• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• 정보보호학회논문지
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• 제22권3호
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• pp.485-494
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• 2012

HIGHT는 국내에서 개발된 초경량 블록 암호로서, 정보통신단체(TTA) 표준과 국제표준화기구(ISO/IEC) 18033-3 표준으로 제정되었다. 본 논문에서는 블록암호 HIGHT에 대한 차분 오류 주입 공격을 제안한다. 제안하는 공격에서 공격자는 암호화 과정에서 라운드 28의 입력값에 임의의 1-바이트 오류를 주입할 수 있다고 가정한다. 이러한 가정에서 오류 주입을 통해 얻어진 암호문과 정상적으로 얻어진 암호문의 차분 특성을 이용하여 비밀키를 복구한다. 12개의 오류를 주입할 경우에는 88%의 성공 확률, 7개의 오류를 주입하는 경우에는 51%의 성공 확률로 수초내에 HIGHT의 비밀키를 복구한다.

• Journal of Korea Trade
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• 제24권3호
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• pp.55-72
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• 2020

Purpose - In this paper, we provide recommendations for Korea's long-term direction and strategic measures to attract inward foreign direct investment (FDI) in response to Japan's export regulations. In doing so, we analyze the current situation and characteristics of trade between Korea and Japan, focusing on the parts and materials industry, which is particularly affected by Japan's trade regulations. Design/methodology - Based on the analysis of five successful inward FDI cases (e.g. Toray, IGK, Delkor, GlobalWafers, DuPont) and statistic trend review in the parts and materials industry, we consider various factors pertaining to successful inward FDI in Korea and propose valuable investment attraction strategies. Findings - For a successful investment attraction strategy, we studied some statistical trends in the internal and external environments of the parts and materials industry and successful investment attraction cases in Korea. We have found that in order to increase the probability of success in attracting investment, we need a mid-to long-term strategy considering multiple factors such as "Production-oriented, Demand-linked, Global Value Chain (VGC) linked, and Policy-linked investment attraction." Originality/value - We suggest several specific measures and important strategic implications for the Korean government and firm's managers to attract inward FDI successfully.

• 한국기계가공학회지
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• 제20권12호
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• pp.1-7
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• 2021

The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• 시스템엔지니어링학술지
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• 제19권2호
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• 환경생물
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• 제40권4호
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• pp.525-549
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• 2022

개체군은 진화적 단위이자 개체군 통계적 단위로서, 생물학적 침입의 과정은 개체군의 생태적, 진화적 동태에 기인한다. 개체군의 공간구조는 개체군 동태에 영향을 줄 수 있으며, 분산이 반드시 동반되는 생물학적 침입에 대한 연구에서는 공간구조를 고려할 필요가 있다. 메타개체군 이론은 공간구조를 가진 개체군에 대한 대표적인 접근으로, 주로 생태학 및 진화생물학 연구에 활용되고 있다. 메타개체군은 공간적 구조를 가진 개체군의 동의어로 여겨지기도 하지만, 용어의 모호한 사용을 피하기 위해서는 절멸 가능성이 고려되는 경우에 한해 정의되어야 한다는 비판이 있다. 침입 초기 단계의 개체군은 높은 절멸 가능성을 가지는 경우가 많으므로, 메타개체군 이론을 적용하기에 용이하다. 한편, 침입 초기 개체군의 생태적·유전적 특성은 분산의 영향을 크게 받기 때문에, 메타개체군 이론은 개체군 수준에서 침입성의 변화와 침입 가능성을 설명하는 강력한 도구가 될 수 있을 것으로 생각된다. 그러나, 한국에서 침입생물에 대한 생태학적 연구는 주로 종 수준의 분포 변화에 대해 이루어지고 있고, 메타개체군 개념을 적용한 경우가 드문 실정이다. 메타개체군 이론을 활용한다면, 국내 연구가 상대적으로 미진했던 개체군 단위의 침입 기작을 보다 상세히 규명할 수 있을 것으로 생각된다. 본 연구에서는 실제 침입생물에 미치는 메타개체군의 영향을 쉽게 파악하기 위해 침입생물 메타개체군이 자연적인 분산 거리를 넘어서 연결되는지 여부에 따라 장거리와 중거리 두 가지 규모로 나누는 체계를 활용할 것을 제안하였다. 메타개체군 개념에 입각한 침입생물 연구가 침입의 기작을 이해하고 장래의 침입 리스크를 예측·관리하는 데에 도움을 줄 것으로 기대한다.

• 지능정보연구
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• 제25권1호
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• pp.63-83
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• 2019

투자자들은 기업의 내재가치 분석, 기술적 보조지표 분석 등 복잡한 분석보다 차트(chart)에 나타난 그래프(graph)의 모양으로 매매 시점을 찾는 직관적인 방법을 더 선호하는 편이다. 하지만 패턴(pattern) 분석 기법은 IT 구현의 난이도 때문에 사용자들의 요구에 비해 전산화가 덜 된 분야로 여겨진다. 최근에는 인공지능(artificial intelligence, AI) 분야에서 신경망을 비롯한 다양한 기계학습(machine learning) 기법을 사용하여 주가의 패턴을 연구하는 사례가 많아졌다. 특히 IT 기술의 발전으로 방대한 차트 데이터를 분석하여 주가 예측력이 높은 패턴을 발굴하는 것이 예전보다 쉬워졌다. 지금까지의 성과로 볼 때 가격의 단기 예측력은 높아졌지만, 장기 예측력은 한계가 있어서 장기 투자보다 단타 매매에서 활용되는 수준이다. 이외에 과거 기술력으로 인식하지 못했던 패턴을 기계적으로 정확하게 찾아내는 데 초점을 맞춘 연구도 있지만 찾아진 패턴이 매매에 적합한지 아닌지는 별개의 문제이기 때문에 실용적인 부분에서 취약할 수 있다. 본 연구는 주가 예측력이 있는 패턴을 찾으려는 기존 연구 방법과 달리 패턴들을 먼저 정의해 놓고 확률기반으로 선택해서 매매하는 방법을 제안한다. 5개의 전환점으로 정의한 Merrill(1980)의 M&W 파동 패턴은 32가지의 패턴으로 시장 국면 대부분을 설명할 수 있다. 전환점만으로 패턴을 분류하기 때문에 패턴 인식의 정확도를 높이기 위해 드는 비용을 줄일 수 있다. 32개 패턴으로 만들 수 있는 조합의 수는 전수 테스트가 불가능한 수준이다. 그래서 최적화 문제와 관련한 연구들에서 가장 많이 사용되고 있는 인공지능 알고리즘(algorithm) 중 하나인 유전자 알고리즘(genetic algorithm, GA)을 이용하였다. 그리고 미래의 주가가 과거를 반영한다 해도 같게 움직이지 않기 때문에 전진 분석(walk-forward analysis, WFA)방법을 적용하여 과최적화(overfitting)의 실수를 줄이도록 하였다. 20종목씩 6개의 포트폴리오(portfolio)를 구성하여 테스트해 본 결과에 따르면 패턴 매매에서 가격 변동성이 어느 정도 수반되어야 하며 패턴이 진행 중일 때보다 패턴이 완성된 후에 진입, 청산하는 것이 효과적임을 확인하였다.