• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.901-908
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• 2011

For the available grid environmental realization, the resource supply PC must have to provide an appropriate security function of their operation environments. SKY@HOME is a kind of the grid computing environments. If this has not supervised by administrator handling smoothly, it is inherently vulnerable state to the security level of the grid environments, because the resource supply PC is not update a security function without delay. It is also have the troublesome problems which have to install of an additional security program for support the appropriate security. This paper proposes an integration security model on the policy-based that provides an update each level according to the situation of the resource supply PC for improving its problems as a security aspect of the SKY@HOME. This model analyzes the security state of the resource supply PC respectively, and then the result is available to provide an appropriate security of the resource supply PC using an integration security model. The proposed model is not need additionally to buy and install the software, because it is provided the security management server oriented service. It is also able to set up the suit security function of a characteristic of the each resource supply PC. As a result, this paper clearly show the participation of resource supply PC improved about 20%.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.273-273
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• 2010

고열부하 환경에 노출되는 핵융합로의 플라즈마 대향부품은 주로 낮은 원자번호 물질-열전도가 좋은 물질-구조체의 순으로 다층 구조를 이루고 있으며, 이들 간의 우수한 접합성은 부품의 성능을 좌우하는 핵심 요소이다. 이러한 플라즈마 대향부품의 건전성을 평가하기 위해서는 고열속의 열부하를 반복적으로 인가하는 시험이 요구되며, 이를 위해 본 연구원에서는 KoHLT-1, 2의 시험시설을 운용하고 있다. 본 시설에서는 열부하원으로서 그라파이터 히터를 사용하며, 히터는 두 개의 시험 대상부품 사이에 설치되고, 히터에 고전류를 인가하여 복사열에 의해 시험 부품에 열부하를 가하게 된다. 고열부하 환경에서 열피로 시험을 위해 히터에 인가되는 전류를 시간에 따라 일정한 패턴으로 반복적으로 ON-OFF 하게 된다. 본 논문에서는 이러한 고열부하시험을 수행함에 있어 고려해야 할 여러 가지 요소에 대해 논의하였다. 우선 인가하는 열유속(heat flux) 값은 일차적으로 시험시설의 최대 출력에 의해 좌우되며, 시험대상물의 운전조건 및 열부하 반복횟수에 의해 결정된다. 열부하 반복횟수는 주어진 열유속 값에 대해 total strain이 파단에 이르는 수준에 의해 결정된다. 열부하를 인가하는 시간은 히터에 전류를 인가했을 때 요구되는 온도로 상승하는 데 걸리는 시간과 시험대상물의 온도가 더 이상 증가하지 않는데 걸리는 시간에 의해 좌우된다. 냉각시간은 길수록 시험대상물의 온도가 냉각수의 온도에 접근하게 되나 너무 길어지면 시험시간이 급격히 증가하게 되므로, 온도 감소 곡선을 검토하여 적절한 시간을 정하게 된다. 열유속 측정은 냉각수의 온도 상승값과 유량으로부터 계산하게 되며, 정확한 측정을 위해서는 열부하를 인가하는 시간이 충분히 길어야 한다. 또한 시험대상 부품에서 열부하가 인가되는 면적을 정확히 정의해야 하며, 냉각관로에 열부하가 인가되어서는 않된다. 또한 시험대상부품을 지지하는 지지구조체를 통한 열손실을 최소화해야 정확한 열유속을 측정할 수 있다. 시험대상부품을 설치할 때 히터와의 간격 또한 결정해야 할 중요한 요소이며, 간격이 좁을수록 최대 열유속 값을 증가시킬 수 있으나, 너무 가까운 경우 히터의 열변형에 의한 접촉 및 아크 방전의 가능성이 있으며, 이 경우 히터와 시험대상부품의 손상을 가져오게 된다. 시험대상물이 국제열핵융합로(ITER)의 일차벽과 같이 베릴륨이 포함되어 있는 경우 방전에 의한 손상은 인체에 유해한 오염의 원인이 될 수 있다. 또한 순간적인 방전은 고가의 고전류전원의 고장을 유발할 수도 있다. 열부하 시험 중 시험대상물의 온도를 정확히 측정하는 것은 필수적이며, 온도 변화 곡선으로부터 시험대상물의 건전성 여부를 판단할 수 있다. 이를 위해 변화를 가장 잘 탐지 할 수 있는 위치에 온도 센서를 설치하는 것이 관건이며, 이는 사전 분석을 통해 알 수 있다.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.299-316
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• 2019

The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.5
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• pp.193-204
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• 2019

The explosion of data due to the improvement of sensor technology and computing performance has become the basis for analyzing the situation in the industrial fields, and various attempts to detect events based on such data are increasing recently. In particular, sound signals collected from sensors are used as important information to classify events in various application fields as an advantage of efficiently collecting field information at a relatively low cost. However, the performance of sound-event classification in the field cannot be guaranteed if noise can not be removed. That is, in order to implement a system that can be practically applied, robust performance should be guaranteed even in various noise conditions. In this study, we propose a system that can classify the sound event after generating the enhanced sound signal based on the deep learning algorithm. Especially, to remove noise from the sound signal itself, the enhanced sound data against the noise is generated using SEGAN applied to the GAN with a VAE technique. Then, an end-to-end based sound-event classification system is designed to classify the sound events using the enhanced sound signal as input data of CNN structure without a data conversion process. The performance of the proposed method was verified experimentally using sound data obtained from the industrial field, and the f1 score of 99.29% (railway industry) and 97.80% (livestock industry) was confirmed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.131-142
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• 2009

While users of information security products require high-quality products that are secure and have high performance, there are neither examples for evaluating the quality of information security products nor studies on the quality model and metrics for the quality evaluation. In this paper, information security products are categorized into three different types and the security and performance of various information security products are analyzed. Through this process and after consideration of information security products' security and performance, a new quality model that possesses 7 characteristics and 24 sub-characteristics has been defined. In addition, metrics consisting of 62 common and 45 extended metrics that can be used to evaluate the quality of information security products are introduced, and a proposition for a method of generating the quality evaluation metrics for specific information security products is included. The method of generating metrics proposed in this paper can be extended in order to be applied to a variety of information security products, and by generating and verifying the quality evaluation metrics for firewall, intrusion detection systems and fingerprint systems it is shown that it applicable on a variety of information security products.

• Journal of the Korea Society for Simulation
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• v.31 no.3
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• pp.45-54
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• 2022

This paper is about designing and implementing the simulation training equipment (simulator) for the CBRN Reconnaissance Vehicle-II (armor type). The simulation training equipment (simulator) is a military training equipment in a virtual environment that analyzes the training using various CBRN equipment according to the CBRN situation and make a professional report. The controller or training instructor can construct a scenario using the instructor control system for a possible CBRN situation, spread the situation, and observe the process of the trainee performing the propagated situation appropriately. All process can be monitored and analyzed by the system, and it can be recorded, so it is also used for AAR (After Action Review). To implement CBRN situation training in a virtual environment, instructor control (IOS), host (HOS), video (IGS), input/output device (IOC), and sound (ACS) were implemented, a long-range chemical automatic detector (LCA), a combined chemical detector (CAD), a control (MCC) and an operation (OCC) computer were developed as simulators. In this paper, the design and development of simulation training equipment for CBRN Reconnaissance Vehicle-II (armor type) was conducted, and the performance was verified through integrated tests and acceptance tests.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.10-18
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• 2009

A moving surface vessel generates a ship wake which contains a cloud of micro-bubbles with radii ranging between $8{\sim}200{\mu}m$. Such micro-bubbles can be detected by active sonar system for more than ten minutes depending on the size and speed of the surface vessel. In this paper, a reverberation model for the ship wake is presented. The developed model consists of the acoustic scattering model due to the distribution of the micro-bubbles and the kinematic model for the moving active sonar. The acoustic scattering model is based on the volume integration, where the volume scattering strengths are obtained from the spatial distribution of micro-bubbles. Since the directivity and look-direction of active sonar are important factors for moving active sonar, the kinematic model utilizes the Euler transformation to obtain the relative motion between the global and local coordinates. In order to verify the developed model, a series of sea experiment was executed in September 2007 to obtain the spatial-temporal distribution of a bubble cloud, and analyzed to be compared with the simulation results.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.375-384
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• 2022

The shipping industry is cyclically impacted by complex variables such as various economic indicators, social events, and supply and demand. The purpose of this study was to analyze the operating profit of 13 Korean liner companies over 30 years, including the financial crisis of the late 1990s, the global financial crisis of the late 2000s, and the COVID-19 global pandemic. This study was conducted to also identify factors that impacted the profit ratio of Korea's liner shipping companies according to economic conditions. It was divided into ocean-going and short-sea shipping, reflecting the characteristics of liner shipping companies, and was analyzed by hierarchical multiple regression analysis. The time series data are based on the Korean International Financial Reporting Standards (K-IFRS) and comprise seaborne trade volume, fleet evolution, and macroeconomic indicators. The outliers representing the economic downturn due to social events were separately analyzed. As a result of the analysis, the China Container Freight Index (CCFI) positively impacted ocean-going as well as short-sea liner shipping companies. However, the Korean container shipping volume only impacted ocean-going liners positively. Additionally, world and Korea's GDP, world seaborne trade volume, and fuel price are factored in the operating profit of short sea liner shipping. Also, the GDP growth rate of China, exchange rate, and interest rate did not significantly impact both groups. Notably, the operating profitability of Korea's liner shipping shows an exceptionally high rate during the recessions of 1998 and 2020. It is paradoxical, and not correlated with the classical economic indicators. Unlike other studies, this paper focused on the operating profit before financial expenses, considering the complexity as well as difficulty in forecasting the shipping cycle, and rendered conclusions using relatively long-term empirical analysis, including three economic shocks.