• Proceedings of the Korean Geotechical Society Conference
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• 2000.11b
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• pp.60-76
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• 2000

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG)sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1203-1214
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• 2015

This paper deals with the experimental validation of the use of PVDF Patches for the assessment of spatial derivatives of displacement field. It focuses more exactly on the shear Force Identification by using Specific forms of PVDF patcHes (FISH) on beams. An overview of the theoretical approach is exposed. The principle is based on the use of the weak form of the equation of motion of the beam which allows the shear forces to be extracted at one edge of the sensor when this last has a specific form. The experimental validation is carried out with a cantilever steel beam, excited by a shaker at its free boundary. The validation consists in comparing the shear force measured by the designed sensor glued at the free edge and the directly measured force applied by the shaker. The sensor is made of two patches, called the "stiffness" patch and the "mass" patch. The use of both patches allows one to identify correctly the shear force on a large frequency domain. The use of only the stiffness patch is valid in the low frequency domain and has the advantage to have a frequency-independent gain that allows its use in real time.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.131-139
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• 1999

An easy and efficient method is proposed for a computation of reliability of preprocessing filters in the fire control system when the sensor data are frequently unreliable depending on the operation environment. It computes state transition probability matrix after modeling filter states as a Markov process, and computing false alarm and detection probability of each filter state under the given sensor failure probability. It shows that two important indices such as distributed state probability and error variance can be derived easily for a reliability assessment of the given sensor fusion system.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.211-219
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• 2006

A random forests classifier is applied to multi-sensor data fusion for supervised land-cover classification in order to account for the importance of variable. The random forests approach is a non-parametric ensemble classifier based on CART-like trees. The distinguished feature is that the importance of variable can be estimated by randomly permuting the variable of interest in all the out-of-bag samples for each classifier. Two different multi-sensor data sets for supervised classification were used to illustrate the applicability of random forests: one with optical and polarimetric SAR data and the other with multi-temporal Radarsat-l and ENVISAT ASAR data sets. From the experimental results, the random forests approach could extract important variables or bands for land-cover discrimination and showed reasonably good performance in terms of classification accuracy.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.87-95
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• 2021

Recently interest for air pollution is gradually increasing. However, according to the environmental assessment of air quality, the level of air pollution in the nation is quite serious, and air pollutants measuring facilities are also not enough. In this paper, a secure air pollutants sensor system based low power wireless communication is designed and implemented. The proposed system is composed of three parts: air pollutants measuring sensors module, LoRa-based data transmission module, and monitoring module. In the air pollutants measuring module, the MSP430 board with six big air pollutants measuring sensors are used. The air pollutants sensing data is transmitted to the control server in the monitoring system using LoRa transmission module. The received sensing data is stored in the database of the monitoring system, and visualized in real-time on the map of the sensor locations. The implemented air pollutant sensor system can be used for measuring the level of air quality conveniently in our daily lives.

• 한국가스학회:학술대회논문집
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• 1999.09a
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• pp.127-132
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• 1999

• Journal of Biosystems Engineering
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• v.42 no.2
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• pp.126-126
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• 2017

• Proceedings of the Korean Nuclear Society Conference
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• 2004.05a
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• pp.103-103
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• 2004

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1629-1639
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• 2009

This paper describes the environmental monitoring system for supporting comfortable subways based on USN. Our development system includes the sensor field based on integrated sensor, monitoring system for supporting the local and remote monitoring and middle-ware performs the collecting, analyzing, and storing the data. In this paper, we installed the temperature, humidity, micro-dust sensor and water-level sensor for supporting the rail-roads and make up the integrated sensor enables to reuse the analog device from 4~20mA output with connection of wireless sensor device. Middleware includes the modules of collecting, analysis, and storing the data and monitoring system supports the local for administrator and remote monitoring for citizen services based on web. The middleware and monitoring in this paper is comprised of some components can reuse and support the change of application and sensors. Our development system supports the mobility of sensor devices and distributes system. Data collection and management function supported by middleware will use assessment.