• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.8
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• pp.98-103
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• 2015

Power plant is that very high reliability when industrial and economic impact on the overall electric power system is required, it is essential to improve the reliability, especially the fault prediction diagnosis. Since an accident caused by the partial discharge in the power plant is above state has a faster response characteristic than the other indications in the case of any, the partial discharge generated in the power plant immediately detect the deterioration of insulation due to the accident of the power plant and the non-drawn It should prevent or reduce. Partial Discharge Measuring Systems for UHV SF6 Gas Insulated Switchgear and power transformer on site installed has some probability of abnormal recognition in case of non-flexible deal with on site noise. Many methode to eliminate these kinds of noises, UHF Detection System is chosen as purchase description in Korea, but this system having a bandwidth between 500MHz 1.5GHz wide band. Initial install periods(about 20 years ago), this band had no strong signal source, but in these days this wide band have strong signals, such as LTE. So, module described in this paper is designed as simultaneously use with wide and narrow band for solve this noise problem, and introduce this system.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.101-105
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• 2018

In this paper, the kernel regression model is applied for the case study of gas turbine abnormal state analysis. In addition to vibration analysis at the remote site, the kernel regression model technique can is useful for analyzing abnormal state of rotor vibration signals of gas turbine in power plant. In monitoring based on data-driven techniques correlated measurements, the fault free training data of shaft vibration obtained during normal operations of gas turbine are used to develop a empirical model based on auto-associative kernel regression. This data-driven model can be used to predict virtual measurements, which are compared with real-time data, generating residuals. Any faults in the system may cause statistically abnormal changes in these residuals and could be detected. As the result, the kernel regression model provides information that can distinguish anomalies such as sensor failure in a shaft vibration signal.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.505-508
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• 2021

반도체 회로의 미세화로 단위 공정이 증가하면 TAT(turn-around time) 증가에 따른 제조 비용이 늘어난다. 반도체 공정 중 포토 공정은 마스크의 회로를 웨이퍼에 전사하는 공정으로 전사를 담당하는 노광장비의 성능에 의해 회로의 정확성이 결정된다. 이런 정확성을 검증하는 계측공정은 회로의 미세화가 진행될수록 필요성은 증가하나 TAT 증가의 주된 요인으로 최근 기계학습을 사용한 다양한 예측 모형들의 개발로 계측 결과를 예측하는 실험들이 진행되고 있다. 본 논문은 노광장비 센서들의 이상값을 감지하여 분류 후 계측공정을 진행하는 LFDC(Lithography Fault Detection and Classification) 시스템의 문제인 분류 성능이 떨어지는 것을 해결하기 위해 XGBoost를 사용하여 계측공정을 진행하지 않고 노광장비 센서의 이상값을 학습된 학습기를 통해 분류하여 포토 공정을 재진행하거나 다음 공정을 진행하는 방법을 실험하였다. 실험에서 사용된 계측 결과 예측 모형은 89%의 정확도를 확보하였고 반도체 데이터 특성인 심각한 불균형의 데이터에 대해서도 같은 정확도를 얻었다. 이런 결과는 노광장비 센서들의 이상값에 대해 89%는 정상으로 판단하였고 정상으로 판단한 웨이퍼를 실제 계측 시 예측과 같은 결과를 얻었다. 계측 결과 예측 모형을 사용하면 실제 계측을 진행하지 않고 노광장비 센서들의 이상값에 대한 판정을 할 수 있어 TAT 단축으로 제조 비용감소, 계측 장비 부하 감소 및 효율 향상을 할 수 있다. 하지만 본 논문에서는 90%의 성능을 보이는 계측 결과 예측 모형으로 여전히 10%에 대해서는 실제 계측이 필요한 문제에 대해 추후 더 연구가 필요하다.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.510-519
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• 2020

Within an intelligent automated cyber-physical system, the realization of the autonomous mechanism for data collection, data integration, and data analysis plays a critical role in the design, development, operation, and maintenance of such a system. This construct is particularly vital for fault-tolerant route-finding systems that rely on the imprecise GPS location of the vehicles to properly operate, timely plan, and continuously produce informative feedback to the user. More essentially, the integration of digital twins with cyber-physical route-finding systems has been overlooked in intelligent transportation services with the capacity to construct the network routes solely from the locations of the operating vehicles. To address this limitation, the present study proposes a conceptual architecture that employs digital twin to autonomously maintain, update, and manage intelligent transportation systems. This virtual management simulation can improve the accuracy of time-of-arrival prediction based on auto-generated routes on which the vehicle's real-time location is mapped. To that end, first, an intelligent transportation system was developed based on two primary mechanisms: 1) an automated route finding process in which predictive data-driven models (i.e., regularized least-squares regression) can elicit the geometry and direction of the routes of the transportation network from the cloud of geotagged data points of the operating vehicles and 2) an intelligent mapping process capable of accurately locating the vehicles on the map whereby their arrival times to any point on the route can be estimated. Afterward, the digital representations of the physical entities (i.e., vehicles and routes) were simulated based on the auto-generated routes and the vehicles' locations in near-real-time. Finally, the feasibility and usability of the presented conceptual framework were evaluated through the comparison between the primary characteristics of the physical entities with their digital representations. The proposed architecture can be used by the vehicle-tracking applications dependent on geotagged data for digital mapping and location tracking of vehicles under a systematic comparison and simulation cyber-physical system.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.86-102
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• 2012

For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, and maps of the topography, geology, landcover and green infrastructure were constructed for a spatial database. The factors that influence flooded areas occurrence, such as slope gradient, slope, aspect and curvature of topography and distance from darinage, were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. The frequency ratio coefficient is overlaid for flooded areas susceptibility mapping as each factor's ratings. Then the flooded areas susceptibility map was verified and compared using the existing flooded areas. As the verification results, the frequency ratio model showed 82% in prediction accuracy. The method can be used to reduce hazards associated with flooded areas and to plan land use.

• Tunnel and Underground Space
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• v.14 no.6 s.53
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• pp.440-449
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• 2004

Rock mass includes natural discontinuities such as joints and faults during its formation. Discontinuities are also referred as planes of weakness because of their weak mechanical characteristics. In the design of underground structures, it is necessary to consider the properties of discontinuities to insure the stability. During the excavation of a tunnel, these discontinuities have to be identified as early as possible so that proper change in excavation method or support design can be made accordingly. The excavation of the tunnel in a stable rock mass causes a 3-dimensional arching effect around the excavation face. It was revealed by previous studies that the existence of a weak zone or a fault zone ahead of tunnel foe induces a typical displacement tendency of convergence. For better understanding of the meaning of influence/trend lines of various displacement components, three-dimensional numerical analyses were conducted while varying deformation moduli, thicknesses and orientations of discontinuities. Numerical results showed that the changes in influence/trend lines of various displacement components were very similar to those by measurements. The discrepancies from the expected values were dependent on the physical properties, thicknesses and orientations of discontinuities.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.129-150
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• 2006

Mineral potential mapping is an important procedure in mineral resource assessment. The purpose of this study is to analyze mineral potential using weight of evidence model and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential mineral in the Gangreung area, Korea. for this, a spatial database considering mineral deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The used mineral deposits were non-metallic(Kaolin, Porcelainstone, Silicastone, Mica, Nephrite, Limestone and Pyrophyllite) deposits of sedimentary type. The factors relating to mineral deposits were the geological data such as lithology and fault structure, geochemical data, including the abundance of Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, Zn, $Cl^-,\;F^-,\;{PO_4}^{3-},\;{NO_2}^-,\;{NO_3}^-,\;SO_{42-}$, Eh, PH and conductivity and geophysical data, including the Bouguer and magnetic anomalies. These factors were used with weight of evidence model to analyze mineral potential. Probability models using the weight of evidence were applied to extract the relationship between mineral deposits and related factors, and the ratio were calculated. Then the potential indices were calculated by summation of the likelihood ratio and mineral potential maps were constructed from Geographic Information System (GIS). The mineral potential maps were then verified by comparison with the known mineral deposit areas. The result showed the 85.66% in prediction accuracy.