• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.77-88
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• 2008

This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.158-168
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• 2017

As the big data technologies advance, there is an increasing interest in systematic methodologies for data-based policy determination especially in the public health area. This study proposes a method to develop an issue detection model through the collaboration with domain experts in order to intelligently detect major socio-national issues on infectious diseases based on data. At first, the factors influencing the 'domestic inflow of foreign infectious diseases' are determined and variables representing the factors are set. Thereafter, by using system dynamics methods, the causal analysis is made to find causal map indicating main influential factors. In this process, an empirical modeling is conducted through collaboration between data analysts and experts in the infectious disease domain. The proposed issue detection approach based on domain knowledges will make it possible to make a decision on policies more efficiently if the detection system is capable of continuos monitoring of the related issues.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.351-360
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• 2013

Harmonic analysis enables to characterize patterns of variation in MODIS NDVI time series data and track changes in ground vegetation cover. In harmonic analysis, a periodic phenomenon of time series data is decomposed into the sum of a series of sinusoidal waves and an additive term. Each wave is defined by an amplitude and a phase angle and accounts for the portion of variance of complex curve. In this study, harmonic analysis was explored to tract ground vegetation variation through time for land-cover vegetation change detection. The process also enables to reconstruct observed time series data including various noise components. Harmonic model was tested with simulation data to validate its performance. Then, the suggested change detection method was applied to MODIS NDVI time series data over the study period (2006-2012) for a selected test area located in the northern plateau of Korean peninsula. The results show that the proposed approach is potentially an effective way to understand the pattern of NDVI variation and detect the change for long-term monitoring of land cover.

• Journal of Korean Society of Forest Science
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• v.100 no.3
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• pp.315-326
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• 2011

In recent years, domestic and international interests focus on climate change, and importance of forest as carbon sink have been also increased. Particularly REDD+ mechanism expanded from REDD (Reduced Emissions from Deforestation and Degradation) is expected to perform a new mechanism for reducing greenhouse gas in post 2012. To conduct this mechanism, countries which try to get a carbon credit have to certify effectiveness of their activities by MRV (Measuring, Reporting and Verification) system. This study analyzed the approaches for detecting land cover change and estimating carbon stock by remote sensing technology which is considered as the effective method to develop MRV system. The most appropriate remote sensing for detection of land cover change is optical medium resolution sensors and satellite SAR (Synthetic Aperture Radar) according to cost efficiency and uncertainty assessment. In case of estimating carbon stock, integration of low uncertainty techniques, airborne LiDAR (Light Detection and Ranging), SAR, and cost efficient techniques, optical medium resolution sensors and satellite SAR, could be more appropriate. However, due to absence of certificate authority, guideline, and standard of uncertainty, we should pay continuously our attention on international information flow and establish appropriate methods. Moreover, to apply monitoring system to developing countries, close collaboration and monitoring method reflected characteristics of each countries should be considered.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1331-1345
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• 2017

Binary instrumentation has been developed for monitoring and debugging executables without their source codes. Previous efforts on the binary instrumentation are mainly focused on its capability and accuracy, but not on efficiency for practical application. In particular, criteria and measurement methodologies for evaluating and comparing the efficiency of binary investigation tools and algorithms do not estimated yet. In this paper, we propose the instrumentation primitives which are a unit functionality and measurement methodology. Through the empirical experiments by adopting the proposed methodology on DynamoRIO and Pin, we show the feasibility of the proposal.

• Journal of Korean Society of Transportation
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• v.23 no.1
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• pp.83-92
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• 2005

This study if the first trial toward realizing a new methodology for vehicle re-identification based on heterogeneous sensor systems. A major interest of the author is how to effectively utilize information obtained from different sensors to derive accurate and reliable section travel times. The 'blade' sensor that is a newly developed sensor for capturing vehicle wheel information and the existing square loop sensor are employed to extract the inputs of the proposed vehicle re-identification algorithm. The fundamental idea of the algorithm developed in this study, which is so called 'anonumous vehicle re-identification,' it to match vehicle features obtained from both sensors. The results of the algorithm evaluation reveal that the proposed methodology could be successfully implemented in the field. The proposed methodology would be an invaluable tool for operating agencies in support of traffic monitoring systems and traveler information systems.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.361-363
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• 2000

최근 초고속 통신망의 원활한 관리와 운용을 위해 이질적인 통신망을 연동할 수 있게 하는 일관성있는 통신망 구조와 다양한 기술적 환경이 요구된다. 이런 추세에 따라 차세대 인터넷 네트워킹 서비스를 위한 분산 망 관리와 객체지향 개념 및 기존 통신 개념을 포함하는 TINA 구조를 기반으로한 망 관리 영역을 제시하고, 이들의 다양한 관리 영역별로 특수화된 정보 관리와 네트워크 간의 호환성 있는 정보 엑세스를 위한 인터페이스가 필요하다. 따라서 본 논문에서는 망 관리자들이 통합된 인터네트워킹 상태에서 웹 브라우저의 단일 인터페이스를 통해 네트워크 상태를 확인, 모니터링할 수 있는 실제적인 시스템 개발을 위한 분석과 설계 및 프로토타이핑에 초점을 맞추고 있다. 이를 위해 UML 방법론을 채택하고 이를 지원하는 CASE 도구를 이용해 분석과 설계를 수행하였으며, 망 관리자의 일관성 있는 투명한 관리를 위해서 CORBA와 Java 기반의 분산 객체 기술을 이용해 시스템을 개발하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.201-203
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• 2019

5G 시스템은 혁신적인 발전을 거듭하며 강력해진 기술 표준을 근간으로 초광대역 이동형 데이터 서비스, 대규모 사물통신 서비스 등을 제공하고 있다. 본 논문에서는 SW를 기반으로 한 5G의 기술 개발 방법론을 제안한다. 또, 실제 환경에서 차세대 모바일 네트워크의 성능 분석이 가능한 SW 기반의 시스템 레벨 테스트베드 및 모니터링 프로그램을 OAI(Openairinterface) 5G 연구 단체의 오픈소스를 이용하여 구현한다. 기존 하드웨어 위주의 단일 칩 시스템(SoC)을 이용한 구현은 손상되기가 쉽고 유지보수가 힘들다는 단점이 있다. 하지만 기존 하드웨어 칩을 소프트웨어 기반인 소스코드로 구현하면 유지 보수가 용이하다는 장점뿐만 아니라 하드웨어가 변경되더라도 다른 하드웨어에 호환되는 이식성 때문에 생산성이 높아지며 비용 절감의 효과도 기대할 수 있다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.77-77
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• 2021

가뭄은 사회·경제적으로 매우 큰 피해를 주는 자연재해이며, 그 시작과 발생 지역을 정확하게 예측하는 데 어려운 문제가 있다. 이에 수문 분야에서는 가뭄에 영향을 미치는 수문·기상인자들을 이용하여 다양한 가뭄지수를 개발하였고 이를 활용하여 가뭄 현상을 모니터링하고 예측 및 전망하는데 다양한 노력을 기울이고 있다. 하지만 가뭄지수들은 실제 가뭄이 어떠한 형태로 발생하는지 파악하기에 많은 한계점을 가지고 있다. 이에 최근 들어 미국과 유럽에서는 실제 농업, 환경, 에너지 등과 같은 다양한 분야에 걸쳐 가뭄 피해로 인해 생기는 가뭄 영향을 보다 체계적이고 상세한 데이터 인벤토리로 구축하고 가뭄지수와의 상관관계, 회귀분석과 같은 연구를 통해 가뭄 영향 예측을 시도하고 있다. 따라서 본 연구에서는 보고서, 데이터베이스, 웹 크롤링(Web-Crawling)을 통한 뉴스 기사 등과 같은 자료를 수집하여 국내 가뭄 영향 인벤토리를 구축하였다. 또한 수문 분야에 널리 사용되고 있는 가뭄지수인 표준 강수 증발산량지수 SPEI(Standardized Precipitation-Evapotranspiration Index)를 기반으로 지역에 따른 가뭄 영향을 예측하기 위해 최근 로지스틱 회귀모형, Random forest, Support vector machine, XGBoost 등의 다양한 머신러닝 기법을 적용하였다. 각 모형의 성능을 Receiver Operating Characteristic(ROC) 곡선을 통해 평가하여 가뭄 영향 예측에 적절한 머신러닝 기법을 제시하였다. 본 연구 결과를 통해 텍스트 기반의 가뭄 영향 자료와 머신러닝 기법을 통한 가뭄 영향 예측 방법론은 가뭄 재난 관리에 유용한 정보를 제공할 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.