• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2011.04a
• /
• pp.652-655
• /
• 2011

건축, 구조, 기계, 전기, 토목, 조경 등 IT와 관련된 다양한 산업 분야에서는 각 산업의 기획, 생산, 운영등 단계별로 다양한 주제를 가지고 논의 및 시뮬레이션을 통하여 각 업체별로 의사를 개진해 나간다. 그러나 업종별 공정에 맞는 커뮤니케이션이 원활히 이루어져 나가기는 힘들다. 이러한 이유 중 가장 큰 부분은 데이터들이 다양한 뷰어(Viewer)들을 통하여 2차원적으로 보는 부분이 큰 비중을 차지하는 것에 비해 실제 결과물은 3차원적으로 다루어지고 있기 때문에 과정과 결과간의 격차로 인하여 벌어지는 문제가 크다. 각각 다양한 분야에서 생산되는 데이터들의 불확실성, 표현의 애매모호함으로 인하여, 이전 공정 단계에서 받은 데이터들을 현재의 공정 단계에 맞는 데이터로 변환되는 과정이 생긴다. 불필요한 과정들이 반복되는 사이 모든 공정이 지연되고, 많은 예산이 소모되는 등 많은 문제들이 발생되고 있다. 따라서 본 연구에서는 다채널을 활용한 VR 시뮬레이터로 몰입감을 높이고, 실제와 유사한 실감형 시뮬레이션으로 엔지니어링 VR 시뮬레이션이 가능함을 알 수 있었다. 향후에는 사용자의 용도와 목적에 따라 다양한 분야에서 활용의 범위가 넓을 것으로 기대되며, 많은 공간과 비용이 드는 현재의 다채널 시뮬레이터 형태를 보다 공간과 비용의 소모가 적고, 이동성을 갖춘 이동형 다채널 VR 시뮬레이터에 관한 연구가 필요하다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.11a
• /
• pp.373-377
• /
• 2002

Unlike desktop systems, embedded systems usually meet a strict restriction on using memory. It is required to allocate several class files on memory to run a Java program. A Java class file contains such data including a constant pool, class overview, fields information, and methods information. Some of them are used merely for a debugging purpose, others for a program execution. This paper analyzes the internal structure, or format of the class files used for embedded Java systems. We also investigate how much memory will be necessary for each part of the class files when the files are allocated in memory. The experiment was performed on the simpleRTJ, an open-source commercial embedded Java system.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.1
• /
• pp.27-34
• /
• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• Journal of the Korea Convergence Society
• /
• v.12 no.1
• /
• pp.185-191
• /
• 2021

A powder metallurgy process with granules is the manufacturing technology that can achieve higher-density sintered parts than conventional powder metallurgy processes. However, there is a disadvantage in that the production cost increases significantly due to the additional granulation step. High granule productivity must be guaranteed for affordable material costs in this manufacturing technology. This paper performed a series of scattering, collision, and adhesion simulations of agglomerated powders to investigate the characteristics of granulation process affecting the manufacturing yield rate. The results of this simulation-based convergence study can contribute to improving productivity in the metal powder spray granulation process.

• Journal of Drive and Control
• /
• v.18 no.4
• /
• pp.9-18
• /
• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• Journal of the Korean Society for Precision Engineering
• /
• v.10 no.3
• /
• pp.107-116
• /
• 1993

In the design and operation of robitic arm with flexible links, the equation of motion are required to exactly model the interaction between rigid body motion and elastic motion and to be formulated efficientlyl. In this paper, the flexible link is represented by applying the D-H rigid link representation method to measure the elestic deformation. And the equations of motion of robotic arm, which are configured by the generalized coordinates of elastic and rigid degrees of freedom, are formulated from the principle of virtual power. Dynamic characteristics due to elastic deformation of each link are obtained by using F. E. M to model complex shaped link acurately and by eliminating elastic modes of higher order that do not largely affect motion to reduce the number of elastic degrees of freedom. Also presented is the result of simulation of flexible robotic arms whose joints are controlled by direct or PD control.

• Journal of Drive and Control
• /
• v.21 no.2
• /
• pp.15-22
• /
• 2024

The objective of this study was to examine whether a gerotor pump used in a transmission could be converted into an electric vehicle thermal management system pump using a virtual design environment. To achieve this objective, we first built an environment that could analyze the performance of a gerotor pump in heat transfer fluid. Flow rate, pressure, and volumetric efficiency were then analyzed when using heat transfer fluid in a gerotor pump. Finally, how large the displacement volume of the pump should be designed when using a heat transfer fluid other than oil was determined. Based on results of this study, it is expected that gerotor pumps will be applied to new business fields such as electric vehicle cooling systems.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.37 no.1
• /
• pp.47-54
• /
• 2013

This study investigates the crack growth behavior due to primary water stress corrosion cracking (PWSCC) in the dissimilar metal butt weld of a reactor piping using Alloy 82/182. First, detailed finite element stress analyses were performed to predict the stress distribution of the dissimilar metal butt weld in which the hydrostatic and the normal operating loads as well as the weld residual stresses were considered to evaluate the stress redistribution due to mechanical loadings. Based on the stress distributions along the wall thickness of the dissimilar metal butt weld, the crack growth behavior of the postulated axial and circumferential cracks were predicted, from which the crack growth diagram due to PWSCC was proposed. The present results can be applied to predict the crack growth rate in the dissimilar metal butt weld of reactor piping due to PWSCC.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.1
• /
• pp.7-15
• /
• 2023

Recently, as interest in air quality in vehicles increases, the use of fine dust detection sensors for air quality measurement is becoming common. An axial-flow fan is inserted in the fine dust sensor installed in the air conditioning system in the vehicle to prevent dust from sinking directly on the sensor. When the sensor operates, the flow noise caused by the rotation of the axial-flow fan acts as a major noise source of the fine dust sensor. flow noise is recognized as one of the product competitiveness of fine dust sensors. In this study, the noise was gradually reduced at the same flow rate by improving the flow performance of the small axial flow fan. First, a virtual fan performance tester consisting of about 20 million grids was developed to analyze the aerodynamic performance of the target small axial-flow fan. In addition, the flow field was simulated by using compressible Large Eddy Simulation for direct computation of flow noise as well as high-accurate prediction of flow rate. The validity of numerical method are confirmed through the comparison of predicted results with experimental ones. After the effects of pitch angle on flow performance were analyzed using the verified numerical method, the pitch angle was determined to maximize the flow rate. It was found that the flow rate was increased by 8.1 % and noise was reduced by 0.8 dBA when the axial-flow fan with the optimum pitch angle was used.

• Korean Chemical Engineering Research
• /
• v.61 no.3
• /
• pp.388-393
• /
• 2023

The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.