• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2001년도 학술대회논문집
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• pp.139-144
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• 2001

Insulation aging diagnosis system provides early warning regarding electrical equipment defect. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear, it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a magnetic wave and acoustic signal to diagnoses an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System) and acquires 2D patterns from analyzing it. For filtering the noise contained in sensor signals we used ICA algorithms. Using this data, design of the neuro-fuzzy model that diagnoses an electrical equipment is investigated. Validity of the new method is asserted by numerical simulation.

• 한국지진공학회논문집
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• 제25권2호
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• 한국항해항만학회지
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• 제42권1호
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• pp.57-66
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• 2018

해운산업은 파생적 수요의 특성으로 대외적 요인에 영향을 크게 받는다. 하지만, 공급 측면은 이러한 수요의 변화에 즉각적으로 대응할 수 없는 특성 때문에 해운산업은 호황과 불황을 거듭하게 된다. 그러므로 정부는 이러한 상황에 대응하기 위해 조기경보모형을 구축해 시장을 모니터링하고 다가올 위험을 예측하는 것이 필요하다. 본 연구에서는 신호접근법을 사용해 조기경보모형을 구축하였으며, 위기지수는 BDI를 사용해 정의하였으며 금융, 경제, 선박 등 다양한 선행지수를 활용해 종합선행지수를 도출하였다. 그 결과, 종합선행지수가 해운분야의 실제 위기지수와 비교해 4개월의 시차를 두고 높은 상관관계를 보였고, QPS(Quadratic Probability Score)가 0.37로 정확도가 높은 것으로 나타났다.

• 사물인터넷융복합논문지
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• 제10권2호
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• pp.25-41
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• 2024

노천 광산 채굴 시나리오와 관련하여 현재 중국에서는 주요 수동 및 정기 검사를 위한 비디오 모니터링을 사용하는 것으로 인건비를 지속적으로 투자해야 하며 적시성이 낮다. 이 조기경보 모니터링의 문제를 해결하기 위해 이 글에서는 공간화 알고리즘 모델을 개발하여 노천광산의 월경채굴 조기경보시스템을 설계하고 광산채굴장비의 지리적 정보를 산출하고 실시간으로 광산 승인 범위의 레이어 좌표와 비교하고, 자동으로 광산의 월경 채굴 행동을 예측한다. 장시 핑샹 지역을 연구 대상으로 하여 노천 광산 채굴 엔지니어링 기계 장비를 식별 및 추적 대상으로 선정하였으며, 현장 실험을 통해 시스템이 안정적이고 신뢰할 수 있으며 검증 시스템의 목표 추적 정확도가 높은 것으로 나타났으며, 광산 채굴 감독의 적시성과 정확성을 향상시킬 수 있고 감독의 인건비를 크게 절감할 수 있다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권3호
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• pp.191-193
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• 2002

Insulation aging diagnosis system provides early warning in regard to electrical equipment defects. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. For solving this problem, many researchers proposed a method that diagnose power plant by using partial discharge. In this paper, we design the intelligent sensor to diagnose insulation degradation state that uses a Microprocessor and Al. Proposed sensor has MCU that is used to diagnose insulation degradation and communicate with main IDD system. And we use a fuzzy model to diagnose insulation degradation.

• Geomechanics and Engineering
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• 제11권1호
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• pp.41-58
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• 2016

This paper focuses on the deformation behavior of tunnels crossing a weak zone in conventional tunneling. A three-dimensional finite element model was adopted that allows realistic modeling of the tunnel excavation and the support installation. Using the 3D FE model, a parametric study was conducted on a number of tunneling cases with emphasis on the spatial characteristics of the weak zone such as the strike and dip angle, and on the initial stress state. The results of the analyses were thoroughly examined so that the three-dimensional tunnel displacements at the tunnel crown and the sidewalls can be related to the spatial characteristic of the weak zone as well as the initial stress state. The results indicate that the effectiveness of the absolute displacement monitoring data as early warning indicators depends strongly on the spatial characteristics of the weak zone. It is also shown that proper interpretation of the absolute monitoring data can provide not only early warning for a weak zone outside the excavation area but also information on the orientation and the extent of the weak zone. Practical implications of the findings are discussed.

• 지질공학
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• 제26권2호
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• pp.235-250
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• 2016

우리나라는 여름철 강수량이 연 강수량의 약 70% 이상을 차지하고 일 강우량이 200 mm가 넘는 극한강우가 증가하고 있다. 강우는 산사태를 유발하는 가장 직접적인 인자로서 이를 활용한 산사태 발생 예측 기준을 설정하고 경보를 발령하여 산사태로 인한 피해를 최소화 하는 것이 필요하다. 본 연구에서는 기존의 발생한 산사태이력 중 발생시점 및 장소가 분명한 12개소를 선정하고 각 지역의 강우데이터를 수집하여 분석하였으며, RTI (Rainfall Triggering Index) 모델에 사용된 각 인자들을 한국의 산사태 유발 강우특성에 따라 적정성을 검토하여 반영하고 강우강도의 단위시간을 달리한 3가지 모델을 비교하였다. 분석결과, 60-minutes RTI 모델은 3개소에서 산사태 발생 예측에 실패하였으며, 30-minutes RTI 모델 및 10-minutes RTI 모델은 모두 사전예측 가능하였다. 각 모델별 산사태 발생 경보에 따른 평균 대응시간은 60-minutes RTI model이 4.04시간, 30-minutes RTI model과 10-minutes RTI model은 각각 6.08과 9.15시간으로 단위시간이 짧은 강우강도를 사용한 RTI 모델이 산사태 사전예측실패 가능성이 적고 보다 긴 대응시간을 확보 할 수 있는 것으로 나타났다. 이를 통해 산사태 발생 예측을 통한 대응시간은 단위시간을 세분화한 모델일수록 더 많은 시간을 확보 할 수 있음을 알 수 있다. 또한, 단시간 내 발생하는 변동성이 큰 강우강도 가진 한국의 강우특성을 고려할 때 시간 단위 이하의 강우강도를 적용하는 것이 RTI 모델을 통한 산사태 예측과 조기경보시스템의 정확도를 높일 수 있을 것으로 판단된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.231-235
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• 2001

Insulation aging diagnosis system provides early warning regarding electrical equipment defect. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear, it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a magnetic wave and acoustic signal to diagnoses an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System) and acquires 2D Patterns from analyzing it. For fettering the noise contained in sensor signals we used ICA algorithms. Using this data design of the neuro-fuzzy model that diagnoses an electrical equipment is investigated. Validity of the new method is asserted by numerical simulation.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.827-830
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• 2006

The tsunami from the megathrust earthquake magnitude 9.3 on 26 December 2004 is the largest tsunami the world has known in over forty years. This tsunami destructively attacked 13 countries around Indian Ocean with at least 230,000 fatalities, displaced people 2,089,883 and 1.5 million people who lost their livelihoods. The ratio of women and children killed to men is 3 to 1. The total damage costs US$ 10.73 billion and rebuilding costs US$ 10.375 billion. The tsunami's death toll could have been drastically reduced, if the warning was disseminated quickly and effectively to the coastal dwellers along the Indian Ocean rim. With a warning system in Indian Ocean similar to that operating in the Pacific Ocean since 1965, it would have been possible to warn, evacuate and save countless lives. The best tribute we can pay to all who perished or suffered in this disaster is to heed its powerful lessons. UNESCO/IOC have put their tremendous effort on better disaster preparedness, functional early warning systems and realistic arrangements to cope with tsunami disaster. They organized ICG/IOTWS (Indian Ocean Tsunami Warning System) and the third of this meeting is held in Bali, Indonesia during $31^{st}$ July to $4^{th}$ August 2006. A US$ 53 million interim warning system using tidal gauges and undersea sensors is nearing completion in the Indian Ocean with the assistance from IOC. The tsunami warning depends strictly on an early detection of a tsunami (wave) perturbation in the ocean itself. It does not and cannot depend on seismological information alone. In the case of 26 December 2004 tsunami when the NOAA/PMEL DART (Deep-ocean Assessment and Reporting of Tsunami) system has not been deployed, the initialized input of sea surface perturbation for the MOST (Method Of Splitting Tsunami) model was from the tsunamigenic-earthquake source model. It is the first time that the satellite altimeters can detect the signal of tsunami wave in the Bay of Bengal and was used to validate the output from the MOST model in the deep ocean. In the case of Thailand, the inundation part of the MOST model was run from Sumatra 2004 for inundation mapping purposes. The medium and high resolution satellite data were used to assess the degree of the damage from Indian Ocean tsunami of 2004 with NDVI classification at 6 provinces on the Andaman seacoast of Thailand. With the tide-gauge station data, run-up surveys, bathymetry and coastal topography data and land-use classification from satellite imageries, we can use these information for coastal zone management on evacuation plan and construction code.

• 상하수도학회지
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• 제37권5호
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• pp.271-279
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• 2023

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.