• 한국안전학회지
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• 제26권4호
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• pp.34-40
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• 2011

This paper is purposed to develop an leakage current detecting system(LCDS) which can prevent electrical fires on breaker capacity expansion as well as ruptures of XL(Extra long) pipelines and power failure by operation of ELB(Earth leakage breaker) at auto water machine in winter. In order to develop LCDS, this paper studied field state investigation, field state experiment, development of leakage alarm system and verification experiments. Field states investigation at livestock companies(10 companies) in cheong-won location to deduce the problems of auto water machine is analyzed. The field state experiment is conducted at B livestock company in cheong won location. The field state experiment method is measured with leakage current when ELB tripped by environment factor(fine, cloudy, and rainy day). The LCDS is developed as MCU(Micro Control Unit) part applied leakage current values at B livestock company. Verification experiments for the leakage current detecting system were conducted by two methods of current supply and field test. Results show that LCDS suggested in this paper are valuable and usable in auto water machine based on environment factor, which will prevent severe damage to human beings and properties and reduce the electrical fires in livestock.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2002년도 금형가공 심포지엄
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• pp.3-10
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• 2002

In this study on machine measuring system based on solid feature was developed. This system was applied with injection mold using 3 dimensional solid modeler for verification. Developed program include pre-processor, main processor, and post processor. In pre-processor there are functions which check intersection, simulate motion of probe and calculate measuring time. Main processor generates measuring path and output NC code in Unigraphics. In post-processor functions that include evaluation of undercut or overcut and display of measuring procedure are offered. In addition analysis module for quality control of measured data on manufactured product was developed with geometric and dimensional tolerance concept. As the result developed program could get stability of system, precision of product, rapidity and cost down of manufacturing process compared with before measuring process.

• ETRI Journal
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• 제25권5호
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• pp.401-406
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• 2003

A biometric system determines the identity of a person by measuring physical features that can distinguish that person from others. Since biometric features have many variations and can be easily corrupted by noises and deformations, it is necessary to apply machine learning techniques to treat the data. When applying the conventional machine learning methods in designing a specific biometric system, however, one first runs into the difficulty of collecting sufficient data for each person to be registered to the system. In addition, there can be an almost infinite number of variations of non-registered data. Therefore, it is difficult to analyze and predict the distributional properties of real data that are essential for the system to deal with in practical applications. These difficulties require a new framework of identification and verification that is appropriate and efficient for the specific situations of biometric systems. As a preliminary solution, this paper proposes a simple but theoretically well-defined method based on a statistical test theory. Our computational experiments on real-world data show that the proposed method has potential for coping with the actual difficulties in biometrics.

• ETRI Journal
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• 제36권5호
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• pp.696-703
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• 2014

In this paper, we present a walking guidance system for the visually impaired for use at subway stations. This system, which is based on environmental knowledge, automatically detects and recognizes both exit numbers and arrow signs from natural outdoor scenes. The visually impaired can, therefore, utilize the system to find their own way (for example, using exit numbers and the directions provided) through a subway station. The proposed walking guidance system consists mainly of three stages: (a) sign detection using the MCT-based AdaBoost technique, (b) sign recognition using support vector machines and hidden Markov models, and (c) three verification techniques to discriminate between signs and non-signs. The experimental results indicate that our sign recognition system has a high performance with a detection rate of 98%, a recognition rate of 99.5%, and a false-positive error rate of 0.152.

• 한국생산제조학회지
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• 제6권1호
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• pp.51-58
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• 1997

The verification of the STL file is essential to build the confident parts using a RP machine, because the STL file obtained from the CAD software has many errors-the orientation of triangle does not coincide with adjacent triangles or some triangles are omitted, overlpped and so forth. Especially, the STL file translated from the surface model has more errors than those translated from the solid model. In this study, all possible errors were classified with the most general from and the causes of errors were analyzed to verify and correct errors. Using the triangle based non-manifold geometric modeling, these errors were corrected. Especially, this study took the notice of the problem about the intersected triangles and non-manifold properties overlooked in the previous studies. But this study has a penalty on computing time of $O(n^2)$.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.

• 문화기술의 융합
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• 제9권5호
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• pp.843-847
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• 2023

본 논문은 현대 기술이 발전함에 따라 다양한 화학 제품 및 인화성 물질이 광범위하게 사용되면서 각종 산업재해 및 농지와 대형 산불로 이어질 수 있는 화재 예방을 위해 효율적인 화재 식별을 탐지하는 모델을 제시한다. 본 논문에서는 영상을 활용하여 효율이 높고 빠른 시간안에 화재 연기를 검출할 수 있는 알고리즘을 제시하며, SVM(Support Vector Machine)과 K 접힘 교차 검증 기술을 기반으로 한 알고리즘을 제시한다. 영상을 분석하여 화재 및 연기 검출 알고리즘은 기존의 알고리즘에 비해 상대적으로 검출 성능이 우수하며, 본 논문에서 검출하는 화재 및 연기의 특징 분석이 안정적이고 효율적으로 분석되어 향후 화재 위험에 노출될 수 있는 다양한 분야에서 활용될 것으로 판단된다.

• 제어로봇시스템학회논문지
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• 제5권1호
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• pp.115-121
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• 1999

Advanced digital computer technology enables the computer-based controllers to replace the traditional analog controllers used in factory automations. This replacement, however, brings up the side effects caused by the quantization error and non-real-time execution of control software. This paper describes the structure of real-time simulator and controller that can be used for design and verification of real-time digital controllers. The virtual machine concept adopted by the proposed real-time simulator makes the proposed simulator be independent from the specific hardware platforms. The proposed system can also be used in the loosely coupled distributed environments connected through local area network using real-time message passing algorithm and virtual data table based on the shared memory mechanism.

• Nuclear Engineering and Technology
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• 제44권6호
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• pp.663-672
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• 2012

As software based digital I&C (Instrumentation and Control) systems are used more prevalently in nuclear plants, enhancement of software dependability has become an important issue in the area of nuclear I&C systems. Critical attributes of software dependability are safety and reliability. These attributes are tightly related to software failures caused by faults. Software testing and V&V (Verification and Validation) activities are hence important for enhancing software dependability. If the risky modules of safety-critical software can be predicted, it will be possible to focus on testing and V&V activities more efficiently and effectively. It should also make it possible to better allocate resources for regulation activities. We propose a prediction technique to estimate risky software modules by adopting machine learning models based on software complexity metrics. An empirical study with various machine learning algorithms was executed for comparing the prediction performance. Experimental results show SVMs (Support Vector Machines) perform as well or better than the other methods.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.26-30
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• 2020

In this paper, we studied the problem if the experiment number occurring in order to identify defect in scaffold. We need to change each of the 5 print factor to predict defect when printing disk type scaffold using FDM 3d printer. So then the number of scaffold print will be more than 100,000 times. This experiment number is difficult to perform in the field. In order to solve this problem, we have produced a prediction model based on machine learning multiple linear regression using print conditions and defect scaffold data for print conditions. The prediction model produced was verified through experiments. The verification confirmed that the error was less than 0.5 %. We have confirmed that satisfied within the target margin of error 5 %.