• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.431-433
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• 2021

In this paper, abnormal behavior monitoring system using YOLO AI platform was implemented and had superior response characteristics compared to the conventional monitoring system using two-shot detection by using one-shot detection of YOLO system. The YOLO platform was trained using image dataset composed of abnormal behaviors such as assault, theft, and arson. The abnormal behavior monitoring system consists of client and server and can be applicable to smart cities to solve various crime problems if it is commercialized.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.2
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• pp.92-98
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• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.492-500
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• 2014

Activity monitoring has been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior. In this research, we propose a data-driven activity monitoring method to measure relative sales performance which is not sensitive to special event which frequently occur in marketing area. Moreover, the proposed method can automatically updates the monitoring threshold that accommodates a drastically changing business environment. The results from simulation and practical case study from sales of electronic devices demonstrate the usefulness and applicability of the proposed activity monitoring method.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1519-1530
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• 2020

As livestock farms grow in size, the number of breeding individuals increases, making it difficult to manage livestock. Livestock farms require an integrated management system such as a monitoring system, an access control system, and an abnormal behavior detection system to manage livestock houses. In this paper, a smart korean cattle livestock management system using IoT and AI technology was proposed for livestock management in livestock farms. The smart korean cattle farm management system consists of a monitoring and control system, a vehicle access management system, and an abnormal cattle behavior detection system. It is expected that this will help manage large-scale livestock houses, and additional research is needed to improve the performance of abnormal behavior detection in the future.

• Journal of KIISE
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• v.41 no.12
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• pp.1152-1159
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• 2014

In this paper, we presents a system to ensure the safety of residents through appropriate action or alarm in case the residents occurs an emergency situation and abnormal activity. We collect and analysis real-time data of living environment of the residents using video and sensor. The existing system have been determined by using only the sensor data it have several problems. Our system attach camera to solve the existing system problem. We use weighted difference image and motion vector. The existing system, it takes about 48 hours to determine that an abnormal activity occurs. However, our system takes less than 1 hour.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.3
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• pp.129-138
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• 2005

This paper presents a methodology for the determination of optimal managing criteria for cable tension force in cable-stayed bridges using acceleration data acquired by monitoring system. There are many long span bridges installed with monitoring system in Korea. The monitoring systems are installed to diagnose abnormal behavior or damages in bridges and to warn these to bridge management agency. In cable-stayed bridges, the cable tension force could be an important indicator of abnormal behavior because of the geometric configuration of the cable-stayed bridge. If the management value of cable tension force is set too high or too low, then the monitoring system could not warn properly for the abnormal behavior of a bridge. Generally, the management value is set by empirical or engineering judgment, but in this paper, a new methodology for the determination of managing criteria for cable tension force is proposed based on the probability distribution model for tension force and reliability analysis. The proposed methodology is applied to a real concrete cable-stayed bridge in order to investigate its applicability.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.4
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• pp.441-455
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• 2015

In the present study, a face safety monitoring system was developed that will enable judging collapse risks on faces during tunnel construction to secure workers' safety. This system enables detecting abnormal behaviors of faces by analyzing the displacement of faces measured in real time using the x-MR control chart technique. In addition, an algorithm to judge false alarms was developed so that abnormal behaviors of faces and errors occurring in the process of work can be distinguished from each other by comparing the number of measured values exceeding the management criteria and moving range k. The results of the present study are applicable to real-time monitoring of behavior on the face in dangerous ground sections to minimize damage to workers.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

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. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.