• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.156-156
• /
• 2021

본 연구는 지역빈도해석을 기반으로한 인공신경망 모델과 기존에 널리 사용되는 방법인 홍수지수법의 성능을 몬테카를로 시뮬레이션을 이용하여 평가하였다. 컴퓨터 기술이 발달함에 따라 인공지능에 대한 접근성이 좋아지며 수문학을 포함한 다양한 분야에 적용되고 있다. 인공지능을 이용하여 강수량 및 유량 등 다양한 수문자료에 대한 예측이 이루어지고 있으나 빈도해석에 관한 연구는 비교적 적다. 본 연구에서 사용된 인공 지능 모델은 대상 지점의 지형학적 자료와 수문학적 자료를 이용하여 인공신경망을 통해 지점의 확률강우량(QRT-ANN) 및 확률분포형의 매개변수 (PRT-ANN)를 추정한다. 지형학적 자료로는 위도, 경도 그리고 고도가 사용되었으며 수문학적 자료로는 대상 지점의 최근 30년 일일연최대강우량을 사용하였다. 지역빈도해석의 정확도는 지역 내 통계적 특성이 비슷한 지점들이 포함되면 될수록 높아진다. 통계적 특성으로는 불일치 척도, 이질성 척도, 적합성 척도가 있으며 다양한 조건의 통계적 특성에 따른 세 개의 지역빈도해석 방법의 성능을 평가하고자 하였다. 대상 지역 내 n개의 지점이 있다고 가정하였을 때, 홍수지수법의 경우 n-1개의 지점으로 추정한 지역 성장곡선을 이용하여 나머지 1개 지점의 확률강우량을 산정할 수 있으며 인공신경망 모델들 또한 n-1개 지점들의 자료를 이용하여 모델을 구축한 뒤 나머지 지점의 확률강우량 및 확률분포형의 매개변수를 예측할 수 있다. PRT-ANN의 경우 예측된 매개변수를 이용하여 확률강우량을 산정하며 시뮬레이션 시행마다 발생시킨 자료의 지점빈도해석 결과에 대한 나머지 세 방법의 평균 제곱근 상대오차 (Relative root mean square error, RRMSE)를 계산하였다. 몬테카를로 시뮬레이션을 이용한 성능 분석을 통하여 관측값의 다양한 통계적 특성에 맞는 지역빈도해석 방법을 제시할 수 있을 것으로 판단된다.

• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.17-24
• /
• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.701-706
• /
• 2023

The Korean government and industry have presented a roadmap for the commercialization of UAM services and are promoting it in earnest. In order to introduce full-scale UAM services, there are various issues to be solved, such as the development of high-performance aircraft, the design of network bases and corridors, the optimization of operation management, and the establishment of related laws and systems. In this study, in terms of optimizing operation management, we will examine research trends by field, focusing on Korea, and derive research topics that need to be solved in the future. Korean researchers have suggested that research is centered on UAM service usage fees, usage intentions and acceptance models, and vertiport location selection, but operational optimization studies such as service order acceptance, aircraft repositioning, and battery charging and maintenance scheduling are needed in the future.

• Earthquakes and Structures
• /
• v.23 no.2
• /
• pp.129-138
• /
• 2022

One of the objectives to prevent building pounding between two adjacentstructures is to considerseparation distance or decrease relative displacement during seismic excitation. Although the majority of building codes around the world have basically suggested some equations or approximately recommended various distances between structuresto avoid pounding hazard, but a lot of reportsin zone of pounding have obviously shown thatsafety situation or economic consideration are not always provided due to the collisions between buildings and the cost of land, respectively. For this purpose, a dynamic MDOF model by having base isolation system is numerically considered and using various earthquake records, relative displacements are mathematically investigated. Different equations to determine the value of damping ratio are collected and the results of evaluations are listed for comparison among them to present a new equation for determination of impact damping ratio. Presented equation is depends significantly on impact velocity before and after impact based on artificial neural network, which the accuracy of them is investigated and also confirmed. In order to select the optimum equation, hysteresisloop of impact between base of building and rubber bumper is considered and compared with the hysteresis loop of each impact, calculated by different equations. Finally, using representative equation, the effect of thickness, number and stiffness of rubber bumpers are numerically investigated. The results of analysis indicate that stiffness and number of bumpers have significantly affected in zone of impact force while the thickness of bumpers have not shown significant influence to calculate impact force during earthquake. For instance, increasing the number of bumpers, gap size between structures and also the value of stiffness is caused to decrease impact force between models. The final evaluation demonstrates that bumpers are able to decrease peak lateral displacement of top story during impact.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2022.06a
• /
• pp.110-110
• /
• 2022

본 연구는 자율운항 선박의 원격 고장 진단 기법 개발의 일부로 수행되었다. 특히, 엔진 연료 계통 장비로부터 계측된 시계열 데이터로부터 상태 진단을 위한 알고리즘 구현 결과를 제시하였다. 엔진 연료 펌프와 청정기를 가진 육상 실험 장비로부터 진동 시계열 데이터 계측하였으며, 이상 감지, 고장 분류 및 고장 예측이 가능한 심층 학습(Deep Learning) 및 기계 학습(Machine Learning) 알고리즘을 구현하였다. 육상 실험 장비에 고장 유형 별로 인위적인 고장을 발생시켜 특징적인 진동 신호를 계측하여, 인공 지능 학습에 이용하였다. 계측된 신호 데이터는 선행 발생한 사건의 신호가 후행 사건에 영향을 미치는 특성을 가지고 있으므로, 시계열에 내포된 고장 상태는 시간 간의 선후 종속성을 반영할 수 있는 학습 알고리즘을 제시하였다. 고장 사건의 시간 종속성을 반영할 수 있도록 순환(Recurrent) 계열의 RNN(Recurrent Neural Networks), LSTM(Long Short-Term Memory models)의 모델과 합성곱 연산 (Convolution Neural Network)을 기반으로 하는 Conv1D 모델을 적용하여 예측 정확성을 비교하였다. 특히, 합성곱 계열의 RNN LSTM 모델이 고차원의 순차적 자연어 언어 처리에 장점을 보이는 모델임을 착안하여, 신호의 시간 종속성을 학습에 반영할 수 있는 합성곱 계열의 Conv1 알고리즘을 고장 예측에 사용하였다. 또한 기계 학습 모델의 효율성을 감안하여 XGBoost를 추가로 적용하여 고장 예측을 시도하였다. 최종적으로 연료 펌프와 청정기의 진동 신호로부터 Conv1D 모델과 XGBoost 모델의 고장 예측 성능 결과를 비교하였다

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.6
• /
• pp.1055-1065
• /
• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.99-105
• /
• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.383-388
• /
• 2024

This paper is a study to derive the predicted value of power generation based on the photovoltaic power generation data measured in Jeollanam-do, South Korea. Multivariate variables such as direct current, alternating current, and environmental data were measured in the inverter to measure the amount of power generation, and pre-processing was performed to ensure the stability and reliability of the measured values. Correlation analysis used only data with high correlation with power generation in time series data for prediction using partial autocorrelation function (PACF). Deep learning models were used to measure the amount of power generation to predict the amount of photovoltaic power generation, and the results of correlation analysis of each multivariate variable were used to increase the prediction accuracy. Learning using refined data was more stable than when existing data were used as it was, and the solar power generation prediction algorithm was improved by using only highly correlated variables among multivariate variables by reflecting the correlation analysis results.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.66 no.3
• /
• pp.39-51
• /
• 2024

To effectively implement the integrated water management policy outlined in the National Water Management Act, it is essential to analyze agricultural water supply and demand at both basin and water district levels. Currently, agricultural water is primarily distributed through open canal systems and controlled by floodgates, yet the utilization-to-supply ratio remains at a mere 48%. In the case of agricultural water, when analyzing water balance through existing national basin water resource models (K-WEAP, K-MODISM), distortion of supply and regression occurs due to calculation of regression rate based on the concept of net water consumption. In addition, by simplifying the complex and diverse agricultural water supply system within the basin into a single virtual reservoir, it is difficult to analyze the surplus or shortage of agricultural water for each field within the basin. There are limitations in reflecting the characteristics and actual sites of rural water areas, such as inconsistencies with river and reservoir supply priority sites. This study focuses on the development of a model aimed at improving the deficiencies of current water balance analysis methods. The developed model aims to provide standardized water balance analysis nationwide, with initial application to the Anseo standard watershed. Utilizing data from 32 facilities within the standard watershed, the study conducted water balance analysis through watershed linkage, highlighting differences and improvements compared to existing methods.

• IMMUNE NETWORK
• /
• v.22 no.6
• /
• pp.50.1-50.19
• /
• 2022

Autoreactive B cells are not entirely deleted, but some remain as immunocompetent or anergic B cells. Although the persistence of autoreactive B cells as anergic cells has been shown in transgenic mouse models with the expression of B cell receptor (BCR) reactive to engineered self-antigen, the characterization of naturally occurring anergic B cells is important to identify them and understand their contribution to immune regulation or autoimmune diseases. We report here that a low-level expression of CD138 in the splenic B cells marks naturally arising anergic B cells, not plasma cells. The CD138^int B cells consisted of IgM^lowIgD^high follicular (FO) B cells and transitional 3 B cells in homeostatic conditions. The CD138^int FO B cells showed an anergic gene expression profile shared with that of monoclonal anergic B cells expressing engineered BCRs and the gene expression profile was different from those of plasma cells, age-associated B cells, or germinal center B cells. The anergic state of the CD138^int FO B cells was confirmed by attenuated Ca²⁺ response and failure to upregulate CD69 upon BCR engagement with anti-IgM, anti-IgD, anti-Igκ, or anti-IgG. The BCR repertoire of the CD138^int FO B cells was distinct from that of the CD138^- FO B cells and included some class-switched B cells with low-level somatic mutations. These findings demonstrate the presence of polyclonal anergic B cells in the normal mice that are characterized by low-level expression of CD138, IgM downregulation, reduced Ca²⁺ and CD69 responses upon BCR engagement, and distinct BCR repertoire.