• Title/Summary/Keyword: advanced models

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Numeical Analysis on wall-Attaching Offset Jet with Various Turbulent $\kappa-\varepsilon$ Models (다양한 $\kappa-\varepsilon$ 난류모델에 의한 단이 진 벽면 분류에 대한 수치해)

  • 윤순현
    • Journal of Advanced Marine Engineering and Technology
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    • v.23 no.2
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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.

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Model Developments for Quantitative Estimates of the Benefits of the Signals on Nuclear Power Plant Availability and Economics (원자력발전소의 가용도와 경제성에 신호가 주는 이득의 정량적 산출을 위한 모델개발)

  • Seong, Poong-Hyun
    • Nuclear Engineering and Technology
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    • v.25 no.3
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    • pp.394-402
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    • 1993
  • A novel framework for quantitative estimates of the benefits of signals on nuclear power plant availability and economics has been developed in this work. The models developed in this work quantify how the perfect signals affect the human operator's success in restoring the power plant to the desired state when it enters undesirable transients. Also, the models quantify the economic benefits of these perfect signals. The models have been applied to the condensate feedwater system of the nuclear power plant for demonstration.

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Constraining Physical Properties of High-redshift Galaxies : Effects of Star-formation Histories

  • Lee, Seong-Kook
    • The Bulletin of The Korean Astronomical Society
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    • v.36 no.1
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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.

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Development of droplet entrainment and deposition models for horizontal flow

  • Schimpf, Joshua Kim;Kim, Kyung Doo;Heo, Jaeseok;Kim, Byoung Jae
    • Nuclear Engineering and Technology
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    • v.50 no.3
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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.

The Analysis of Tunnel Behavior using Different Constitutive Models (다양한 구성방정식에 따른 터널 거동해석)

  • Kim, Young-Min;Kang, Seong-Gwi
    • Tunnel and Underground Space
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    • v.20 no.2
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    • pp.73-81
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    • 2010
  • The paper presents the application of FE simulations of NATM tunnel using different constitutive models. The results from a series of two dimensional plane strain finite element analyses of medium-liner interaction for NATM are presented. Four types of constitutive models are considered, namely, linear elastic, elasto-plastic Mohr-Coulomb, Hardening-Soil, Soft-Soil model. The design for tunnels requires a proper estimate of surface settlement and lining forces. It is shown that the advanced constitutive model gives better predictions for both ground movement and structural forces.

Multi-Vehicle Tracking Adaptive Cruise Control (다차량 추종 적응순항제어)

  • Moon Il ki;Yi Kyongsu
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.1 s.232
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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.

A Study on Artificial Intelligence Based Business Models of Media Firms

  • Song, Minzheong
    • International journal of advanced smart convergence
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    • v.8 no.2
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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.

Efficient Driver Attention Monitoring Using Pre-Trained Deep Convolution Neural Network Models

  • Kim, JongBae
    • International Journal of Internet, Broadcasting and Communication
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    • v.14 no.2
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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.

A Study on the Performance Analysis of Entity Name Recognition Techniques Using Korean Patent Literature

  • Gim, Jangwon
    • Journal of Advanced Information Technology and Convergence
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    • v.10 no.2
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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.

Fine-tuning BERT Models for Keyphrase Extraction in Scientific Articles

  • Lim, Yeonsoo;Seo, Deokjin;Jung, Yuchul
    • Journal of Advanced Information Technology and Convergence
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    • v.10 no.1
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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.