조는 시계열 예측 및 회귀 문제의 다양한 문제에 적용될 수 있다.

• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.29-38
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• 2019

A path prediction method using lifelog requires a large amount of training data for accurate path prediction, and the path prediction performance is degraded when the training data is insufficient. The lack of training data can be solved using data of other users having similar user movement patterns. Therefore, this paper proposes a path prediction algorithm based on user similarity. The proposed algorithm learns the path in a triple grid pattern and measures the similarity between users using the cosine similarity technique. Then, it predicts the path with applying measured similarity to the learned model. For the evaluation, we measure and compare the path prediction accuracy of proposed method with the existing algorithms. As a result, the proposed method has 66.6% accuracy, and it is evaluated that its accuracy is 1.8% higher than other methods.

• Journal of KIISE
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• v.43 no.12
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• pp.1334-1341
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• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• Tunnel and Underground Space
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• v.25 no.1
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• pp.76-85
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• 2015

We have developed a software to predict failure time of the rock slope based on analysis of the data from real time displacement measurements with respect to time. The software consists of four modules that play roles in analytical methods such as inverse velocity method, log time-log velocity method, log velocity-log acceleration method and nonlinear least square method to estimate failure time. VisualBasic.NET on the MS Visual Studio platform was utilized as a development tool to efficiently implement the modules and the graphical user interface of the software. Displacement data obtained from laboratory physical model studies of plane sliding were used to explore the applicability of the software, and to evaluate the possibility of predicting potential slope failure. It seems possible to estimate failure time using developed software for sliding plane having exponential type of deformability.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.378-381
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• 2020

항공/여행 상품은 타 산업보다 불확실성에 취약하며 시간의 절대적인 종속성으로 인해 정확한 수요 파악 및 예측을 하지 못할 경우 가치가 0으로 수렴한다. 이에 본 논문은 웹 크롤링을 기반으로 잠재여행 욕구를 파악하고, 향후 성장할 것으로 예상되는 항공 노선 및 취항지를 예측 및 분석하는 기법을 제안하고자 한다.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.469-477
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• 2009

In mobile systems, energy efficiency is critical to extend battery life. Therefore, power consumption should be taken into account to develop software in addition to performance, Efficient software design in power and performance is possible if accurate power prediction is accomplished during the execution of software, In this paper, power estimation model is developed using statistical analysis, The proposed model analyzes processor behavior Quantitatively using the data of performance monitoring events and power consumption collected by executing various benchmark programs, And then representative hardware events on power consumption are selected using hierarchical clustering, The power prediction model is established by regression analysis in which the selected events are independent variables and power is a response variable, The proposed model is applied to a PXA320 mobile processor based on Intel XScale architecture and shows average estimation error within 4% of the actual measured power consumption of the processor.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.420-430
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• 2006

In this paper, we propose a flexible GGOP prediction structure to improve coding efficiency for multi-view video coding. In general, reference software used for MVC uses the fixed GGOP prediction structure. However, the performance of MVC depends on the base view and numbers of B-pictures between I-picture(or P-picture) and P-picture. In order to implement the flexible GGOP prediction structure, the location of base view is decided according to the global disparities among the adjacent sequences. Numbers of B-pictures between I-picture(or P-picture) and P-picture are decided by camera arrangement such as the baseline distance among the cameras. The proposed method shows better result than the reference software of MVC. The proposed prediction structure shows considerable reduction of coded bits by 7.1%.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.219-226
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• 2014

In this study, reliability software cost model considering shape parameter based on life distribution from the process of software product testing was studied. The shape parameter using the Erlang and Log-logistic model that is widely used in the field of reliability problems presented. The software failure model was used finite failure non-homogeneous Poisson process model, the parameters estimation using maximum likelihood estimation was conducted. In comparison result of software cost model based on the Erlang distribution and the log-logistic distribution software cost model, because Erlang model is to predict the optimal release time can be software, but the log-logistic model to predict to optimal release time can not be, Erlang distribution than the log-logistic distribution appears to be effective. In this research, software developers to identify software development cost some extent be able to help is considered.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.7-9
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• 2002

최근 소프트웨어 사용자의 요구사항이 빠르게 변화하고 있으며, 그에 따른 소프트웨어 규모도 커지고 있다. 소프트웨어 개발 업체들은 적은 개발비용으로 사용자의 기대를 만족시키는 고품질의 소프트웨어를 단기간에 출시하고자 많은 노력을 기울이고 있으며, 소프트웨어 제품과 프로세스들에 대해서 제언하고 예측할 수 있는 능력을 확보하고자 노력하고 있다. SPICE 모델에 따른 소프트웨어 프로세스 개선은 소프트웨어 개발 업체의 개발 및 관리 문제점을 해결하는데 사용되고 있으나 개선을 위한 지침의 부족으로 개선 실행에 어려움을 보이고 있다. 이어 본 논문에서는 SPICE 모델에 따른 소프트웨어 프로세스 심사 결과의 개선 항목을 잠재적인 결함으로 간주하고, GQM 방법론에 의해서 소프트웨어 프로세스 개선을 수행함으로써 조직의 비전과 목표 프로세스 능력을 달성할 수 있도록 제안한다. 또한, 결함 제거를 위한 트리거를 구축하고, 개선 사항과 타 프로세스와의 연관성을 분석하여 효과적인 프로세스 개선을 유도하고자 한다.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.12
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• pp.1175-1183
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• 2013

Even if some CFD software developers and its users think that a state-of-the-art CFD software can be used to reasonably solve at least single-phase nuclear reactor safety problems, there remain limitations and uncertainties in the calculation result. From a regulatory perspective, the Korea Institute of Nuclear Safety (KINS) is presently conducting the performance assessment of commercial CFD software for nuclear reactor safety problems. In this study, to examine the prediction performance of commercial CFD software with the porous model in the analysis of the scale-down APR (Advanced Power Reactor Plus) internal flow, a simulation was conducted with the on-board numerical models in ANSYS CFX R.14 and FLUENT R.14. It was concluded that depending on the CFD software, the internal flow distribution of the scale-down APR was locally somewhat different. Although there was a limitation in estimating the prediction performance of the commercial CFD software owing to the limited amount of measured data, CFX R.14 showed more reasonable prediction results in comparison with FLUENT R.14. Meanwhile, owing to the difference in discretization methodology, FLUENT R.14 required more computational memory than CFX R.14 for the same grid system. Therefore, the CFD software suitable to the available computational resource should be selected for massively parallel computations.