• Atmosphere
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• v.34 no.2
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• pp.217-231
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• 2024

The Korea Integrated Model (KIM)-based real-time volcanic ash dispersion prediction system, which employs the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, has been developed to quantitatively predict volcanic ash dispersion in East Asia and the Northwest Pacific airspace. This system, known as KIM-HYSPLIT, automatically generates forecasts for the vertical and horizontal spread of volcanic ash up to 72 hours. These forecasts are initiated upon the receipt of a Volcanic Ash Advisory (VAA) from the Tokyo Volcanic Ash Advisory Center by the server at the Korea Meteorological Administration (KMA). This system equips KMA forecasters with diverse volcanic ash prediction information, complemented by the Unified Model (UM)-based HYSPLIT (UM-HYSPLIT) system. Extensive experiments have been conducted using KIM-HYSPLIT across 128 different volcanic scenarios, along with qualitative comparisons with UM-HYSPLIT. The results indicate that the ash direction predictions from KIM-HYSPLIT are consistent with those from UM-HYSPLIT. However, there are slight differences in the horizontal extent and movement speed of the volcanic ash. Additionally, quantitative verifications of the KIM-HYSPLIT forecasts have been performed, including threat score evaluations, based on recent eruption cases. On average, the KIMHYSPLIT forecasts for 6 and 12 hours show better quantitative alignment with the VAA forecasts compared to UM-HYSPLIT. Nevertheless, both models tend to predict a broader horizontal spread of the ash cloud than indicated in the VAA forecasts, particularly noticeable in the 6-hour forecast period.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.59-67
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• 2014

A numerical model and a controller of Active Front wheel Steer (AFS) system are designed in this study. The AFS model consists of four sub models, and the AFS controller uses sliding mode control and PID control methods. To test this model and controller an Integrated Dynamics Control with Steering (IDCS) system is also designed. The IDCS system integrates an AFS system and an ARS (Active Rear wheel Steering) system. The AFS controller and IDCS controller are compared under several driving and road conditions. An 8 degree of freedom vehicle model is also employed to test the controllers. The results show that the model of AFS system shows good kinematic steering assistance function. Steering ratio varies depends on vehicle velocity between 12 and 24. Kinematic stabilization function also shows good performance because yaw rate of AFS vehicle tracks the reference yaw rate. IDCS shows improved responses compared to AFS because body side slip angle is also reduced. This result also proves that AFS system shows satisfactory result when it is integrated with another chassis system. On a split-m road, two controllers forced the vehicle to proceed straight ahead.

• Journal of Korean Association for Spatial Structures
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• v.1 no.2 s.2
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• pp.59-66
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• 2001

This paper describes three modules for development of the Space Frame Integrated Design System(SFIDS). The Control Module is implemented to control the developed system. The Model Generation Module based on PATRAN user interface enables users to generate a complicated finite element model for space frame structures. The Optimum Design Module base on a branch of combinatorial optimization techniques which can realize the optimization of a structure having a large number of members designs optimum members of a space frame after evaluating analysis results. The Control Module and the Model Generation Module Is implemented by PATRAN Command Language(PCL) while C++ language is used in the Optimum Design Module. The core of the system is PATRAN database, in which the Model Generation Module creates information of a finite element model. Then, PATRAN creates Input files needed for the analysis program from the information of the finite element model in the database, and in turn, imports output results of analysis program to the database. Finally, the Optimum Design Module processes member grouping of a space frame based on the output results, and performs optimal member selection of a space frame. This process is repeated until the desired optimum structural members are obtained.

• Journal of the Korea Safety Management & Science
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• v.16 no.1
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• pp.239-250
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• 2014

The purpose of this research is to implement and develop the Economic Cost Driver Size(ECDS) extended model to determine the optimal cash driver size with measurement complexity cost and allocation fail cost. ECDS model can be used to seek both measurement accuracy and time efficiency of the Activity-Base Costing (ABC). The study also develops Activity Priority Number (APN) to evaluate the importance of nonvalue-added activities improvement and to determine the representative cost driver of value-added activities when applying ECDS model. APN consists of Severity Priority Number (SPN), Undetectablitiy Priority Number (UPN) and Occurrence Priority Number (OPN). APN can be obtained from lower-stream activity, current activity, upper-stream activity in terms of hierarchical dependency of SIPOC (Supplier, Input, Process, Output, and Customer). In order to seek both efficiency of invested capital and reduction of overhead cost, the paper proposes the integrated ABC and Economic Value Added (EVA) model using redesigned ABC-based statement of comprehensive income and EVA-based statement of financial position. For a better understanding of the proposed ABC-EVA integrated model, numerical examples are demonstrated in this paper. Cost drivers of ABC and capital drivers of EVA in the proposed model can be used to reduce activity overhead cost from ABC-based statement of comprehensive income and to lessen activity capital charge from EVA-based statement of financial position.

• Journal of Korea Spatial Information System Society
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• v.11 no.4
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• pp.19-27
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• 2009

Integrated Urban Spatial Information Management Platform (USIMP) is defined as an operational environment where technologies for intelligent management of urban facilities are made possible through the integration of diverse technologies such as sensors for ground and underground facilities, middleware technology, wired and wireless network, GIS-USN linkage. To make the integration of these diverse technology possible, it is imperative to have a sound reference model for the platform. This paper provides a standardized reference model for USIMP based on the RM-OPD(Reference Model for Open Distributed Processing) standard.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.6
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• pp.527-533
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• 2008

This paper proposes a product data model and associated process for CAE activities in context of integrated product development. The data model and process enable Product Lifecycle Management(PLM) systems to integrate currently separated CAE activities into the main product development process. The product view concept in the proposed product data model supports independent CAE activities including analysis of various alternatives based on shared product structures with design departments and seamless translation of the CAE result to design product views. The proposed model is validated through an implementation of a prototype PLM system that can integrate and synchronize CAE process with the company-wide product development process.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.1
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• pp.115-127
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• 2000

The purpose of this study is to propose the Design Object Model for implementation of an integrated structural design system for building structures. This study outlines the step-by-step development methodologies of the Design Object Model, which covers classification and modeling of the building design information. The Design Object Model has been efficiently developed through the proposed development methodologies. As a result, the Design Object Model has been proved to be efficient in design information management by representing the information from planning perspective, in recognition of structural member in space by the topology design object, and in representation of analysis s design information.

• Journal of Korean Society on Water Environment
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• v.27 no.5
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• pp.710-718
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• 2011

An integrated water quality management of reservoir and river would be required when the quality of downstream river water is affected by the discharge of upstream dam. In particular, for the control of downstream turbidity during flood events, the integrated modeling of reservoir and river is effective approach. This work was aimed to develop a laterally-averaged two-dimensional hydrodynamic and water quality model (CE-QUAL-W2), by which water quality can be predicted in the downstream of Yongdam dam in conjunction with the reservoir model, and to validate the model under two different hydrological conditions; wet year (2005) and drought year (2010). The model results clearly showed that the simulated data regarding water elevation and suspended solid (SS) concentration are well corresponded with the measured data. In addition, the variation of SS concentration as a function of time was effectively simulated along the river stations with the developed model. Consequently, the developed model can be effectively applied for the integrated water quality management of Yongdam dam and downstream river.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• ETRI Journal
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• v.46 no.1
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• pp.22-34
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• 2024

Exposure to varied noisy environments impairs the recognition performance of artificial intelligence-based speech recognition technologies. Degraded-performance services can be utilized as limited systems that assure good performance in certain environments, but impair the general quality of speech recognition services. This study introduces an audiovisual speech recognition (AVSR) model robust to various noise settings, mimicking human dialogue recognition elements. The model converts word embeddings and log-Mel spectrograms into feature vectors for audio recognition. A dense spatial-temporal convolutional neural network model extracts features from log-Mel spectrograms, transformed for visual-based recognition. This approach exhibits improved aural and visual recognition capabilities. We assess the signal-to-noise ratio in nine synthesized noise environments, with the proposed model exhibiting lower average error rates. The error rate for the AVSR model using a three-feature multi-fusion method is 1.711%, compared to the general 3.939% rate. This model is applicable in noise-affected environments owing to its enhanced stability and recognition rate.