• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.49-57
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• 2019

In this paper, we describe the implementation methods and algorithms for the various technologies and devices required for the construction of the engagement HILS(Hardware In the Loop Simulation) in the limited space to simulate the high-speed maneuvering encounter situation of the weapon system in 3-dimensional real world space. Through this research, we have been able to suggest ways to analyze the major design elements of future electronic warfare equipment through experiments simulating actual engagements between various high-speed maneuvering weapons systems and electronic warfare devices in the future battlefield. It was confirmed that the M&S technology could be used to eliminate technical risks, reduce development cost, and shorten development time in the future real system development. The results of this study can be a great assist not only for the field of electronic warfare system research and development, but also for the research & implementation on HILS of various engaging class weapons systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.76-76
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• 2021

물정보 중 가뭄 정보가 상대적으로 부족한 원인은 무엇을 가뭄으로 볼 것인지 정의하기 어렵기 때문이다. 특히 우리나라와 같이 댐 및 저수지, 광역상수도 등 수자원시스템 네트워크를 기반으로 물공급이 이루어지는 경우, 개별 요소만을 고려한 기존 가뭄모니터링 및 전망은 현실적이지 못하며, 가뭄 위험도 관리 측면에서도 부족한 부분이 있다. 가뭄 현상의 경우 기상학적 영향인 강수의 부족이 가장 큰 요소로 기여하지만 실질적으로 국민에 필요한 양보다 적은 양의 물이 공급될 때 국민들은 가뭄을 체감한다. 이러한 점을 보완하기 위하여 지역별로 사용하는 수원 및 물수급 시설 등을 세분화하고, 실적기반 분석을 통해 분석대상 지역의 가뭄을 정확히 판단하기 위한 합리적인 물수급 분석 모형 개발이 필요하다. 즉, 공간분석단위를 표준유역 단위 이하의 취방류 시설물을 기준으로 구성하고, 이들 시설물의 운영정보와 수문기상 빅데이터를 연계한 물순환 모형을 구현함으로써 댐, 저수지, 하천 등 다양한 수원을 가지는 유역 내 가용 수자원량을 준실시간 개념으로 평가하는 시스템의 개발이 필요하다. 본 연구에서는 하천을 중심으로 물수급 관련 수요·공급 시설의 위치를 절점으로 부여하고 연결하는 물수급 네트워크 알고리즘을 통해 빅데이터 기반 물수급 분석 모형을 개발하였다. 주요 모니터링 지점 및 모든 이수 시설의 위치를 유역분석 기법을 통하여 점(point), 선(line), 면(shape)으로 구성된 지형공간정보의 위상(topology) 관계를 설정하여 물수급 분석의 계산순서를 선정하고, 시계열 DB를 입력하여 지점별 물수급 분석 결과를 도출하였다. 권역별 주요 수위-유량관측소 1:1 Nash 계수를 검증한 결과 저유량에서 0.8 이상의 높은 재현 성능을 보이는 것으로 나타났다. 이에 따라 본 연구에서 개발된 물수급 분석 모형은 향후 물관련 이슈 지역의 용수공급능력 평가 및 수자원장기종합계획 등 다양한 수자원 정책평가에 활용될 것으로 기대된다.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.37-44
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• 2013

In study suggested clinical availability to shoulder forced traction method in term of quality of image, the patient's convenience and stability, according to whether to use of shoulder forced traction bend using computed tomography(CT) that X-ray calibration and various mathematic calibration algorithm application can be applied by AEC. To achieve this, 79 patients is complaining of cervical pain oriented that shoulder forced traction bend use the before and after acquires lateral projection scout image and transverse image. transverse image of a fixed size in concern field of pixel and figure the average HU value compare that quantitative analysis. Artifact and pixel and resolution to qualitative clinical estimation image analysis. the patient feel inconvenience degree that self-diagnosis survey that estimate. As a result, lateral projection scout image if you used shoulder forced traction bend for the depicted has been an increase in the number of a cervical vertebrae. transverse image concern field shoulder forced traction bend use the before and after for pixel and the average HU-value changes was judged to be almost irrelevant. Artifact and resolution and contrast, in qualitative analysis of the results relating the observer to the unusual result. So, the patients of 82.27% complained discomfort that use of shoulder forced traction bend in self-diagnosis survey. No merit of medical image by using of bend from result was analyzed quality of image to quantitative and qualitative method judged. Nowadays, CT is supplied possible revision of quality of radiation by reduction of slice and automatic exposure controller, etc and application of preconditioning filter process due to various mathematic revision algorithm. So, image noise by beam hardening artifact should not be a problem. shoulder forced traction bend of use no longer judged clinically availability because have not influence of image quality and give discomfort, have extra dangerousness.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.121-137
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• 2019

De-identification is a method by which the remaining information can not be referred to a specific individual by removing the personal information from the data set. As a result, de-identification can lower the exposure risk of personal information that may occur in the process of collecting, processing, storing and distributing information. Although there have been many studies in de-identification algorithms, protection models, and etc., most of them are limited to structured data, and there are relatively few considerations on de-identification of unstructured data. Especially, in the medical field where the unstructured text is frequently used, many people simply remove all personally identifiable information in order to lower the exposure risk of personal information, while admitting the fact that the data utility is lowered accordingly. This study proposes a new method to perform de-identification by applying the k-anonymity protection model targeting unstructured text in the medical field in which de-identification is mandatory because privacy protection issues are more critical in comparison to other fields. Also, the goal of this study is to propose a new utility metric so that people can comprehend de-identified data set utility intuitively. Therefore, if the result of this research is applied to various industrial fields where unstructured text is used, we expect that we can increase the utility of the unstructured text which contains personal information.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.619-625
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• 2023

In this paper, the remote care of medical beds with multiple body pressure sensors is described. Falling is one of the factors that seriously threaten the safety of patients and harm their health. In this study, a new bed was developed to overcome this. The bed system consists of a keyboard that can operate, a keyboard controller that manages the movement of the keyboard, a sensor that measures body pressure, a sensor controller that transmits and receives sensor values, a main controller that checks it and operates automatically or manually according to the algorithm, and a server that oversees all these information. The bed system checks the patient's location through a sensor and wirelessly alerts the server through the main controller when the patient determines that there is a risk of falling, so that the nurse or nurse can recognize the patient's dangerous condition. The server may receive state data transmitted from the wired/wireless terminal to monitor whether the bed system is operating normally. The controller of the keyboard operates a keyboard-type mechanism and automatically controls the prevention of bedsores connected by body pressure sensors to physically separate the area to which the patient's pressure is applied to prevent bedsores. The main controller checks the presence of the patient's bed and transmits it to the server. In conclusion, the proposed system can smart monitor the user's state and perform remote care.

• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.43-52
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• 2024

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.