• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.39-39
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• 2020

Low-flow 하천에서의 최저수위를 나타내는 지표이다. 일반적으로 유황곡선의 갈수량(Q355)를 대표적으로 사용한다. Low-flow는 물 공급 관리 및 계획, 관개용수, 생태계등 다양한 분야에 영향을 미친다. 이러한 Low-flow를 산정하기 위해서는 충분한 기간의 유량자료가 필요하다. 하지만 국토의 70%가 산지지형으로 구성되어 있는 우리나라의 경우 국가하천과 1급하천을 제외한 산지유역은 수위관측소가 부재하거나 결측으로 인해 자료가 충분하지 않아 Low-flow분석에 한계가 있다. 이에 과거에는 미계측지역의 갈수량을 예측하기 위해서 다중회귀분석, ARIMA 모형 등 다양한 기법을 사용하였지만, 최근들어 머신러닝 모형의 수요가 증가하고 있다. 이에 본 연구에서는 새로운 패러다임에 맞는 머신러닝 기법인 DNN기법을 사용하고자 한다. DNN기법은 ANN기법의 단점인 학습과정에서 최적 매개변수값을 찾기 어렵고, 학습시간이 느린 단점을 보완한 방법이다. 따라서 본연구에서는 머신러닝 기법인 DNN기법을 통해 미계측지역에 적용 가능한 지역화 Low-flow indices를 산정하고자 한다. 먼저, Low-flow에 영향을 미치는 인자들을 수집하고 인자들간의 상관분석, 다중공선성 분석을 통해 통계적으로 유의한 변수를 선정하여, 머신러닝 모형에 입력자료를 구축하였다. 또한 기존의 갈수량 예측기법인 다중회귀분석 결과와 비교하여 머신러닝 기법의 효용성을 검토하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.563-566
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• 2020

본 논문은 초고해상도 컴퓨터 홀로그램 생성을 위한 GPU 기반 2D Shift-FFT 의 효율적인 구현 방법을 제안한다. 본 연구가 제안하는 알고리즘은 기존에 여섯 단계로 이루어진 처리과정을 다섯 단계로 줄임으로서, 병렬처리에서 비효율적인 메모리 접근 과정을 줄인다. 또한, 핀드(pinned) 메모리 기반의 CPU-GPU 데이터 통신 통로인 핀드 버퍼(pinned buffer)를 사용하고 다중 스트림을 채용함으로써, GPU 활용의 주요 병목원인이 되는 데이터 통신의 부하를 줄이고 GPU 활용 효율을 높인다. 본 연구는 제안하는 알고리즘의 효용성을 증명하기 위해 서로 다른 두 시스템에 알고리즘을 구현하고, 다양한 크기의 행렬에 대한 2D-FFT 처리에 대한 성능을 측정하였다. 그 결과, CPU 기반의 FFTW 라이브러리 대비 최대 3 배, 동일한 GPU 를 사용하는 cuFFT 라이브러리 대비 최대 1.5 배 높은 성능을 달성하였다. 이러한 결과는, 본 연구가 제안하는 알고리즘의 효용성을 보여주는 결과다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1247-1254
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• 2020

With the advent of the smart home era, equipment that provides confidentiality or performs authentication exists in various places in real life. Accordingly security against physical attacks is required for encryption equipment and authentication equipment. In particular, fault injection attack that artificially inject a fault from the outside to recover a secret key or bypass an authentication process is one of the very threatening attack methods. Fault sources used in fault injection attacks include lasers, electromagnetic, voltage glitches, and clock glitches. Fault injection attacks are classified into single fault injection attacks and multiple fault injection attacks according to the number of faults injected. Existing multiple fault injection systems generally use a single fault source. The system configured to inject a single source of fault multiple times has disadvantages that there is a physical delay time and additional equipment is required. In this paper, we propose a multiple fault injection system using heterogeneous fault sources. In addition, to show the effectiveness of the proposed system, the results of a multiple fault injection attack against Riscure's Piñata board are shown.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.201-201
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• 2010

The evaporative desalination system with solar energy would be the efficient and attractive method to get fresh water. This study was described the development of Multi Effect Distillation(MED) with solar energy desalination system. The system was designed and manufactured Multi effect distillation on the capacity of $3m^3$/day. The experimental apparatus consists mainly of ejector pump, Hot water pump, flow meter, demister, cooler, evaporator and condenser. Evaporator and condenser were made Shell&Tube Heat Exchanger type with corrugated tube. The experimental variables were chosen $75^{\circ}C$ for hot water inlet temperature, 40, 60 and $80{\ell}$/min for hot water inlet volume flow rate, 6.0 and $8.0{\ell}$/min for evaporator feed seawater flow rate, $18^{\circ}C$ for sea water inlet temperature to cover the average sea water temperature and the salinity of sea water is measured about 33,000 PPM (parts per million). for a year in Korea. This study was analyzed the results of thermal performance of Multi Effect Distillation. The results are as follows, The experimental Multi effect distillation is required about 40 kW heat source for production of $3m^3$/day fresh water. Various operating flow rate was confirm in the experiments to get the optimum design data and the results showed that the optimum total flow was $8.0{\ell}$/min. Comparison of Single Effect Distillation with Multi Effect Distillation showed MED is at least more than double of SED.

• Clean Technology
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• v.22 no.4
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• pp.225-231
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• 2016

In this study, modeling and optimization study have been performed to obtain $1,524.58kg\;h^{-1}$ of a solidified NaCl by evaporating a 21.0 wt% of NaCl aqueous solution in order to reduce the steam consumption from $3,139kg\;h^{-1}$ to $496kg\;h^{-1}$ using a two-stage evaporation and a vapor recompression processes. Aspen Plus release 8.8 at AspenTech was utilized for the modeling of two stage evaporation process and PRO/II with PROVISION release 9.4 at Schneider Electric was also used for the simulation of two-stage vapor recompression process with an inter-cooler. For the simulation of the evaporation process containing NaCl aqueous solution, Aspen Plus release 8.8 at AspenTech Inc. was utilized and for the modeling of vapor recompression process PRO/II with PROVISION release at Schneider Electric Inc. For the vapor recompression process, single stage compression and two-stage compression system was compared.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.1
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• pp.131-139
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• 1987

In this study, characteristics of multi-photos and optimal photographing method are suggested by analyzing the normal case and convergent case with multiple method. The optimal photographing method is applied to deformation measurement of a model miniature structure under loading. Comparing with conventional measurement method in accuracy, efficiency and proprities of application of this method are suggested. As a result, the optimal photographing condition is ideal at $90^{\circ}$ convergent multiple case, whose measurement values approach to that of precision level within $5{\sim}9{\mu}m$ and bring more than about 55% improvement of accuracy comparing with normal case at the number of photos respectively. Therefore application of this method in deformation measurement as well as precision analysis of structures is desired in precision and economical aspect.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.471-478
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• 2011

High resolution Pan-sharpening technique is becoming increasingly important in the field of remote sensing image analysis as an essential image processing to improve the spatial resolution of original multispectral image. The general scheme of pan-sharpening technique consists of upsampling process of multispectral image and high-pass detail injection process using the panchromatic image. The upsampling process, however, brings about image blurring, and this lead to spectral distortion in the pan-sharpening process. In order to solve this problem, this paper presents a new method that adopts image restoration techniques based on optimization theory in the pan-sharpening process, and evaluates its efficiency and application possibility. In order to evaluate the effect of image restoration techniques on the pansharpening process, the result obtained using the existing method that used bicubic interpolation were compared visually and quantitatively with the results obtained using image restoration techniques. The quantitative comparison was done using some spectral distortion measures for use to evaluate the quality of pan-sharpened image.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.127-134
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• 2007

There are many researches about the intelligent stock trading systems with the help of the advance of the artificial intelligence such as machine learning techniques, Though the establishment of the reasonable trading policy plays an important role in the performance of the trading systems most researches focused on the improvement of the predictability. Also some previous works, which treated the trading policy, treated the simplified versions dependent on the predictors in less systematic ways. In this paper, we propose the integrated multiple simulation' as a method of optimizing trading performance of stock trading systems. The propose method is adopted in the NXShell a development environment for neural network based stock trading systems. Under the proposed integrated multiple simulation', we simulate the multiple tradings for all combinations of the neural network's outputs and the trading policy parameters, evaluate the learning performance according to the various metrics and establish the optimal policy for a given prediction module based on the resulting performance. In the experiment, we present the trading policy comparison results using the stock value data from the KOSPI and KOSDAQ.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.741-747
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• 2012

In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.23-29
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• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.