• 한국콘텐츠학회논문지
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• 제17권12호
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• pp.158-168
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• 2017

빅데이터 관련 기술의 발전으로 공공 보건 분야 등을 필두로 데이터에 기반한 정책을 결정하는 체계적인 방법론에 대한 관심이 증가하고 있다. 본 연구는 데이터를 기반으로 국가, 사회의 주요 이슈를 지능적으로 탐지하기 위해서 도메인 전문가와의 협업을 통해 이슈 탐지 모델을 개발하는 방법을 제안한다. 우선, '해외 발생 감염병 국내 유입' 이슈를 대상으로 이슈에 영향을 주는 요인을 도출하고, 영향 요인을 대표하는 변수 들을 설정한다. 다음으로 시스템 다이내믹스 기법을 이용하여 각 영향요인 간의 인과 분석을 통해 인과지도를 구성하여 영향력 높은 주요 요인들을 찾아낸다. 이 과정에서는 데이터 분석가와 감염병 도메인의 전문가와의 협업을 통해 실증적인 모델링을 진행한다. 이러한 도메인 지식 기반 이슈 탐지 모델을 기반으로 하여 상시 모니터링이 가능한 이슈 탐지 체계가 구축되면 더욱 효과적인 정책 의사 결정이 가능할 것이다.

• 천문학회보
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• 제42권2호
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• pp.53.3-53.3
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• 2017

The pilot study of SLGT (Superconducting Low-frequency Gravitational-wave Telescope) is being performed by KKN (KASI-KISTI-NIMS) collaboration. In this presentation, we discuss prospectives of detecting GWs in the low-frequency band (0.1-10 Hz), which is a target frequency band of SLGT, but can be hardly observed by advanced LIGO. IMBHBs (Intermediate Mass Black Hole Binaries) and IMRIs (Intermediate Mass Ratio Inspirals) with total masses of O(1000) up to O(10,000) solar masses are most probable sources between 0.1-10 Hz. We estimate horizon distances and signal to noise ratios of IMBHBs and IMRIs for different SLGT design sensitivities. Based on our calculations, detection rates for IMBHBs and IMRIs with SLGT will be discussed.

• Journal of Information Processing Systems
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• 제8권2호
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• pp.359-374
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• 2012

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

• Smart Structures and Systems
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• 제6권5_6호
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• 한국군사과학기술학회지
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• 제21권3호
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• pp.306-314
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• 2018

In maritime environment, it is necessary to understand the characteristics of sunglint since it may degrade the target detection performance of the infrared sensor mounted weapons. In this paper, sunglint in LWIR band is modeled using the slope distribution of the sea surface, and is verified by comparing the radiance of a simulated result with that of the real world. According to the simulation, sunglint is critical when the solar zenith angle is over $60^{\circ}$. The peak radiance of sunglint grows as the solar zenith angle increases until it reaches $83^{\circ}$ and has a large difference depending on the solar zenith angle when the wind speed is small. Finally, seasonal and temporal characteristics of sunglint effects are analyzed. In summer, sunglint is dominant in the horizon near the solar azimuth right after sunrise and before sunset. However, in winter, the influence of sunglint lasts even during the daytime since the elevation of the sun is much lower than in summer.

• 천문학회보
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• 제38권1호
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• pp.57.2-57.2
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• 2013

Based on N-body simulations, we find out that significant fraction of dynamically formed BH-BH (10 $M_{\odot}$ and NS-NS (1.4 $M_{\odot$ ecah) binaries are ejected from globular clusters. About 30 percent of compact stars are ejected in the form of binary. The merging time of ejected binary depends on the velocity dispersion of globular cluster. Some of ejected binaries have merging time-scales shorter than Hubble time and are expected to produce gravitational waves that can be detectable by the advanced ground-based interferometers. The merger rates of ejected BH-BH and NS-NS binaries per globular cluster are estimated to be 3.5 and 17 per Gyr, respectively. Assuming the spatial density of globular clusters as 8.4 $h^3$ clusters $Mpc^{-3}$ and extrapolating to the horizon distance of the advanced LIGO-Virgo network, we expect the detection rates solely attributed to BH-BH and NS-NS with cluster origin are to be 42 and 1.7 $yr^{-1}$, respectively. Besides, we find out that BH-NS binary ejection hardly occurs in globular clusters and dynamically formed compact binaries may possibly be the source of short GRBs whose locations are far from host galaxies.

• Geomechanics and Engineering
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• 제25권1호
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• pp.59-73
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• 2021

There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• 전자공학회논문지SC
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• 제43권1호
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• pp.31-38
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• 2006

이 논문에서는 클러터 환경에서 최적의 거리와 거리방향 속도 추적을 위한 검출문턱값과 파형 파라미터의 one-step-ahead 제어를 제시하였다. 파형 파라미터의 최적 제어는 파형 파라미터에 대한 제약 조건이 있는 상황에서 추적 성능지수를 최소화하는 것이다. 성능지수는 항적분실확률과 추정오차 공분산행렬의 함수로 표현하였다. 항적분실확률과 오차 공분산행렬은 하이브리드 알고리듬을 이용하여 얻었다. 거짓 검출과 클러터 간섭의 영향은 이 성능 예측 알고리듬에서 함께 고려되었다. 여기서 제안한 one-step-ahead 제어의 추적 성능을 여러 가지 수치실험을 통해 확인하였고, 이 제어 방법은 유한 구간의 최적화 결과로부터 얻어진 경험적인 방법이다.

• 대한교통학회지
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• 제25권2호
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• pp.145-155
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• 2007

본 연구는 출발시각기준 실시간 통행시간 정보를 제공을 위한 공간적 설계의 적절성을 판단하는 것이다. 그리고 적정 링크길이를 평가하기 위해 실시간 통행시간 정보제공 오차를 정의하고, 기존의 방법보다 개선된 실시간 통행시간 제공방법을 제시하였다. 이를 연구대상에 적용한 결과 첫째, 실시간 통행시간 정보제공을 위한 공간적 설계는 경로단위보다는 링크단위가 오차가 적은 것으로 나타났다. 둘째, 실시간 통행시간 정보 제공방법 비교에서는 기존방법보다 본 연구에서 제시한 방법(동적특성 반영)이 오차가 적은 것으로 나타났다. 그리고 셋째, 적정 링크길이는 약 4km가 적정한 것으로 나타났다. 결론적으로, 출발시각기준 실시간 통행시간 정보제공을 위한 공간적 설계는 동적특성을 반영한 링크단위로 적용하는 것이 효과적이다.