• Journal of Information Processing Systems
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• 제19권5호
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• pp.602-613
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• 2023

High-quality image datasets are in high demand for various applications. With many online sources providing manually collected datasets, a persisting challenge is to fully automate the dataset collection process. In this study, we surveyed an automatic image dataset generation field through analyzing a collection of existing studies. Moreover, we examined fields that are closely related to automated dataset generation, such as query expansion, web scraping, and dataset quality. We assess how both noise and regional search engine differences can be addressed using an automated search query expansion focused on hypernyms, allowing for user-specific manual query expansion. Combining these aspects provides an outline of how a modern web scraping application can produce large-scale image datasets.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1324-1327
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• 2009

Social infrastructure is the basis of public welfare and should be recognized and managed as important assets. Bridge is one of the most important infrastructures to be managed systematically because the impact of the failure is critical. It is essential to monitor the performance of bridges in order to manage them as an asset. But current analytical methods such as predictive modeling and structural analysis are very complicated and difficult to use in practice. To apply these methods, structural and material condition data collection should be performed in each element of bridge. But it is difficult to collect these detailed data in large numbers and various kinds of bridges. Therefore, it is necessary to collect data of major measurement items and predict the life of bridges roughly with advanced information technologies. When certain measurement items reach predefined limits in the monitoring bridges, precise performance measurement will be done by detailed site measurement. This paper describes the selection of major measurement items that can represent the tendency of bridge life and introduces automated bridge data collection test-bed using wireless sensor network technology. The following will be major parts of this paper: 1) Examining the features of conventional bridge management system and data collection method 2) Mileage concept as a bridge life indicator and measuring method of the indicator 3) Test-bed of automated and real-time based bridge life indicator monitoring system using wireless sensor network

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.40-44
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• 2002

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs(Ground Control Points). Many applications of remote sensing data, such as change detection, mapping and environmental monitoring, rely on the accuracy of precision correction. However it is a very time consuming and laborious process. It requires GCP collection, the identification of image points and their corresponding reference coordinates. At typical satellite ground stations, GCP collection requires most of man-powers in processing satellite images. A method of automatic registration of satellite images is demanding. In this paper, we propose a new algorithm for automatic precision correction by GCP chips and RANSAC(Random Sample Consensus). The algorithm is divided into two major steps. The first one is the automated generation of ground control points. An automated stereo matching based on normalized cross correlation will be used. We have improved the accuracy of stereo matching by determining the size and shape of match windows according to incidence angle and scene orientation from ancillary data. The second one is the robust estimation of mapping function from control points. We used the RANSAC algorithm for this step and effectively removed the outliers of matching results. We carried out experiments with SPOT images over three test sites which were taken at different time and look-angle with each other. Left image was used to select UP chipsets and right image to match against GCP chipsets and perform automatic registration. In result, we could show that our approach of automated matching and robust estimation worked well for automated registration.

• 정보보호학회논문지
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• 제9권3호
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• pp.13-26
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• 1999

인터넷의 급속한 발전과 함께 정보 시스템의 보안 위협인 침입 사고도 급증하고 있다. 보다 강 화된 보안 메커니즘이 요구되고 있다. 시스템 로그 분석은 이런 침입 사실을 탐지하고 침입 자를 추적하 기 위해 필수적인 과정이나 로그 자료의 종류와 형태의 다양함으로 인해 자동화된 로그 수 집 및 분석이 현실적으로 어려운 상태이다. 우리는 침입 추적에 필요한 로그 자료의 형태를 정의하고 방 대한 로그 자 료로부터 효율적으로 로그수집, 분석할수 있는 도구를 설계 및 구현하였다. 이 논문에서는 개발된 도구 를 사용하여 실제 침입 추적을 한 경험을 소개하고 도구의 향후 개선 방향을 제시한다 Widespread use of Internet despite numerous positive aspects resulted in increased number of system intrusions and the need for enhanced security mechanisms is urgent. Systematic collection and analysis of log data are essential in intrusion investigation. Unfortunately existing logs are stored in diverse and incompatible format thus making an automated intrusion investigation practically impossible. We examined the types of log data essential in intrusion investigation and implemented a tool to enable systematic collection and efficient analysis of voluminous log data. Our tool based on RBDMS and SQL provides graphical and user-friendly interface. We describe our experience of using the tool in actual intrusion investigation and explain how our tool can be further enhanced.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.329-330
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• 2020

본 논문에서는 Azure 클라우드 플랫폼의 가상서버 호스팅을 이용해 데이터 수집 환경을 구축하고, Azure에서 제공하는 자동화된 기계학습(Automated Machine Learning, AutoML)을 기반으로 데이터 분석 방법에 관한 연구를 수행했다. 가상 서버 호스팅 환경에 LAMP(Linux, Apache, MySQL, PHP)를 설치하여 데이터 수집환경을 구축했으며, 수집된 데이터를 Azure AutoML에 적용하여 자동화된 기계학습을 수행했다. Azure AutoML은 소모적이고 반복적인 기계학습 모델 개발을 자동화하는 프로세스로써 기계학습 솔루션 구현하는데 시간과 자원(Resource)를 절약할 수 있다. 특히, AutoML은 수집된 데이터를 분류와 회귀 및 예측하는데 있어서 학습점수(Training Score)를 기반으로 보유한 데이터에 가장 적합한 기계학습 모델의 순위를 제공한다. 이는 데이터 분석에 필요한 기계학습 모델을 개발하는데 있어서 개발 초기 단계부터 코드를 설계하지 않아도 되며, 전체 기계학습 시스템을 개발 및 구현하기 전에 모델의 구성과 시스템을 설계해볼 수 있기 때문에 매우 효율적으로 활용될 수 있다. 본 논문에서는 NPU(Neural Processing Unit) 학습에 필요한 데이터 수집 환경에 관한 연구를 수행했으며, Azure AutoML을 기반으로 데이터 분류와 회귀 등 가장 효율적인 알고리즘 선정에 관한 연구를 수행했다.

• Advances in robotics research
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• 제2권3호
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• pp.201-217
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• 2018

Agriculture production is a critical human intensive task, which takes place in all regions of the world. The process to grow and harvest crops is labor intensive in many countries due to the lack of automation and advanced technology. Much of the difficult, dangerous and dirty labor of crop production can be automated with intelligent and robotic platforms. We propose an intelligent, agent-oriented robotic team, which can enable the process of harvesting, gathering and collecting crops and fruits, of many types, from agricultural fields. This paper describes a novel robotic organization enabling humans, robots and agents to work together for automation of gathering and collection functions. The focus of the research is a model, called HARMS, which can enable Humans, software Agents, Robots, Machines and Sensors to work together indistinguishably. With this model, any capability-based human-like organization can be conceived and modeled, such as in manufacturing or agriculture. In this research, we model, design and implement a technology application of knowledge-based robot-to-robot and human-to-robot collaboration for an agricultural gathering and collection function. The gathering and collection functions were chosen as they are some of the most labor intensive and least automated processes in the process acquisition of agricultural products. The use of robotic organizations can reduce human labor and increase efficiency allowing people to focus on higher level tasks and minimizing the backbreaking tasks of agricultural production in the future. In this work, the HARMS model was applied to three different robotic instances and an integrated test was completed with satisfactory results that show the basic promise of this research.

• 대한원격탐사학회지
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• 제37권3호
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• pp.463-473
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• 2021

본 연구에서는 최근 USGS에서 공개한 Landsat 8 Collection 2 Level 2 Science Product (L2SP) 위성 영상을 이용하여 국내 지표면 온도를 산출하였고, 기존 Landsat 8 Collection 1 Level 1 Terrain Precision (L1TP) 위성 영상을 활용하여 산출한 국내 지표면 온도와의 비교와 기상청 종관기상관측자료(Automated Synoptic Observing System, ASOS)와의 검증을 통해 L2SP 기반 지표면 온도 자료의 국내 영역에 대한 적합성을 평가하고자 하였다. L2SP는 연구 및 분석에 용이하도록 Landsat 8 Collection 2 Level 1 데이터를 기반으로 만든 Level 2 자료로, 기존의 계산식을 통해 산출해야 하는 지표면 반사도 자료와 지표면 온도 자료를 계산 처리 없이 바로 사용할 수 있다는 장점이 있다. 2018년부터 2020년까지 3년간의 Landsat 8 지표면 온도 산출물과 관측소 지점 8개소 주변 3×3 격자 영역과의 비교한 결과, 8개 관측소 기준 L2SP 지표면 온도와 L1TP 지표면 온도의 평균 피어슨 상관계수(Pearson correlation coefficient)는 각각 0.971, 0.964로 두 자료 모두 상당히 강한 양의 상관관계를 보여주었으며, RMSE (Root Mean Square Error)의 경우 각각 4.029℃, 5.247℃로 L2SP 지표면 온도 자료가 더 낮은 RMSE를 보여주는 것을 확인 하였다. 이는 관측소 위치별로 값에 차이가 생길 수 있지만 평균적인 지표 결과를 보았을 때, L2SP 지표면 온도 자료가 L1TP를 통해 산출되는 지표면 온도 자료와 비교했을 때 준수하거나 그 이상의 정확도를 보여주어 국내 지표면 온도 산출 연구에 적합하다고 판단된다. 따라서 향후 계절적 요인과 고도에 따른 온도 차이 등의 환경 및 지형적인 요인도 고려를 하거나, 본 연구 과정에서 발생한 Science Product의 고정적인 영상 품질 문제 등이 개선된다면 보다 더 안정적이고 정확도 높은 지표면 온도 참조 자료로써의 유용성이 클 것이라 판단된다.

• 정보보호학회논문지
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• 제28권4호
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• pp.869-878
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• 2018

사이버 공간 내 디지털 증거 데이터는 수정 및 삭제되기 쉬우며 실시간으로 변경사항이 반영되므로 사건 발생 시점 이후 증거 데이터의 빠른 획득이 필요하다. 클라이언트 측에서의 증거 수집은 별도의 행정절차로 인한 시간 지연 없이 데이터를 획득할 수 있다는 장점이 있지만, 대용량 데이터의 수집에 있어서는 마찬가지로 수집 시간 지연 문제에 취약하다. 따라서 본 논문에서는 사이버 공간 내 주요 웹 기반 서비스를 중심으로, 클라이언트 측면에서의 자동화 된 증거 수집 방식을 제안하여 대용량 데이터에 대한 효율적인 증거 수집이 가능하도록 한다. 나아가 제안한 방식을 사용하고 수집한 디지털 증거의 법정제출시점까지의 무결성을 보장하는 사이버 공간 내 디지털 증거 수집 시스템을 제안한다.

• 한국공간정보시스템학회 논문지
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• 제11권2호
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• pp.45-53
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• 2009

The fast progress on multimedia data acquisition technologies has enabled collecting vast amount of videos in real time. Although the amount of information gathered from these videos could be high in terms of quantity and quality, the use of the collected data is very limited typically by human-centric monitoring systems. In this paper, we propose a framework for analyzing long traffic video using series of content-based analyses tools. Our framework suggests a method to integrate theses analyses tools to extract highly informative features specific to a traffic video analysis. Our analytical framework provides (1) re-sampling tools for efficient and precise analysis, (2) foreground extraction methods for unbiased traffic flow analysis, (3) frame property analyses tools using variety of frame characteristics including brightness, entropy, Harris corners, and variance of traffic flow, and (4) a visualization tool that summarizes the entire video sequence and automatically highlight a collection of frames based on some metrics defined by semi-automated or fully automated techniques. Based on the proposed framework, we developed an automated traffic flow analysis system, and in our experiments, we show results from two example traffic videos taken from different monitoring angles.

• Bulletin of the Korean Chemical Society
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• 제32권6호
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• pp.2039-2044
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• 2011

A semi-continuous and automated method for quantifying atmospheric ammonia at the parts per billion level has been developed. The instrument consists of a high efficiency diffusion scrubber, an electrolytic on-line anion exchange device, and a conductivity detector. Water soluble gases in sampled air diffuse through the porous membrane and are absorbed in an absorbing solution. Interferences are eliminated by using an anion exchange devises. The electrical conductivity of the solution is measured without chromatographic separation. The collection efficiency was over 99%. Over the 0-200 ppbv concentration range, the calibration was linear with $r^2$ = 0.99. The lower limit of detection was 0.09 ppbv. A parallel analysis of Seoul air over several days using this method and a diffusion scrubber coupled to an ion chromatography system showed acceptable agreement, $r^2$ = 0.940 (n = 686). This method can be applied for ambient air monitoring of ammonia.