• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.248-255
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels. however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results are clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the firstpart are used to prepare a set of data for learning process. Tunnel behavior especially the displacements of the lining has been exclusively investigated for the back analysis.

• 한국생물공학회:학술대회논문집
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• 2004.07a
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• pp.47-84
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• 2004

Principal Component Analysis나 Partial Least Squares와 같은 다변량 통계 기법은 변수간의 correlation structure로부터 공정의 variance를 설명할 수 있는 latent variable를 얻고 이를 이용하여 공정을 효과적으로 modeling할 수 있는 방법으로 최근 들어 많은 관심을 얻고 있다. 하지만 PLS는 공정이 stationary state에 있다고 가정하기 때문에, 생물학적 공정의 non-stationary and time-varying behavior를 설명하기에 부적절하다. 본 논문에서는 PLS 알고리즘의 혐기성 폐수처리 공정에의 적용에 있어, 이와 같은 문제를 해결하기 위해서 adaptive PLS 알고리즘을 사용함으로써 변화하는 공정의 특성에 대응하여 모델을 update하는 방법을 이용하였다. 하지만 실시간 데이터로부터 adaptive PLS 방법을 적용하는 데에는 많은 어려움이 존재하며, 특히 outlier나 abnormal disturbance에 모델이 부적절하게 adaptation하는 문제가 발생할 수 있다. 따라서 이의 해결을 위해 adaptive PLS를 적용하는데 있어 robustness를 향상시키기 위해 monitoring index를 이용하여 abnormal data에 weight를 주고 안정적인 모델의 update가 가능하게 하는 방법을 제안하였으며, 이를 적용하여 성공적으로 혐기성 폐수처리 공정의 Output을 예측하고 효과적으로 공정을 모니터링할 수 있었다. 만들어진 PLS 모델은 산업폐수를 처리하기 위한 industrial plan에서 측정된 실제 데이터에 적용하여 그 효용성을 입증하였으며, 그 결과는 mechanistic model을 적용하기 힘든 실공정에 비교적 쉽게 implementation할 수 있는 장점이 있다.

• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2003.10a
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• pp.23-24
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• 2003

The goal of this study is to develop a biomarker used in monitoring abnormal behaviors of Japanese medaka (Oryzias latipes) as a model organism caused by hazardous chemicals. Japanese medaka was treated by copper and fluoranthene of appropriate sublethal concentrations after starvation for 48 hr. In this study we investigated neural toxicity of copper and fluoranthene in Japanese medaka (Oryzias latipes) along with comparative analysis of corresponding behavioral responses. The untreated individuals showed common behavioral characteristics (i.e., smooth and linear movements). Locomotive activity of the fish was monitored using an image processing and automatic data acquisition system. When treated with copper (100 ppb), the fish showed shaking patterns more frequently. As the concentration of copper increased to 1,000 ppb, activity decreased, and the fish showed an erratic movement. The treated with fluoranthene, however, showed stopping and abrupt change of orientation (100 ppb), and severely reduced locomotive activity and enhanced surfacing activity (1,000 ppb).

• Journal of IKEEE
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• v.13 no.2
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• pp.140-149
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• 2009

Traffic anomaly detection is one of important technology that should be considered in network security and administration. In this paper, we propose an abnormal traffic detection mechanism that includes traffic monitoring and traffic analysis. We develop analytical passive monitoring system called WISE-Mon which can inspect traffic behavior. We establish a criterion by analyzing the characteristics of a traffic training set. To detect abnormal traffic, we derive a hyperplane by using Fisher linear discriminant and chi-square distribution as well as the analyzed characteristics of traffic. Our mechanism can support reliable results for traffic anomaly detection and is compatible to real-time detection. In addition, since the trend of traffic can be changed as time passes, the hyperplane has to be updated periodically to reflect the changes. Accordingly, we consider the self-learning algorithm which reflects the trend of the traffic and so enables to increase the pliability of detection probability. Numerical results are presented to validate the accuracy of proposed mechanism. It shows that the proposed mechanism is reliable and relevant for traffic anomaly detection.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.10
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• pp.319-326
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• 2016

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. In this paper, we propose a new Kinect camera-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The moving-pigs are labeled as regions of interest. 3) A contour method is proposed and applied to solve the touching-pigs problem in the Kinect-depth image. The experimental results with the depth videos obtained from a pig farm located in Sejong illustrate the efficiency of the proposed method.

• Smart Structures and Systems
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• v.30 no.6
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• pp.661-671
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• 2022

Stayed cables are the key components for transmitting loads in cable-stayed bridges. Therefore, it is very important to evaluate the cable force condition to ensure bridge safety. An online condition assessment and anomaly localization method is proposed for cables based on the spatiotemporal correlation of grouped cable forces. First, an anomaly sensitive feature index is obtained based on the distribution characteristics of grouped cable forces. Second, an adaptive anomaly detection method based on the k-nearest neighbor rule is used to perform dissimilarity measurements on the extracted feature index, and such a method can effectively remove the interference of environment factors and vehicle loads on online condition assessment of the grouped cable forces. Furthermore, an online anomaly isolation and localization method for stay cables is established, and the complete decomposition contributions method is used to decompose the feature matrix of the grouped cable forces and build an anomaly isolation index. Finally, case studies were carried out to validate the proposed method using an in-service cable-stayed bridge equipped with a structural health monitoring system. The results show that the proposed approach is sensitive to the abnormal distribution of grouped cable forces and is robust to the influence of interference factors. In addition, the proposed approach can also localize the cables with abnormal cable forces online, which can be successfully applied to the field monitoring of cables for cable-stayed bridges.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.5
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• pp.445-458
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• 2014

The displacement data monitored during tunnel construction play a crucial role in predicting the behaviour of ground around and ahead of excavation face. However, the management criteria for monitoring data are not well established especially for the reliable analysis on varying aspect of displacement data along with chainage. In this study, we evaluated the applicability of x-MR control chart method, which is kind of applied statistical management method, for the analysis of displacement monitoring data in terms of prediction of possible collapse or induced cracks. As a result, a possible abnormal behaviour could be predicted beforehand at 5 ~ 13 m ahead or on at least one day before it occurred by using x-MR control chart method. In addition, it is noted that the moving range for the x-MR control chart should be set to 5~10 for this purpose.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1181-1191
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• 2021

The pandemic has rapidly developed a non-face-to-face environment. However, the sudden transition to a non-face-to-face environment has led to new security issues in various areas. One of the new security issues is the security threat of insiders, and the zero trust security model is drawing attention again as a technology to defend against it.. Software Defined Perimeter (SDP) technology consists of various security factors, of which device validation is a technology that can realize zerotrust by monitoring insider usage behavior. But the current SDP specification does not provide a technology that can perform device validation.. Therefore, this paper proposes a device validation technology using SVDD-based abnormal behavior detection technology through user behavior monitoring in an SDP environment and presents a way to perform the device validation technology in the SDP environment by conducting performance evaluation.

• Journal of the Korean Geotechnical Society
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• v.31 no.4
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• pp.19-29
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• 2015

Nowadays, the importance of the information management of construction sites to achieve the goal of safety construction. This management uses the collaborated analysis of in-situ monitoring data and numerical analysis, especially of an earth retaining structures of excavation sites. In this paper, the fractal theory was applied to actually monitored data from various excavation sites to develop the alternative interpolation technique which could predict the displacement behavior of unknown location around the monitoring locations and the future behavior of the monitoring locations with the steps of excavation. Data, mainly from inclinometer, were collected from various sites where retaining structures were collapsed during construction period, as well as from normal sites with the characteristics of geology, excavation method etc. In the analyses, Hurst exponent (H) was estimated with monitored periods using the Rescaled range analysis (R/S analysis) method applying the H in simulation processes. As the results of the analyses, Hurst exponents were ranged from 0.7 to 0.9 and showed the positive correlation of H > 1/2. The simulation processes, then, with the Hurst exponent estimated by Rescaled range analysis method showed reliable results. In addition, it was also expected that the variation of Hurst exponents with the monitoring period could instruct the abnormal behavior of an earth retaining structures to directors or operators. Therefore it was concluded that fractal theory could be applied for predicting the lateral displacement of unknown location and the future behavior of an earth retaining structures to manage the safety of construction sites during excavation period.

• Journal of the Korea Convergence Society
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• v.3 no.2
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• pp.7-12
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• 2012

An advanced and proactive response mechanism against diverse attacks should be proposed for enhance its security and reliability on android based commercial smart work device. In this study, we propose a user-oriented vulnerability analysis and response system on commercial smart work device based on android when diverse attacks are activated. Proposed mechanism uses simplified and optimized memory for monitoring and detecting the abnormal behavior on commercial smart work device, with which we can find and determine the attacker's attempts. Additionally, proposed mechanism provides advanced vulnerability analysis and monitoring/control module.