• Journal of Advanced Marine Engineering and Technology
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• 제23권2호
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• pp.216-225
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• 1999

Four turbulent $k-{varepsilon}$models(i.e standard model modified models with streamline curvature modification and/or preferential dissipation modification) are applied in order to analyze the tur-bulent flow of wall-attaching offset jet. The upwind numerical scheme was adopted in the present analyses. The streamline curvature modification results in slightly better prediction while the preferential dissipation modification does not. The obtained analytic results will be used as refer-ences for further study regarding Reynolds stress model. In addition this paper introduced a method of increasing nozzle outlet velocity gradually for numercal convergence. Even though the method was simple it was efficient in view of convergent speed CPU running time computer memory storage programming etc.

• Nuclear Engineering and Technology
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• 제25권3호
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• pp.394-402
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• 1993

원자력 발전소 운전원에게 발전소 상태를 정확히 알려주는 완벽한 신호가 원자력 발전소 전체의 가용도와 경제성에 얼마만한 영향을 주는가에 대한 정량적인 분석이 이 논문에서 수행되어졌다. 이 분석을 위한 기본 모델들이 개발되어졌고 이 모델들은 발전소가 정상상태에서 비정상상태로 바뀌어갈때 운전원이 발전소가 완전히 비정상상태로 가기전에 발전소를 정상상태로 회복시키는 것에 완벽한 발전소 신호가 얼마만큼 영향을 주는가를 정량화 하였다. 또한 이러한 완벽한 신호가 경제적으로 얼마만큼의 이득을 주는가도 정량화 하였다. 이 모델 적용의 대상으로는 응축 및 주급수 시스템을 선정하였다.

• 천문학회보
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• 제36권1호
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• pp.59.2-59.2
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• 2011

Constraining physical (or stellar population) properties - such as stellar mass, star-formation rate, stellar population age, and dust-extinction - of galaxies from observation is crucial in the study of galaxy evolution. This is very challenging especially for high-redshift galaxies, and a widely-used method to estimate physical properties of high-redshift galaxies is to compare their photometric spectral energy distributions (SEDs) to spectral templates from stellar population synthesis models. I will show that the SED-fitting results of high-redshift galaxies are strongly dependent on the assumed forms of star-formation histories. I will also present the results of SED-fitting analysis of observed Lyman-break galaxies which show that parametric models with gradually increasing star-formation histories provide better estimates of physical parameters of high-redshift (z>3) star-forming galaxies than traditionally-used exponentially declining star-formation histories. This result is also consistent with the predictions from the modern galaxy formation models.

• Nuclear Engineering and Technology
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• 제50권3호
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• pp.379-388
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• 2018

Models for the rate of atomization and deposition of droplets for stratified and annular flow in horizontal pipes are presented. The entrained fraction is the result of a balance between the rate of atomization of the liquid layer that is in contact with air and the rate of deposition of droplets. The rate of deposition is strongly affected by gravity in horizontal pipes. The gravitational settling of droplets is influenced by droplet size: heavier droplets deposit more rapidly. Model calculation and simulation results are compared with experimental data from various diameter pipes. Validation for the suggested models was performed by comparing the Safety and Performance Analysis Code for Nuclear Power Plants calculation results with the droplet experimental data obtained in various diameter horizontal pipes.

• 터널과지하공간
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• 제20권2호
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• pp.73-81
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• 2010

본 논문에서는 여러 가지의 구성방정식 모델을 이용하여 NATM 터널의 유한요소 해석의 적용에 대하여 설명하였다. 일련의 2차원 평면변형률 조건하 NATM 터널의 지반-구조물에 대한 해석을 분석하였다. 사용한 4가지 구성방정식 모델로는 선형탄성, Mohr-Coulomb 탄소성, 변형경화, 연화지반모델이다. 터널설계에서는 지표면의 침하, 지보재 축력에 대한 적절한 예측이 필요하다. 수치해석결과 진보된 구성방정식 모델이 지반변형과 지보재의 축력에 대해 보다 적절한 결과를 나타내었다.

• 대한기계학회논문집A
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• 제29권1호
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.56-67
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• 2019

The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.

• International Journal of Internet, Broadcasting and Communication
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• 제14권2호
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• 한국정보기술학회 영문논문지
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• 제10권2호
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• pp.139-151
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• 2020

Entity name recognition is a part of information extraction that extracts entity names from documents and classifies the types of extracted entity names. Entity name recognition technologies are widely used in natural language processing, such as information retrieval, machine translation, and query response systems. Various deep learning-based models exist to improve entity name recognition performance, but studies that compared and analyzed these models on Korean data are insufficient. In this paper, we compare and analyze the performance of CRF, LSTM-CRF, BiLSTM-CRF, and BERT, which are actively used to identify entity names using Korean data. Also, we compare and evaluate whether embedding models, which are variously used in recent natural language processing tasks, can affect the entity name recognition model's performance improvement. As a result of experiments on patent data and Korean corpus, it was confirmed that the BiLSTM-CRF using FastText method showed the highest performance.

• 한국정보기술학회 영문논문지
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• 제10권1호
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• pp.45-56
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• 2020

Despite extensive research, performance enhancement of keyphrase (KP) extraction remains a challenging problem in modern informatics. Recently, deep learning-based supervised approaches have exhibited state-of-the-art accuracies with respect to this problem, and several of the previously proposed methods utilize Bidirectional Encoder Representations from Transformers (BERT)-based language models. However, few studies have investigated the effective application of BERT-based fine-tuning techniques to the problem of KP extraction. In this paper, we consider the aforementioned problem in the context of scientific articles by investigating the fine-tuning characteristics of two distinct BERT models - BERT (i.e., base BERT model by Google) and SciBERT (i.e., a BERT model trained on scientific text). Three different datasets (WWW, KDD, and Inspec) comprising data obtained from the computer science domain are used to compare the results obtained by fine-tuning BERT and SciBERT in terms of KP extraction.