• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3104-3120
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• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.6
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• pp.1186-1193
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• 2009

Wearable ECG monitoring is regarded as one of the most essential part in the ubiquitous healthcare environment and subsequently day-life monitoring of a heart condition has been pursued especially for the elder people. However, there are many problems to accomplish this task such as; i) implementation of long-term monitoring device, ii) development of non-irritating electrode on skin and iii) stable signal acquisition. With these aims, we have focused on implementing a non-irritating electrode with an endurable monitoring device for day-life. To accomplish our tasks, we basically developed four different types of textile electrodes that are adapted by both shape and the composed material; flat or convex shape and Ag-conductive paste material or not. It turns out to be that a convex shape and Ag-paste textile electrode has the best performance in terms of both signal-to-noise ratio (SNR) and Impedance/Phase characteristics. Furthermore, ECG amplifier (35 ${\times}$ 35 mm) has developed to resolve the ECG signal and transfer the signal to desktop computing device or portable one by RF serial communication.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.448-452
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• 2007

Many industrial operations require continuous or nearly-continuous operation of machines, which if interrupted can result in significant financial loss. The condition monitoring of these machines has received considerable attention recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is the development of smart sensor using which can on-line perform condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This sensor can receive data in real-time or periodic time from MEMS accelerometer. Furthermore, this system is capable for signal preprocessing task (High Pass Filter, Low Pass Filter and Gain Amplifier) and analog to digital converter (A/D) which is controlled by CPU. A/D converter that converts 10bit digital data is used. This sensor communicates with a remote site PC using TCP/IP protocols. Wireless LAN contain IEEE 802.11i-PSK or WPA (PSK, TKIP) encryption. Developed sensor executes performance tests for data acquisition accuracy estimations.

• Proceedings of the KSRS Conference
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• v.1
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• pp.126-129
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• 2006

The marine environment is under considerable threat from intentional or accidental oil spills, ballast water discharged, dredging and infilling for coastal development, and uncontrolled sewage and industrial wastewater discharges. Monitoring spills and illegal oil discharges is an important component in ensuring compliance with marine protection legislation and general protection of the coastal environments. For the monitoring task an image processing system is needed that can efficiently perform the detection and the tracking of oil spills and in this direction a significant amount of research work has taken place mainly with the use of radar (SAR) remote sensing data. In this paper the level set image segmentation technique was tested for the detection of oil spills. Level set allow the evolving curve to change topology (break and merge) and therefore boundaries of particularly intricate shapes can be extracted. Experimental results demonstrated that the level set segmentation can be used for the efficient detection and monitoring of oil spills, since the method coped with abrupt shape’s deformations and splits.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.598-601
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• 2007

Urban growth management is essential for sustainable urban growth. Monitoring physical urban built-up area is a task of great significance to manage urban growth. Detecting urbanized area is essential for monitoring urbanized area. Although image classifications using satellite imagery are among the conventional methods for detecting urbanized area, they requires very tedious and hard work, especially if time-series remote sensing data have to be processed. In this paper, we propose an effective urbanized area detecting method based on normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). To verify the proposed method, we extract urbanized area using two methods; one is conventional supervised classification method and the other is the proposed method. Experiments shows that two methods are consistent with 98% in 1998, 99.3% in 2000, namely the consistency of two methods is very high. Because the proposed method requires no more process without band operations, it can reduce time and effort. Compared with the supervised classification method, the proposed method using vegetation indices can serve as quick and efficient alternatives for detecting urbanized area.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.4_1
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• pp.549-558
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• 2020

We describe a new approach to real-time implementation of track record and trajectory control of robotic manipulator with eight joints based on monitoring simulator. Trajectory generator uses the kinematic equations of the arm to convert the task description into a series of set points for each of the joint control loops, while the joint controllers, with simple algorithms for just one joint can move at a fast sampling rate, guaranteeing a smooth motion. The proposed control scheme is robust, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate parameter information, nor values of manipulator parameters and payload. Reliability of the proposed technology is veriefied by monitoring simulation and experimental of robot manipulator for the smart factory with eight degrees of freedom.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.56-63
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• 2006

Facilities of 76skV Substation(S/S) play an important role in electric power supply grids. Various power facilities of 765kV S/S might be damaged enormously if a strong earthquake occurs. In an effort to mitigate possible earthquake disasters, KEPRI (Korea Electric Power Research Institute) set forth plans to verify seismic safety of the facilities of 765kV S/S. To accomplish the task, an earthquake monitoring systems is constructed at four 765kV S/S sites(Shin-AnSung, Shin-TaeBaek, Shin-SeoSan and Shin-GaPyung). Data from these earthquake monitoring stations are being transmitted via satellite communication. Currently, KEPRI is operating an earthquake monitoring system in freefield of Shin-SeoSan S/S (NSS) tentatively, Also, the data from NSS is preliminarily analyzed using the horizontal to vertical (H/V) spectrum ratio method. The method of H/V spectrum ratio has been used to infer site amplification without previous knowledge of near surface geology. The results of data analysis shorts good S/N ratio and amplification of 20-25 Hz by site effect. In the near future, the accumulated data is expected to provide a basis for assessing and predicting any damages to integrity of 765kV S/S facilities by earthquakes.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.69-77
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• 2021

This study aims to identify security-based threat information for an organization. This is because protecting the threat for IT systems plays an important role for an corporate's intangible assets. Security monitoring systems determine and consequently respond threats by analyzing them in a real time situation, focusing on events and logs generated by security protection programs. The security monitoring task derives priority by dividing threat information into reputation information and analysis information. Reputation information consisted of Hash, URL, IP, and Domain, while, analysis information consisted of E-mail, CMD-Line, CVE, and attack trend information. As a result, the priority of reputation information was relatively high, and it is meaningful to increase accuracy and responsiveness to the threat information.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.