• Title/Summary/Keyword: Temporal data modeling

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Temporal Prediction of Ice Accretion Using Reduced-order Modeling (차원축소모델을 활용한 시간에 따른 착빙 형상 예측 연구)

  • Kang, Yu-Eop;Yee, Kwanjung
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.50 no.3
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    • pp.147-155
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    • 2022
  • The accumulated ice and snow during the operation of aircraft and railway vehicles can degrade aerodynamic performance or damage the major components of vehicles. Therefore, it is crucial to predict the temporal growth of ice for operational safety. Numerical simulation of ice is widely used owing to the fact that it is economically cheaper and free from similarity problems compared to experimental methods. However, numerical simulation of ice generally divides the analysis into multi-step and assumes the quasi-steady assumption that considers every time step as steady state. Although this method enables efficient analysis, it has a disadvantage in that it cannot track continuous ice evolution. The purpose of this study is to construct a surrogate model that can predict the temporal evolution of ice shape using reduced-order modeling. Reduced-order modeling technique was validated for various ice shape generated under 100 different icing conditions, and the effect of the number of training data and the icing conditions on the prediction error of model was analyzed.

Temporal Video Modeling of Cultural Video (교양비디오의 시간지원 비디오 모델링)

  • 강오형;이지현;고성현;김정은;오재철
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2004.05b
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    • pp.439-442
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    • 2004
  • Traditional database systems have been used models supported for the operations and relationships based on simple interval. video data models are required in order to provide supporting temporal paradigm, various object operations and temporal operations, efficient retrieval and browsing in video model. As video model is based on object-oriented paradigm, 1 present entire model structure for video data through the design of metadata which is used of logical schema of video, attribute and operation of object, and inheritance and annotation. by using temporal paradigm through the definition of time point and time interval in object-oriented based model, we tan use video information more efficiently by me variation.

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Modeling of Data References with Temporal Locality and Popularity Bias (시간 지역성과 인기 편향성을 가진 데이터 참조의 모델링)

  • Hyokyung Bahn
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.23 no.6
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    • pp.119-124
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    • 2023
  • This paper proposes a new reference model that can represent data access with temporal locality and popularity bias. Among existing reference models, the LRU-stack model can express temporal locality, which is a characteristic that the more recently referenced data has, the higher the probability of being referenced again. However, it cannot take into account differences in popularity of the data. Conversely, the independent reference model can reflect the different popularity of data, but has the limitation of not being able to model changes in data reference trends over time. The reference model presented in this paper overcomes the limitations of these two models and has the feature of reflecting both the popularity bias of data and their changes over time. This paper also examines the relationship between the cache replacement algorithm and the reference model, and shows the optimality of the proposed model.

Stochastic precipitation modeling based on Korean historical data

  • Kim, Yongku;Kim, Hyeonjeong
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1309-1317
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    • 2012
  • Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

Vertical Migration of Sound Scatterers in the Southern Yellow Sea in Summer

  • Lu, Lian-Gang;Liu, Jianjun;Yu, Fei;Wu, Wei;Yang, Xiaodong
    • Ocean Science Journal
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    • v.42 no.1
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    • pp.1-8
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    • 2007
  • Acoustic volume backscattering strength data were collected and Conductivity Temperature Depth (CTD) measurements were conducted in the southern Yellow Sea in summer 2005 and 2006. The high temporal and vertical resolution acoustic data measured with a 307 kHz Acoustic Doppler Current Profiler (ADCP) and a 250 kHz acoustic Doppler profile (ADP) had dominant diel variation, which resulted from vertical migration of sound scatterers. Some scatterers congregating in the bottom layer in the daytime migrated upward at dusk, and migrated downward into the bottom layer at dawn. The migration speeds were estimated. More than 33 days data show that the diel migration varies with time. The feature of migration measured with ADCP and ADP is consistent to some extent with what is described in the study on vertical migration of zooplankton in the southern Yellow Sea with conventional net samples.

GRID-BASED SOIL-WATER EROSION AND DEPOSITION MODELING USING GIS AND RS

  • Kim, Seong-Joon
    • Water Engineering Research
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    • v.2 no.1
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    • pp.49-61
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    • 2001
  • A grid-based KIneMatic wave soil-water EROsion and deposition Model(KIMEROM) that predicts temporal variation and spatial distribution of sediment transport in a watershed was developed. This model uses ASCII-formatted map data supported from the regular gridded map of GRASS (U.S. Army CERL, 1993)-GIS(Geographic Information Systems), and generates the distributed results by ASCII-formatted map data. For hydrologic process, the kinematic wave equation and Darcy equation were used to simulated surface and subsurface flow, respectively (Kim, 1998; Kim et al., 1998). For soil erosion process, the physically-based soil erosion concept by Rose and Hairsine (1988) was used to simulate soil-water erosion and deposition. The model adopts single overland flowpath algorithm and simulates surface and subsurface water depth, and sediment concentration at each grid element for a given time increment. The model was tested to a 162.3 $\textrm{km}^2$ watershed located in the tideland reclaimed ares of South Korea. After the hydrologic calibration for two storm events in 1999, the results of sediment transport were presented for the same storm events. The results of temporal variation and spatial distribution of overland flow and sediment areas are shown using GRASS.

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Store-Release based Distributed Hydrologic Model with GIS (GIS를 이용한 기저-유출 바탕의 수문모델)

  • Kang, Kwang-Min;Yoon, Se-Eui
    • Proceedings of the Korea Water Resources Association Conference
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    • 2012.05a
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    • pp.35-35
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    • 2012
  • Most grid-based distributed hydrologic models are complex in terms of data requirements, parameter estimation and computational demand. To address these issues, a simple grid-based hydrologic model is developed in a geographic information system (GIS) environment using storage-release concept. The model is named GIS Storage Release Model (GIS-StoRM). The storage-release concept uses the travel time within each cell to compute howmuch water is stored or released to the watershed outlet at each time step. The travel time within each cell is computed by combining the kinematic wave equation with Manning's equation. The input to GIS-StoRM includes geospatial datasets such as radar rainfall data (NEXRAD), land use and digital elevation model (DEM). The structural framework for GIS-StoRM is developed by exploiting geographic features in GIS as hydrologic modeling objects, which store and process geospatial and temporal information for hydrologic modeling. Hydrologic modeling objects developed in this study handle time series, raster and vector data within GIS to: (i) exchange input-output between modeling objects, (ii) extract parameters from GIS data; and (iii) simulate hydrologic processes. Conceptual and structural framework of GIS StoRM including its application to Pleasant Creek watershed in Indiana will be presented.

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Video Data Modeling for Supporting Structural and Semantic Retrieval (구조 및 의미 검색을 지원하는 비디오 데이타의 모델링)

  • 복경수;유재수;조기형
    • Journal of KIISE:Databases
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    • v.30 no.3
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    • pp.237-251
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    • 2003
  • In this paper, we propose a video retrieval system to search logical structure and semantic contents of video data efficiently. The proposed system employs a layered modelling method that orBanifes video data in raw data layer, content layer and key frame layer. The layered modelling of the proposed system represents logical structures and semantic contents of video data in content layer. Also, the proposed system supports various types of searches such as text search, visual feature based similarity search, spatio-temporal relationship based similarity search and semantic contents search.

Reduced Order Modeling of Backward-Facing-Step Flow Field (후향계단 유동장 축약모델링 기법)

  • Lee, Jin-Ik;Lee, Eun-Seok
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.40 no.10
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    • pp.833-839
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    • 2012
  • In this paper, we analyze the reconstruction error in the modeling of flow field on BFS(Backward Facing Step). In order for the mathematical modelling of a density on the field, the spatial and temporal modes are extracted by POD(Proper Orthogonal Decomposition) method. After formulating the modeling error, we summarize the relationship between the energy strength and the reconstruction errors. Moreover the allowable modeling error limits in the flow control point of view are confined by analysing in the frequency domain as well as time domain of the reconstructed data.