• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.3
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• pp.311-320
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• 2008

A LIDAR can rapidly generate 3D points by densely sampling the surfaces of targets using laser pulses, which has been efficiently utilized to reconstruct 3D models of the targets automatically. Due to this advantage, LIDARs are increasingly applied to the fields of Defense and Security, for examples, being employed to intelligently guided missiles and manned/unmanned reconnaissance planes. For the prior verification of the LIDAR applicability, this study aims at generating simulated LIDAR data. Here, we derived the sensor equation by modelling the geometric relationships between the LIDAR sub-modules, such as GPS, IMU, LS and the systematic errors associated with them. Based on this equation, we developed a program to generate simulated data with the system parameters, the systematic errors, the flight trajectories and attitudes, and the reference terrain model given. This program had been applied to generating simulated LIDAR data for urban areas. By analyzing these simulated data, we verified the accuracy and usefulness of the simulation. The simulator developed in this study will provide economically various test data required for the development of application algorithms and contribute to the optimal establishment of the flight and system parameters.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.603-608
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• 2023

본 연구는 한국어 Vision-Language Pre-training 모델 학습을 위한 대규모 시각-언어 멀티모달 데이터셋 구축에 대한 필요성을 연구한다. 현재, 한국어 시각-언어 멀티모달 데이터셋은 부족하며, 양질의 데이터 획득이 어려운 상황이다. 따라서, 본 연구에서는 기계 번역을 활용하여 외국어(영문) 시각-언어 데이터를 한국어로 번역하고 이를 기반으로 생성형 AI를 활용한 데이터셋 구축 방법론을 제안한다. 우리는 다양한 캡션 생성 방법 중, ChatGPT를 활용하여 자연스럽고 고품질의 한국어 캡션을 자동으로 생성하기 위한 새로운 방법을 제안한다. 이를 통해 기존의 기계 번역 방법보다 더 나은 캡션 품질을 보장할 수 있으며, 여러가지 번역 결과를 앙상블하여 멀티모달 데이터셋을 효과적으로 구축하는데 활용한다. 뿐만 아니라, 본 연구에서는 의미론적 유사도 기반 평가 방식인 캡션 투영 일치도(Caption Projection Consistency) 소개하고, 다양한 번역 시스템 간의 영-한 캡션 투영 성능을 비교하며 이를 평가하는 기준을 제시한다. 최종적으로, 본 연구는 ChatGPT를 이용한 한국어 멀티모달 이미지-텍스트 멀티모달 데이터셋 구축을 위한 새로운 방법론을 제시하며, 대표적인 기계 번역기들보다 우수한 영한 캡션 투영 성능을 증명한다. 이를 통해, 우리의 연구는 부족한 High-Quality 한국어 데이터 셋을 자동으로 대량 구축할 수 있는 방향을 보여주며, 이 방법을 통해 딥러닝 기반 한국어 Vision-Language Pre-training 모델의 성능 향상에 기여할 것으로 기대한다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.200-206
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• 2010

본 논문은 풍력발전 시스템의 하부 지지 구조물인 타워의 플랜지 연결부 설계 평가를 위한 플랜지 모델건전성 평가 기법에 대해 다룬다. 일반적으로 풍력발전 시스템 타워의 연결부는 Ring-형 플랜지의 형태를 가지고 있다. 이러한 ring-형 플랜지에 대한 설계 기준 및 방법은 풍력 발전 시스템 기술기준 등 에 명시되어있다. 이러한 설계 기준을 따르는 플랜지 연결부에 대해 구조 및 체결 볼트의 건전성 평가를 위해 하중평가 전용 프로그램인 GH-Bladed 3.8를 통해 생성된 하중 데이터를 유한요소 범용 프로그램인 Ansys 12.0에 접목하여 구조해석을 수행 하였다. 해석 방법은 풍력발전시스템의 타워를 셸 요소로 모델링하여 계산한 해석 결과를 플랜지 모델의 경계면에 적용 시켜 해석하는 submodeling 기법과 타워를 빔의 형태로 단순화 화여 계산한 거동 결과를 플랜지 모델에 적용하는 기법을 사용 하였다. 이 두 가지의 해석 기법으로 도출된 결과의 비교를 통하여 해석 결과 신뢰성을 평가하고 효율적이고 합리적인 방법을 제시하고자 하였다.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.218-223
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• 2017

This paper deals with a fault diagnosis algorithm using the Kalman filter for a 75-ton Liquid Propellant Rocket Engine (LPRE). To design the Kalman filter, we linearized a non-linear simulation model of a 75-ton LPRE at an operating point, and checked the performance of the Kalman filter by comparing the measured values with estimated values of the states. Then, we artificially injected a fault of the turbopump efficiency into the simulation to confirm the performance of the fault diagnosis algorithm with the developed Kalman filter by comparing the variation of the residuals of the normal state with that of the fault cases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.151-159
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• 2006

This paper studied active response policy generation scheme in intrusion detection system. We considered seven requirements of intrusion detection system for active response with components as the preceding study We presented the scheme which I can generate signature with a base with integrate one model with NIDS and ADS. We studied detection of the Unknown Attack which was active, and studied scheme for generated to be able to do signature automatically through Unknown Attack detection.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.

• Journal of the Korean Magnetics Society
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• v.20 no.4
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• pp.149-152
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• 2010

The flat coil actuator is widely used to make high precision products because it has no friction between the moving coil and the guide. Finite Element Method, a favored actuator design tool due to its high accuracy, was utilized to analyze the electromagnetic actuator, but it consumes a lot of time especially in computation iterations for optimization. Accordingly, the magnetic equivalent circuit analysis can be an alternative tool to FEM because of its computation iteration capability with fair accuracy. In this paper, lumped parameter model and the simulation results are presented. In addition, the result of lumped parameter analysis is compared with those obtained from finite element analysis for verification.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.11a
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• pp.150-153
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• 2006

Gas generator should be adopted either fuel rich or oxidizer rich combustion because of the temperature restriction to avoid any possible thermal damages to turbine blade. This study focuses to model the non-equilibrium chemical reaction of kerosene/LOx with detailed kinetics developed by Dagaut using Perfectly stirred reactor(PSR) assumption. To predict more reliable species fraction and other gas properties, Frenklach's soot model was added to Dagaut's detailed kinetics.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.140-146
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• 2021

As the environment using intelligent IoT devices increases, various studies are underway to ensure the integrity of information sent and received from intelligent IoT devices. However, all IoT information generated in heterogeneous environments is not fully provided with reliable protocols and services. In this paper, we propose an intelligent-based multi-blockchain model that can extract only critical information among various information processed by intelligent IoT devices. In the proposed model, blockchain is used to ensure the integrity of IoT information sent and received from IoT devices. The proposed model uses the correlation index of the collected information to trust a large number of IoT information to extract only the information with a high correlation index and bind it with blockchain. This is because the collected information can be extended to the n-tier structure as well as guaranteed reliability. Furthermore, since the proposed model can give weight information to the collection information based on blockchain, similar information can be selected (or bound) according to priority. The proposed model is able to extend the collection information to the n-layer structure while maintaining the data processing cost processed in real time regardless of the number of IoT devices.