• Journal of The Korean Digital Architecture Interior Association
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• v.8 no.2
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• pp.13-20
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• 2008

As buildings have been constructed higher and more complicated recently, the activities of the residents occurred in those multiplex buildings have also become more various. As a result, possibility and the size of the damage from the disaster like a fire are getting larger. So, many studies for preventing the damage in refuge situation are being conducted. In this study, a new process for finding the optimum refuge path is presented, which is different from existing methods. This new method operates by using the human movement detection system in the building for real time. And the process also shows the new way which can shorten the number of calculation for deciding the optimum refuge path. That new way is to make variables such as the velocity of smoke and person movement into a constant. Finally it will be applied to a multiplex building.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Structural Engineering and Mechanics
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• v.38 no.6
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• pp.715-732
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• 2011

A novel structural damage detection method with a new damage index has been recently proposed by the authors based on the statistical moments of dynamic responses of shear building structures subject to white noise ground motion. The statistical moment-based damage detection (SMBDD) method is theoretically extended in this paper with general application. The generalized SMBDD method is more versatile and can identify damage locations and damage severities of many types of building structures under various external excitations. In particular, the incomplete measurements can be considered by the proposed method without mode shape expansion or model reduction. Various damage scenarios of two general forms of building structures with incomplete measurements are investigated in consideration of different excitations. The effects of measurement noise are also investigated. The damage locations and damage severities are correctly identified even when a high noise level of 15% and incomplete measurements are considered. The effectiveness and versatility of the generalized SMBDD method are demonstrated.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.30-35
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• 2008

This paper describes a fault detection and diagnosis (FDD) system developed for the heat source apparatus in building air-conditioning system. As HVAC&R systems in building become complex and instrumented with highly automated controllers, the processes and systems get more difficult for the operator to understand and detect the mal-functions. Poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of energy used in commercial building. When operating a complex facility, FDD system is beneficial in equipment management to provide the operator with tools which can help in decision making for recovery from a failure of the system. Automated FDD for HVAC&R system has the potential to reduce energy and maintenance costs and improves comfort and reliability. Over the last decade there has been considerable research for developing FDD system for HVAC&R equipment. However, they are being made too much of a theoretical study, so only a small of FDD methods are deployed in the field. This study deduced an actual defect source for the heat source apparatus and suggested a low price FDD method which is ready to be deployed in the field.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2748-2754
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• 2022

Because existing reactant coolant system (RCS) leakage detection mechanisms are insensitive to small leaks, a real-time, direct detection system with a detection threshold below 0.5 gpm·hr^-1 was studied. A beta-ray detection system using a silicon detector with good energy resolution for beta rays and a low gamma-ray response was proposed. The detection performance in the leakage condition was evaluated through experiments and simulations. The concentration of ¹⁶N in the coolant corresponding to a coolant leakage of 0.5 gpm was calculated using the analytic method and ORIGEN-ARP. Based on the concentration of ¹⁶N and the measurement of the silicon detector with ⁹⁰Sr/⁹⁰Y, the beta-ray count rate was estimated using MCNPX. To evaluate the effect of gamma rays inside the containment building, the signal-to-noise ratio (SNR) was calculated. To evaluate the count rate ratio, the radiation field inside the containment building was simulated using MCNPX, and response evaluation experiments were performed using beta and gamma rays on the silicon detector. The expected beta-ray count rate at 0.5 gpm leakage was 7.26 × 10⁵ counts/sec, and the signal-to-background count rate ratio exceeded 88 for a transport time of 10 s, demonstrating its suitability for operation inside a reactor containment building.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.1
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• pp.124-132
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• 2013

Damage location and extent could be detected by the inverse analysis on dynamic response of the damaged structure. In general, detection of damage location is possible by the observation of the mode shape difference between undamaged and damaged structure and assessment of stiffness reduction is possible by the observation of the natural frequency difference of them. The study on damage detection by the dynamic response in civil structures is reported enough and in practical use, but in building structures it is reported seldom due to several problems. The purpose of this study is to present the damage detection method on shear building structures by mode shape. The damage location index using 1st mode shape is observed theoretically to find out damage location. The damage detection method is applied to numerical analysis model such as MATLAB and MIDAS GENw for the verification. Finally the shaking table test on 3 story shear building is performed for the examination of the damage detection method. In shaking table results, as the story stiffness decrease by 25% the 1st mode frequency increase by 12%, and the damage location index represents minus at damaged story.

• Journal of Korean Tunnelling and Underground Space Association
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• v.20 no.6
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• pp.1161-1175
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• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• 한국디지털정책학회:학술대회논문집
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• 2007.06a
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• pp.333-339
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• 2007

To preventand minimize damages from disaters, it is necessary to build a fire detection or a guide system that can gather and organize proper information and that can analyze about disasters. This research aims at suggestion of fire detection and guide system in building on the base of sensor network. To do so, it examines the effects of this suggesting model system based urban disaster management system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.173-174
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• 2023

In this paper, we introduce a model for detecting Personal Protective Equipment (PPE) using YOLO (You Only Look Once), an object detection neural network. PPE is used to maintain a safe working environment, and proper use of PPE protects workers' safety and health. However, failure to wear PPE or wearing it improperly can cause serious safety issues. Therefore, a PPE detection system is crucial in industrial settings.