• Title/Summary/Keyword: Volume Data Modeling

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Two-Dimensional Finite-Volume Unsteady-Flow Model for Shocks (충격파 모의를 위한 이차원 유한체적 비정상 흐름 모형)

  • Lee, Gil-Seong;Lee, Seong-Tae
    • Journal of Korea Water Resources Association
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    • v.31 no.3
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    • pp.279-290
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    • 1998
  • The height and speed of the shock wave are critical data in flood-control operations or in the design of channel walls and bridges along rivers with high flow velocities. Therefore, a numerical model is needed for simulating flow discontinuity over a wide range of conditions. In this study, a governing equation. As a Riemann solver Roe(1981)'s one is used. The model employs the modified MUSCL for handling the unstructured grids in this research. this model that adopts the explicit tradditional twl dimmensional dam break problems, two hydraulic dam break model is simulations, and a steady state simulation in a curved channel. Conclusions of this research are as follows : 1) the finite volume method can be combined with the Godonov-type method that is useful for modeling shocks. Hence, the finite volume method is suitable for modeling shocks. 2) The finite volume model combined with the modified MUSCL is successful in modeling shock. Therefore, modified MUSCL is proved to be valid.

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Statistical analysis and probabilistic modeling of WIM monitoring data of an instrumented arch bridge

  • Ye, X.W.;Su, Y.H.;Xi, P.S.;Chen, B.;Han, J.P.
    • Smart Structures and Systems
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    • v.17 no.6
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    • pp.1087-1105
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    • 2016
  • Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

Modeling of Value Chain for Big Data (빅데이터를 위한 가치사슬 설계)

  • Lee, Sangwon;Park, Sungbum;Lee, Jumin;Ahn, Hyunsup;Choi, Yong Goo
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2015.01a
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    • pp.277-278
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    • 2015
  • The volume sub-challenge requires novel approaches, often referred to as Big Data technologies and methodologies. Data is generated constantly in an ever growing number of places and by an ever growing number of actors while a large proportion of potentially re-usable data resides within silos within institutions or companies. These are needed when conventional database technologies cannot be applied to storage and computing issues. The issue of big data has been referred to as the next frontier in computing. In this paper, we research on factors to design an organizational value chain for Big Data.

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A Knowledge Discovery Framework for Spatiotemporal Data Mining

  • Lee, Jun-Wook;Lee, Yong-Joon
    • Journal of Information Processing Systems
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    • v.2 no.2
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    • pp.124-129
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    • 2006
  • With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.

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.

A Image-based 3-D Shape Reconstruction using Pyramidal Volume Intersection (피라미드 볼륨 교차기법을 이용한 영상기반의 3차원 형상 복원)

  • Lee Sang-Wook
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.1
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    • pp.127-135
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    • 2006
  • The image-based 3D modeling is the technique of generating a 3D graphic model from images acquired using cameras. It is being researched as an alternative technique for the expensive 3D scanner. In this paper, I propose the image-based 3D modeling system using calibrated camera. The proposed algorithm for rendering 3D model is consisted of three steps, camera calibration, 3D shape reconstruction and 3D surface generation step. In the camera calibration step, I estimate the camera matrix for the image aquisition camera. In the 3D shape reconstruction step, I calculate 3D volume data from silhouette using pyramidal volume intersection. In the 3D surface generation step, the reconstructed volume data is converted to 3D mesh surface. As shown the result, I generated relatively accurate 3D model.

An Efficient Collision Queries in Parallel Close Proximity Situations

  • Kim, Dae-Hyun;Choi, Han-Soo;Kim, Yeong-Dong
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.2402-2406
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    • 2005
  • A collision query determines the intersection between given objects, and is used in computer-aided design and manufacturing, animation and simulation systems, and physically-based modeling. Bounding volume hierarchies are one of the simplest and most widely used data structures for performing collision detection on complex models. In this paper, we present hierarchy of oriented rounded bounding volume for fast proximity queries. Designing hierarchies of new bounding volumes, we use to combine multiple bounding volume types in a single hierarchy. The new bounding volume corresponds to geometric shape composed of a core primitive shape grown outward by some offset such as the Minkowski sum of rectangular box and a sphere shape. In the experiment of parallel close proximity, a number of benchmarks to measure the performance of the new bounding box and compare to that of other bounding volumes.

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A Study on Improvement of the Use and Quality Control for New GNSS RO Satellite Data in Korean Integrated Model (한국형모델의 신규 GNSS RO 자료 활용과 품질검사 개선에 관한 연구)

  • Kim, Eun-Hee;Jo, Youngsoon;Lee, Eunhee;Lee, Yong Hee
    • Atmosphere
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    • v.31 no.3
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    • pp.251-265
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    • 2021
  • This study examined the impact of assimilating the bending angle (BA) obtained via the global navigation satellite system radio occultation (GNSS RO) of the three new satellites (KOMPSAT-5, FY-3C, and FY-3D) on analyses and forecasts of a numerical weather prediction model. Numerical data assimilation experiments were performed using a three-dimensional variational data assimilation system in the Korean Integrated Model (KIM) at a 25-km horizontal resolution for August 2019. Three experiments were designed to select the height and quality control thresholds using the data. A comparison of the data with an analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system showed a clear positive impact of BA assimilation in the Southern Hemisphere tropospheric temperature and stratospheric wind compared with that without the assimilation of the three new satellites. The impact of new data in the upper atmosphere was compared with observations using the infrared atmospheric sounding interferometer (IASI). Overall, high volume GNSS RO data helps reduce the RMSE quantitatively in analytical and predictive fields. The analysis and forecasting performance of the upper temperature and wind were improved in the Southern and Northern Hemispheres.

Bootstrap simulation for quantification of uncertainty in risk assessment

  • Chang, Ki-Yoon;Hong, Ki-Ok;Pak, Son-Il
    • Korean Journal of Veterinary Research
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    • v.47 no.2
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    • pp.259-263
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    • 2007
  • The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.

Prediction of Three-Dimensional Solder Joint Profile in Gullwing Lead using Finite Element Modeling (유한요소 모델링을 이용한 Gullwing 리드의 3차원 솔더 접합부 형상 예측)

  • 최동필;유증돈;이태수
    • Journal of Welding and Joining
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    • v.16 no.4
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    • pp.109-116
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    • 1998
  • The three-dimensional profile of a solder fillet is predicted by minimizing the surface tension and gravity energies of the solder joint using finite element modeling. Geometric complexity stemming from the inclined plane of the gullwing lead is resolved by employing three element types. These element types are used to describe the joint profile formed on the vertical, inclined and interfacial planes. The predicted solder joint profiles show good agreements with the experimental data provided that the solder volume is adjusted considering the wicking effects. Effects of the pad length, inclined lead angle and solder volume on joint profiles are also investigated.

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