• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.210-215
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• 1998

To design a space frame structure by the conventional method is not easy in practical sense since it is generally a three-dimensional complicated form, and stability and nonlinear problems are not easily checked in the design process. This paper describes two modules, the Model Generator which is based on PATRAN user interface that enables users to generate a complicated finite element model; the Optimum Design Module which analyzes output results of analysis program, and designs members of a space frame. The Model Generator is based on PCL while C++ language is used in the Optimum Design Module. Structural analysis is performed by using ABAQUS. All of these modules constitute Space Frame Integrated Design System. The Core of the system is PATRAN database, in which the Model Generator 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.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1511-1514
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• 2002

This paper presents content adaptive image watermark embedding using stochastic visual model based on multiwavelet transform. To embedding watermark, the original image is decomposed into 4 levels using a discrete multiwavelet transform, then a watermark is embedded into the JND(just noticeable differences) of the image each subband. The perceptual model is applied with a stochastic approach fer watermark embedding. This is based on the computation of a NVF(noise visibility function) that have local image properties. The perceptual model with content adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the JND. This method uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The experiment results of simulation of the proposed watermark embedding method using stochastic visual model based on multiwavelet transform techniques was found to be excellent invisibility and robustness.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1038-1046
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• 2014

Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.

• Steel and Composite Structures
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• v.12 no.1
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• pp.53-72
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• 2012

In this study, the physically-based failure models for matrix and fibers in compression and tension loading are introduced. For the 3D stress based fiber kinking model a modification is proposed for calculation of the fiber misalignment angle. All of these models are implemented into the finite element code by using the advantage of damage variable and the numerical results are discussed. To investigate the matrix failure model, purely in-plane transverse compression experiments are carried out on the specimens made by Glass/Epoxy to obtain the fracture surface angle and then a comparison is made with the calculated numerical results. Furthermore, shear failure of $({\pm}45)_s$ model is investigated and the obtained numerical results are discussed and compared with available experimental results. Some experiments are also carried out on the woven laminated composites to investigate the fracture pattern in the matrix failure mode and shown that the presented matrix failure model can be used for the woven composites. Finally, the obtained numerical results for stress based fiber kinking model and improved ones (strain based model) are discussed and compared with each other and with the available results. The results show that these models can predict the kink band angle approximately.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.133-140
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• 1999

In this paper, we propose a method of image-based three-dimensional modeling for realistic facial expression. In the proposed method, real human facial images are used to deform a generic three-dimensional mesh model and the deformed model is animated to generate facial expression animation. First, we take several pictures of the same person from several view angles. Then we project a three-dimensional face model onto the plane of each facial image and match the projected model with each image. The results are combined to generate a deformed three-dimensional model. We use the feature-based image metamorphosis to match the projected models with images. We then create a synthetic image from the two-dimensional images of a specific person's face. This synthetic image is texture-mapped to the cylindrical projection of the three-dimensional model. We also propose a muscle-based animation technique to generate realistic facial expression animations. This method facilitates the control of the animation. lastly, we show the animation results of the six represenative facial expressions.

• Journal of Korea Water Resources Association
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• v.31 no.3
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• pp.303-308
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• 1998

A grid-based KInematic were STOrm Runoff Model (KIMSTORM) with predicts temporal and spatial distributions of saturalted orerland flow, subsurface flow and stream flow in a watershed was developed. The model adopts the single overland flowpath algorithm and simulates surface and/or subsurface water depth at each grid element by using grid-based water balance of hydrologic components. The model which is programmed by C-language uses ASCII-formatted map data supported by the irregular gridded map of the GRASS(Geographic Resources Analysis Support System) GIS and generates the spatial distribution maps of discharge, flow depth and soil moisture within the watershed.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.25-34
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• 2021

Availability is one of the important factor for developing weapon system, because it indicates the mission capability and sustainable life cycle management of weapon system. Recently, as weapon system becomes more advanced and more complex, availability estimation becomes more important to reduce the life cycle cost of weapon system. Modeling and simulation(M&S) is useful method to describe the availability of complex weapon system applying operational environment and maintenance plan. Especially agent based model(ABM) has the strength to describe interactions between agents and environments in complex system. Therefore, this paper presents the availability estimation of weapon system using agent based model. The sample data of part list and reliability analysis is applied to build availability estimation model. User agent and mechanic agent are developed to illustrate the behavior of operation and maintenance using formal specification. Storage reliability is applied to describe failure of each parts. The experimental result shows that this model is quite useful to estimate availability of weapon system. This model may estimate more reasonable availability, if full scale data of weapon system and real field data of operation is provided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.