• 한국CDE학회논문집
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• 제3권4호
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• pp.293-303
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• 1998

The objective of this study is to develop a PC level freeform surface modeling system which explicitly represents information of part geometry. Surface modeler uses nonuniform rational B-spline (NURBS) function with nonuniform knot vector for the flexible modeling work. The results of this study are as follows. 1) By implementation surface modeler through applying representation scheme proposed to represent free-form surface explicity, the technical foundation to develop free-from surface modeling system using parametric method. 2) Besides the role to model geometric shape of a surface, geometric modeler is developed to model arbitrary geometric shape. By doing this, the availability of the modeling system is improved. Geometric modeler can be utilized application fields such as collision test of tool and fixture, and tool path generation for NC machine tool.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 춘계학술대회
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• pp.426-431
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• 2004

Leaking water coolant into stator electrical insulation is a growing concern for the aging water-cooled generator since leaks in the generator water-cooled stator winding can affect machine availability and insulation life. But a domestic techniques of such field are insufficient and depend wholly on GE or TOSHIBA technique. Therefore this paper introduces measuring principle and developed measuring system, which has been used to detecting wet absorption. We accomplished the experiment with a stator promotion of virtue which is used in actual power plant. Also, Experimental method of generator stator winding, which is investigated into wet absorption test.

• 한국공작기계학회논문집
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• 제16권6호
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• pp.62-70
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• 2007

In this study, the natural flaws in welding parts are classified using the signal pattern classification method. The storage digital oscilloscope including FFT function and enveloped waveform generator is used and the signal pattern recognition procedure is made up the digital signal processing, feature extraction, feature selection and classifier design. It is composed with and discussed using the distance classifier that is based on euclidean distance the empirical Bayesian classifier. Feature extraction is performed using the class-mean scatter criteria. The signal pattern classification method is applied to the signal pattern recognition of natural flaws.

• 한국공작기계학회논문집
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• 제17권2호
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• pp.77-82
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• 2008

Rubbing-roller is used for manufacturing liquid crystal display, and static displacement of the rubbing-roller becomes bigger as length of the rubbing roller made of aluminum is getting longer. Therefore, material of the rubbing-roller is changed from aluminum to CFRP(Carbon Fiber Reinforced plastic). Recently thermal spraying is applied to manufacturing process of long rubbing-roller. The thermal spraying has disadvantages such as increment of manufacturing time and fraction defective caused by density of stainless steel particle. In this study, fitting process by drawing was suggested and FEM analysis with Tsai-Wu failure theory and fitting experiments are carried out to find adequate shrink allowance. The suggested shrink allowance gives proper adhesive force, and CFRP failure is not occurred. Furthermore, the fitting process is applied to long rubbing-roller and availability of the fitting process is studied by measurement of roundness, straightness and shear strength.

• 한국안전학회지
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• 제20권3호
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• pp.1-8
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• 2005

The industrial induction motor is widely used in the rotating electrical machine for the transmission of power. It is very reliable equipment, but it could lead to the loss of production and lift when failure occurs. Therefore, the failure data is acquired and analyzed by attaching an exclusive instrument to existing induction motor. However, these instruments could lead to side effects, increasing the production costs, because they are very expensive. The purpose of this study is the development of an induction motor bearing failure diagnosis system constructed using LabVIEW which can be supplied the kernelled function, process monitoring and current signature analysis. In addition, the availability and reasonability of the constructed system was examined for an induction motor with failure defects in outer raceway and ball bearing. From the results, it shows that failure diagnosis system constructed is useful for real-time monitoring with detection of bearing defects over the web.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1858-1872
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• 2023

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases. To improve application of SAR images, a management system for SAR images is proposed in this paper. The system provides trainable land cover classification module and display of SAR images on the map. Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images. The classifier is based on convolutional neural network structure. Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems. Thus, it is adopted to create each user's classifier. In our experiments, it is shown that the module works well with two different SAR datasets. With this system, SAR data and land cover classification results are managed and easily displayed.

• 한국정보통신학회논문지
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• 제20권9호
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• pp.1710-1716
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• 2016

네트워크 장비에서 동작하는 소프트웨어의 버그 수정이나 새로운 기능 추가를 위해서 소프트웨어를 업그레이드 할 필요가 있다. 하지만 서비스 중인 소프트웨어를 업그레이드하기 위해서는 네트워크 서비스를 종료한 후에 소프트웨어를 업그레이드해야만 하는 문제가 있다. 이러한 문제를 해결하기 위해서 서비스 중 소프트웨어 업그레이드(ISSU : In-Service Software Upgrade) 기술이 사용된다. ISSU는 네트워크 장비를 오프라인 시키거나 네트워크 서비스를 중단하지 않고 소프트웨어를 업그레이드하는 기술이다. 본 논문에서는 무중단 네트워킹 서비스를 제공하기 위해서 ISSU 기술을 네트워크 OS에 적용하는 방법을 제안하고 구현한다. 본 논문에서는 고가용성 기능을 가지고 있는 한국전자통신연구원에서 개발한 N2OS를 이용하였다. 또한 ISSU 기능이 정상적으로 동작함을 검증하기 위해서 가상 머신 기반의 시험 환경을 만들고 시험을 진행하였다.

• Safety and Health at Work
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• 제10권4호
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• Korean Chemical Engineering Research
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• 제48권6호
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• pp.717-724
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• 2010

액체의 화재 및 폭발위험을 나타내는 가장 중요한 물성의 하나인 인화점의 실험 데이터는 그 필요에도 불구하고 실제로 데이터를 확보하는 것이 가능하지 않은 경우가 많다. 이 연구에서는 DIPPR 801에서 얻은 893개 유기물의 인화점 실험데이터로부터 인화점을 예측하는 부분최소자승법(PLS) 및 support vector machine(SVM) 모델을 만들고 비교하였다. 분자를 구성하는 각 구성요소들이 분자의 물성에 일정한 기여를 한다는 가정을 이용하여 분자의 물성을 예측하는 방법인 그룹기여법을 이용하여 65개 작용기가 이 예측모델의 독립변수가 되었고 분자량의 로그값이 추가되었다. 두 모델에서 결정해야 할 매개변수는 교차검증에서 계산된 오차를 이용하여 결정되었는데, SVM모델은 그 매개변수가 많아 particle swarm optimization을 이용한 최적화를 이용하였다. 훈련데이터의 선택이 예측성능에 영향을 줄 수 있어 임의로 100개의 데이터 세트를 생성하여 테스트하였다. 전체 데이터에 대해 계산된 평균절대오차는 PLS가 13.86~14.55였고, SVM이 7.44~10.26여서 SVM이 PLS에 비해 매우 우수한 예측성능을 보였다.