• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.1383-1388
• /
• 2007

Accuracy and processing time are very important factors when the desired shape is fabricated with Selective Laser Sintering (SLS), one of Solid Freeform Fabrication (SFF) systems. In a conventional SLS process, laser spot size is fixed during laser exposing on the sliced figure. Therefore, it is difficult to accurately and rapidly fabricate the desired shape. In this paper, to deal with those problems an SFF system having ability of changing spot size is developed. The system provides high accuracy and optimal processing time. Specifically, a variable beam expander is employed to adjust spot size for different figures on a sliced shape. Finally, Design and performance estimation of the SFF system employing a variable beam expander are achieved and the mechanism will be addressed to measure the real spot size generated from the variable beam expander.

• Transactions of Materials Processing
• /
• v.13 no.3
• /
• pp.190-204
• /
• 2004

The objective of this study is to develop a highly accurate micro tool alignment system applicable to the micro machining technology. In a specific application such as micro hole punching, radial clearance between micro tools is order of a few micron. Under this micron scale tool clearance, accuracy of tool alignment is very important for ensuring hole quality. In the present study, a two-way image acquisition system was developed, which can produce overlapped image of both micro tools that face each other, and applied to the tool alignment in the micro punching. Also, to meet alignment accuracy of tools within $1\mu\textrm{m}$, the cross correlation image processing algorithm was employed. With this system, $25\mu\textrm{m}$ punching tools with $1\mu\textrm{m}$ radial clearance could be accurately aligned.

• Proceedings of the KIEE Conference
• /
• 2000.07a
• /
• pp.122-124
• /
• 2000

As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. Therefore this paper proposes a method of the modeling of protection systems and fault diagnosis in power systems using Fuzzy Petri Nets (FPN). The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method can cover online processing of real-time data from SCADA (Supervisory Control and Data Acquisition).

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.11
• /
• pp.126-137
• /
• 1997

This paper describes the algorithm which can estimate road following direction and deetect obstacle using a monocular vision system. This algorithm can estimate the course of vehicle using the vanishing point properties and detect obstacle by statistical method. The proposed algorithm is composed of four steps, which are lane prediction, lane extraction, road following parameter estimation and obstacle detection. It is designed for high processing speed and high accuracy. The former is achieved by a small area named sub-windown in lane existence area, the later is realized by using connected edge points of lane. We would like to present that the new mehod can detect obstacle using the simple statistical method. The paracticalities of the processing speed, the accuracy of the algorithm and proposing obstacle detection method, have been justified through the experiment applied VTR image of the real road to the algorithm.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2000.11a
• /
• pp.697-704
• /
• 2000

This study presents methodology and tools for remote measurement of the orientation of the rock parting surfaces. Two close circuit video camera capture the pictures of a rock excavation surface while a laser beam scans the surface. Positions of the laser beams in the two digital images are recognized by image processing. Using the stereoscopic concept, three dimensional coordinations of the rock surface and the orientation of the rock parting planes are calculated. Point, cross and line type laser beams are tested for better image processing results and measurement accuracy of the coordinates. According to a simple accuracy test, cross beam show better results than the point beam. However, line beam show more promising results for the measurement of the rock parting surfaces.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.11 no.5
• /
• pp.65-72
• /
• 2002

In this study, actuator, sensor, guide, power transmission element and control method are considered for ultra-precision positioning apparatus. Through previous process, single plane X-Y stage with ultra-precision positioning is manufactured. Global stage for the purpose of materialization with robust system, is combined by using AC servo motor and ball screw and rolling guide. And ultra-precision positioning system is developed by micro stage with elastic hinge type and piezo element. global servo and micro servo for the purpose of materialization positioning accuracy with nm(nanometer) are controlled simultaneously by using incremental encoder and laser interferometer as displacement measurement sensor. Through previous process, ultra-precision positioning system(100mm stroke and $\pm$ l0nm positioning accuracy) with single plane X-Y stage are materialized.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.5
• /
• pp.56-65
• /
• 1994

We have carried out a research on automatic measurement of gage blocks below 250mm and then improved the accuracy of the system, and reduced the measurement time using image processing technique and computer interface. The accuracy of the system is (230+10330 $L^{2}$)$^{2}$ nm (L:m) with confidence level of 95%. We participated APMP gage block intercomparision-1993/1994. The 1st round intercomparision showed that the measured values of all 5 gage blocks were within 21 nm from average.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.697-698
• /
• 2016

In this paper, we propose a novel approach to improve the accuracy for multiple low-cost drones in indoor environment. When the drones are flying, we employ sensors for checking their position in real-time. If the drones move out of their correct positions, the corresponding instructions are sent immediately. In another thread, we calibrate direction of the drones by checking yaw value. The adjustment is repeated until the drones locate at right position and direction.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.10a
• /
• pp.583-586
• /
• 2016

단백질과 RNA의 상호작용 데이터가 대량으로 늘어남에 따라, 단백질과 RNA의 결합부위를 예측하는 계산학적인 방법들이 많이 개발되고 있다. 하지만, 많은 계산학적인 방법들은 단백질에서 단백질과 RNA 결합부위를 예측한다는 한계점이 있었다. 본 논문에서는 RNA와 단백질의 서열정보를 모두 사용하여, 단백질과 결합하는 RNA 결합부위를 예측하는 기법과 그 결과를 논한다. WEKA random forest(http://www.cs.waikato.ac.nz/ml/weka/)를 이용하여 예측 모델을 개발하였고, RNA 서열의 서열 프로파일, 서열 composition, 결합 상대방의 단백질의 특성 등을 특정으로 표현하였다. Random forest 기법을 사용한 cross validation의 결과로서 1:1 모델에서 제일 높은 성능인 92.4% sensitivity, 92.0% specificity, 92.2% accuracy를 보였고, independent test에서는 72.5% sensitivity, 90.0% specificity, 2.1% accuracy를 보였다.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.5
• /
• pp.1419-1425
• /
• 2000

When we predict of software reliability, we can't know the testing stopping time and how many faults be residues in software the (the maximum value of data) during these software testing process, therefore we assume the maximum value and the training result can be inaccuracy. In this paper, we present neural network approach for software reliability prediction with unnormalized (actual or original collected) data. This approach is not consider the maximum value of data and possible use the network without normalizing but the predictive accuracy is better. Also, the unnormalized method shows better predictive accuracy than the normalized method given by maximum value. Therefore, we can make the best use of this model in software reliability prediction using unnormalized data.