• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.683-688
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• 2011

Objective: The aim of this study is to develop a distributed representative human model(DRHM) generation and analysis system. Background: DRHMs are used for a product with multiple-size categories such as clothing and shoes. It is not easy for a product designer to explore an optimal sizing system by applying various distributed methods because of their complexity and time demand. Method: Studies related to DRHM generation were reviewed and the RHM generation interfaces of three digital human model simulation systems(Jack$^{(R)}$, RAMSIS$^{(R)}$, and CATIA Human$^{(R)}$) were reviewed. Results: DRHM generation steps are implemented by providing sophisticated interfaces which offer various statistical techniques and visualization methods with ease. Conclusion: The DRHM system can analyze the multivariate accommodation percentage of a sizing system, provide body sizes of generated DRHMs, and visualize generated grids and DRHMs. Application: The DRHM generation and analysis system can be of great use to determine an optimal sizing system for a multiple-size product by comparing various sizing system candidates.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1601-1604
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• 2003

This paper describes a split operation for generation of core and cavity plates of an injection mold for a pseudo-solid model of a plastic part. Here, a pseudo-solid model means a sheet model that looks like a solid model. but whose boundary is not closed. When a solid model created in a different CAD system is imported through standard data exchange format, a pseudo-solid model is created in most cases as tolerance or some other problems make sewing operation failed. As most existing mold design system based on solid modeling kernels require a complete part solid model, mold designers have to do time-consuming healing operations to convert a pseudo-solid to solid. The essential capability of mold design system is the split operation for generation of core and cavity plates. Thus. we developed a split operation for pseudo-solid part model to eliminate or reduce healing preprocessing for mold design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4568-4584
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• 2016

Steganography based on text generation has become a hot research topic in recent years. However, current text-generation methods which generate texts of normal style have either semantic or syntactic flaws. Note that texts of special genre, such as poem, have much simpler language model, less grammar rules, and lower demand for naturalness. Motivated by this observation, in this paper, we propose a text steganography that utilizes Markov chain model to generate Ci-poetry, a classic Chinese poem style. Since all Ci poems have fixed tone patterns, the generation process is to select proper words based on a chosen tone pattern. Markov chain model can obtain a state transfer matrix which simulates the language model of Ci-poetry by learning from a given corpus. To begin with an initial word, we can hide secret message when we use the state transfer matrix to choose a next word, and iterating until the end of the whole Ci poem. Extensive experiments are conducted and both machine and human evaluation results show that our method can generate Ci-poetry with higher naturalness than former researches and achieve competitive embedding rate.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.848-853
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• 2005

3D geometric modeling of an object of interest has been intensively investigated in many fields including CAD/CAM and computer graphics. Traditionally, CAD and geometric modeling tools are widely used to create geometric models that have nearly the same shape of 3D real objects or satisfy designers intent. Recently, with the help of the reverse engineering (RE) technology, we can easily acquire 3D point data from the objects and create 3D geometric models that perfectly fit the scanned data more easily and fast. In this paper, we present 3D geometric model generation based on a stereo vision system (SVS) using random pattern projection. A triangular mesh is considered as the resulting geometric model. In order to obtain reasonable results with the SVS-based geometric model generation, we deal with many steps including camera calibration, stereo matching, scanning from multiple views, noise handling, registration, and triangular mesh generation. To acquire reliable stere matching, we project random patterns onto the object. With experiments using various random patterns, we propose several tips helpful for the quality of the results. Some examples are given to show their usefulness.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.95-105
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• 2000

The Purpose of this study is to present a new Trip Generation Model using Backpropagation Neural Networks. For this purpose, it is compared the performance between existing linear/nonlinear Regression models and a new TriP Generation model using Neural Networks. The study was performed according to the below. First, it is analyzed the limits of conventional Regression models, next Proved the superiority of Neural Networks model in theoretical and empirical aspects, and lastly Presented a new approach of Trip Generation methodology. The results show that Backpropagation Neural Networks model is predominant in estimation and Prediction comparable to Regression analysis. Such results mean the possibility of Neural Networks\` application in Trip Generation modeling. Specially under the circumstances of the chancing transportation situations and unstable transportation on vironments, its application in transportation fields will be extended.

• Journal of Aerospace System Engineering
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• v.17 no.2
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• pp.9-15
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• 2023

The task of generating a reduced thermal model of a satellite must be performed at least once in a satellite project to shorten the time of orbital thermal analysis and perform thermal analysis coupled to a launch vehicle. Although there are various methods for generating a reduced thermal model, an intuitive and convenient iso-thermal mesh generation method is used the most widely in practice. However, there is still a lack of research on automation of the isothermal mesh generation method. In this paper, we proposed an automated generation method of satellite reduced thermo-mathematical model based on the isothermal mesh generation method considering temperature tolerance and fixed nodes. The proposed method was validated using three different temperature tolerance cases. The average temperature difference satisfied the guidelines of ECSS.

• 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.