• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.183-183
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• 2021

Deep learning methods and their application have become an essential part of prediction and modeling in water-related research areas, including hydrological processes, climate change, etc. It is known that application of deep learning leads to high availability of data sources in hydrology, which shows its usefulness in analysis of precipitation, runoff, groundwater level, evapotranspiration, and so on. However, there is still a limitation on microclimate analysis and prediction with deep learning methods because of deficiency of gauge-based data and shortcomings of existing technologies. In this study, a real-time rainfall prediction model was developed from a sky image data set with convolutional neural networks (CNNs). These daily image data were collected at Chung-Ang University and Korea University. For high accuracy of the proposed model, it considers data classification, image processing, ratio adjustment of no-rain data. Rainfall prediction data were compared with minutely rainfall data at rain gauge stations close to image sensors. It indicates that the proposed model could offer an interpolation of current rainfall observation system and have large potential to fill an observation gap. Information from small-scaled areas leads to advance in accurate weather forecasting and hydrological modeling at a micro scale.

• 한국압력기기공학회 논문집
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• 제19권2호
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• pp.93-101
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• 2023

In this study, a deep learning algorithm was used to diagnose electric potential signals obtained through CIPS and DCVG, used indirect inspection methods to confirm the soundness of buried pipes. The deep learning algorithm consisted of CNN(Convolutional Neural Network) model for diagnosing the electric potential signal and Grad CAM(Gradient-weighted Class Activation Mapping) for showing the flaw prediction point. The CNN model for diagnosing electric potential signals classifies input data as normal/abnormal according to the presence or absence of flaw in the buried pipe, and for abnormal data, Grad CAM generates a heat map that visualizes the flaw prediction part of the buried pipe. The CIPS/DCVG signal and piping layout obtained from the 3D finite element model were used as input data for learning the CNN. The trained CNN classified the normal/abnormal data with 93% accuracy, and the Grad-CAM predicted flaws point with an average error of 2m. As a result, it confirmed that the electric potential signal of buried pipe can be diagnosed using a CNN-based deep learning algorithm.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(2)
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• pp.910-911
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• 2005

• 한국항공우주학회지
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• 제44권3호
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• pp.240-246
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• 2016

근래 우주 과학 및 연구의 가장 뜨거운 뉴스 또는 성과는 2013년 NASA의 화성 로버인 Curiosity, 2013년 중국의 달 착륙선/로버인 Chang'e 3 호, 2014년 ESA의 67P/Churyumov-Gerasimenko 소행성 탐사선 Rosetta, 그리고 2015년 NASA의 명왕성 탐사선 New Horizons 일 것이다. 이와 같은 장거리 심우주 탐사가 현 기술로 가능하다는 것에 매우 고무될 수밖에 없다. 하지만 이런 놀라운 심우주 항행 기술의 발전에도 불구하고 심우주 데이터 통신 기술 영역은 이렇다 할 변화가 없었다. 이 영역은 큰 변화를 현재까지 거부해 왔으나 최근 들어 지상의 우수한 통신 기술들을 심우주 탐사에 적용하려는 움직임이 관찰되고 있음에 주목할 필요가 있다. 그중에 하나가 본 논문에서 다루려고 하는 태양계 인터넷 기술이다. 본 논문에서 심우주 탐사에 태양계 인터넷이 미치는 영향을 분석하여 발표하고자 한다.

• 수산경영론집
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• 제24권2호
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• pp.17-53
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• 1993

Pusan is the largest fishing port in Korea, and deals with more than I million ton of fish catches annually, including catches of coastal and off-shore fisheries as well as those of deep-sea fishery. However, it hen had no fishing port facilities specialized fer deep-sea fishery since it started 30 years ago. Economic and physical losses resulting from this have teen enormous. Although fishing port facilities are a part of infra-structures built by Governments, the construction of them has been delayed due to financial difficulties of Central or local governments. To overcome this harsh situation to which deep-sea fishery cooperations faced, some cooperations have decided to construct fishing port facilities including fish-pier specialized for deep-sea fishery in Gamcheon port. The construction expenses of these facilities were financed by private funds to which they themselves jointly contributed. As a result, a fish-[pier, which has the capacity of serving one fishing vessel of 10, 000 ton or four of 5, 000 ton or four of 1, 000 ton at the same time, will be opened in here by 1994. The paper examines the master plan to revitalize the deep-sea fisheries industry in a deep depression with the opening of these physical facilities. The framwork of the plan is pursued in two different aspects, which are both hardware and software. In a hardware aspect, the plan in to develop Pusan into a city which is suitable for one of the best fishing ports in the world. That is, it is to develop the city into a place famous for sightseeing as well as the distribution and processing of fish-products centering around fish-piers. On the other hand, in a software aspet, it is regarding improvement of the distribution system of fish-products. One way to do that is to make up some deficiencies of the current system of a producers' joint sale. And the other is to establish an exchange of fish-products futures. Through these institutions, we could abrsorb speculative funds, which would otherwise be invested in speculation on fish-products, into productive investment opportunities, We believe that if the plan is realized, the deep-sea fishery in Korea will revive from a long-tasted depression and make progress to become one of the mai industries of Korea.

• 한국수산과학회지
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• 제49권3호
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• pp.393-403
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• 2016

A steady, strong southward flow was observed in the lower layer beneath the Tsushima Warm Current in the deepest trough of the Korea Strait. Known as the Korea Strait Bottom Cold Water (KSBCW), this bottom current had a mean velocity of 24 cm/s and temperatures below 8–10℃. The direction of the bottom current was highly stable due to the topographic effects of the elongated trough. To determine the path of the southward bottom current, ADCP (Acoustic Doppler Current Profiler) data from 14 stations between 1999 and 2005 were examined. Persistent southward flows with average speeds of 4–10 cm/s were observed at only three places to the north of the strait where the bottom depths were 100–124 m. The collected data suggest a possible course of the southward bottom current along the southeast Korean coast before entering the deep trough of the Strait.

• 한국재료학회지
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• 제12권6호
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• pp.431-435
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• 2002

The dark current and photocurrent(PC) spectrum of Mg-doped GaN thin film were investigated with various bias voltages and temperatures. At high temperature and small bias, the dark current is dominated by holes thermally activated from an acceptor level Al located at about 0.16 eV above the valence band maximum $(E_v)$, The PC peak originates from the electron transition from deep level A2 located at about 0.34 eV above the $E_v$ to the conduction band minimum $(E_ C)$. However, at a large bias voltage, holes thermally activated from A2 to Al experience the field-in-duces tunneling to form one-dimensional defect band at Al, which determines the dark current. The PC peak associated with the transition from Al to $E_ C$ is also observed at large bias voltages owing to the extended recombination lifetime of holes by the tunneling. In the near infrared region, a strong PC peak at 1.20 eV appears due to the hole transition from deep donor/acceptor level to the valence band.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2001년도 추계학술대회 논문집 Vol.14 No.1
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• pp.497-500
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• 2001

Due to their better photosensitivity in X-ray, the amorphous selenium based photoreceptor is widely used on the X-ray conversion materials. It was possible to control the charge carrier transport of amorphous selenium by suitably alloying a-Se with other elements(e.g. As, Cl). The charge transport properties of amorphous Selenium is decided on hole which is induced from metal to selenium in metal-selenium junction and which is transferred in a-Se bulk. This phenomenon is resulted of changing electric field owing to increasing of space charge by deep trap of a-Se bulk. In this paper, We dopped the chlorine to compensate deep hole trap and deposited blocking layer using dielectric material to prevent from increasing space charge for injection charge between metal electrode and a-Se layer. We compared space charge and the decreasing of trap density through measuring dark and photo current. 缀Ѐ㘰〻ሀ䝥湥牡氠瑥捨湯汯杹

• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1784-1790
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• 2019

Accurate and consistent characterization of defects in steam generator tubes (SGT) in nuclear power plants is one of the key issues in the field of nondestructive testing since the large number of signals to be analyzed in a time-limited in-service inspection causes a serious problem in practice. This paper presents an effective approach to this difficult task of automated classification of motorized rotating pancake coil (MRPC) eddy current flaw acquired from tube specimens with deliberated defects using deep neural networks (DNN). This approach consists of five steps, namely, the data acquisition using the MRPC probe in the tube, the signal preprocessing to make data more suitable for training DNN, the data augmentation for boosting a training performance, the training of DNN, and finally demonstration of the trained DNN for discriminating the axial and circumferential defects. The high performance obtained in this study shows that DNN is useful for classification of defects in tubes from the MRPC eddy current signals even though the number of signals is very large.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.