• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.112-119
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• 2003

This paper proposes a model of SP(silent period) generation in masseter muscle by means of computer simulation. The model is based on the anatomical and physiological properties of trigeminal nervous system. In determining the SP generation pathway, evoked SPs of masseter muscle after mechanical stimulation to the chin are divided into normal and abnormal group. Normal SP is produced by the activation of mechanoreceptors in periodontal ligament. The activation of nociceptors contributes to the latter part of normal SP, abnormal extended SP is produced. As a result, the EMG signal generated by a proposed SP generation model is similar to both real EMG signal including normal SP and abnormal extended SP with TMJ patients. The result of this study have shown differences of SP generation mechanism between subjects both with and without TMJ dysfunction.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.69-76
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• 2004

This paper presents a prediction model for the aerosol generation of cutting fluid in turning process. Experimental studies have been carried out in order to identify the characteristics of aerosol generation in non-cutting and cutting cases. The indices of aerosol generation was mass concentration comparable to number generation, which is generally used fur environment criterion. Based on the experimental data, empirical model for predicting aerosol mass concentration of cutting fluid could be obtained by a statistical analysis. This relation shows good agreement with experimental data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.463-478
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• 2024

The limitations of previous research on improving security vulnerabilities in power generation control systems were analyzed, and as an alternative, a cyber security information sharing model is proposed. Understanding the operational characteristics of power generation control systems, we examined the concepts and policies of cyber security information sharing, analyzing practical implementation cases. We presented effective policies and technological approaches that can be applied to power generation control systems, and their efficacy was verified through a model evaluation to validate their impact.

• Korean Journal of Computational Design and Engineering
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• v.8 no.2
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• pp.122-132
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• 2003

STEP AP218 has a standard schema to represent the structural model of a midship section. While it helps to exchange ship structural models among heterogeneous automation systems, most shipyards and classification societies still exchange information using 2D paper drawings. We propose a feature parameter input method to generate a 3D STEP model of a ship structure from 2D drawings. We have analyzed the ship structure information contained in 2D drawings and have defined a data model to express the contents of the drawing. We also developed a QUI for the feature parameter input. To translate 2D information extracted from the drawing into a STEP AP2l8 model, we have developed a shape generation library, and generated the 3D ship model through this library. The generated 3D STEP model of a ship structure can be used to exchange information between design departments in a shipyard as well as between classification societies and shipyards.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of Hydrogen and New Energy
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• v.21 no.5
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• pp.425-434
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• 2010

Integrated gasification combined cycle (IGCC) is an efficient and environment-friendly power generation system which is capable of burning low-ranked coals and other renewable resources such as biofuels, petcokes and residues. In this study some process modeling on a conceptual entrained flow gasifier was conducted using the ASPEN Plus process simulator. This model is composed of three major steps; initial coal pyrolysis, combustion of volatile components, and gasification of char particles. One of the purposes of this study is to develop an effective and versatile simulation model applicable to numerous configurations of coal gasification systems. Our model does not depend on the hypothesis of chemical equilibrium as it can trace the exact reaction kinetics and incorporate the residence time calculation of solid particles in the reactors. Comparisons with previously reported models and experimental results also showed that the predictions by our model were pretty reasonable in estimating the products and the conditions of gasification processes. Verification of the accuracy of our model was mainly based upon how closely it predicts the syngas composition in the gasifier outlet. Lastly the effects of change oxygen are studied by sensitivity analysis using the developed model.

• Proceedings of the Korean System Dynamics Society
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• 2003.08a
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• pp.49-81
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• 2003

It is one of the important problems how to maintain the optimal electric power generation mix. The Objective of this study is development of a computer model which can be used to forecast the investment of power generation unit by the plant owners after restructuring the electricity industry. The impacts of the various government policies can be analyzed using the computer model, thus the government can formulate effective policies for achieving the desired electric power generation mix.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.399-406
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• 2012

The objective of this research is to develop greenhouse gas generation models and estimation method of their parameters for solid waste landfills. Two models obtained by differentiating the Modified Gompertz and Logistic models were employed to evaluate two parameters of a first-order decay model, methane generation potential ($L_0$) and methane generation rate constant (k). The parameters were determined by the statistical comparison of predicted gas generation rate data using the two models and actual landfill gas collection data. The values of r-square obtained from regression analysis between two data showed that one model by differentiating the Modified Gompetz was 0.92 and the other model by differentiating the Logistic was 0.94. From this result, the estimation methods showed that $L_0$ and k values can be determined by regression analysis if landfill gas collection data are available. Also, new models based on two models obtained by differentiating the Modified Gompertz and Logistic models were developed to predict greenhouse gas generation from solid waste landfills that actual landfill generation data could not be available. They showed better prediction than LandGEM model. Frequency distribution of the ratio of Qcs (LFG collection system) to Q (prediction value) was used to evaluate the accuracy of the models. The new models showed higher accuracy than LandGEM model. Thus, it is concluded that the models developed in this research are suitable for the prediction of greenhouse gas generation from solid waste landfills.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.337-344
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• 2001

As cost invested in developing the specified technology is increasing, investors are paying more attention to cost to benefit analysis (CBA). One of the basic elements of CBA for new technological development is the diffusion pattern of demand of such technology. Many studies of technology evaluation have adopted a single generation model to simulate the diffusion pattern of demand. This approach, however, considers the diffusion of the new technology itself, not taking into account a newer generation that can replace the one just invented. In this paper, we show how a multi-generation technology diffusion model can be applied for more accurate CBA for information technology. Monte Carlo simulation is performed to find influential factors on the CBA of a Cybernetic Building System.