• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1303-1322
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• 2020

Indonesia is more prone to natural disasters due to its geological condition under the three main plates, making Indonesia experience frequent seismic activity, causing earthquakes, volcanic eruption, and tsunami. Those disasters could lead to other disasters such as landslides, floods, land subsidence, and coastal inundation. Monitoring those disasters could be essential to predict and prevent damage to the environment. We reviewed the application of remote sensing and Geographic Information System (GIS) for detecting natural disasters in the case of Indonesia, based on 43 articles. The remote sensing and GIS method will be focused on InSAR techniques, image classification, and susceptibility mapping. InSAR method has been used to monitor natural disasters affecting the deformation of the earth's surface in Indonesia, such as earthquakes, volcanic activity, and land subsidence. Monitoring landslides in Indonesia using InSAR techniques has not been found in many studies; hence it is crucial to monitor the unstable slope that leads to a landslide. Image classification techniques have been used to monitor pre-and post-natural disasters in Indonesia, such as earthquakes, tsunami, forest fires, and volcano eruptions. It has a lack of studies about the classification of flood damage in Indonesia. However, flood mapping was found in susceptibility maps, as many studies about the landslide susceptibility map in Indonesia have been conducted. However, a land subsidence susceptibility map was the one subject to be studied more to decrease land subsidence damage, considering many reported cases found about land subsidence frequently occur in several cities in Indonesia.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.163-177
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• 2019

As inter-Korean relations progress, the issue of natural disasters which could directly affect the lives of the people in both Koreas, has not yet been discussed. Considering the current status of inter-Korean relations and the ongoing disaster-related damage in North Korea, it is imperative to establish a technical plan at the pan-governmental level to reduce the damage from natural disasters. The purpose of this study is to secure the Korea Peninsula against natural disasters by organizing South Korea's science and technologies related to natural disasters in order to reduce the damage, and to evaluate the applicability of said technologies. The situation of natural disasters in North Korea for 17 years has been summarized and reclassified based on eight types of natural disasters. Technologies related to natural disasters in South Korea were also investigated and reclassified. Based on the data, a priority evaluation was performed and the prioritization of technology application for each natural disaster type in North Korea was calculated through a quadrant analysis. As a result, the three major categories of high-priority technologies were classified as natural disaster monitoring with remote sensing and spatial information technology, construction of research basis and database based on geographic information system (GIS) and integrated management of complex natural disasters.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.6_spc
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• pp.651-659
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• 2016

Condition monitoring (CM) encounters a large data problem due to sensors that measure vibration data with a continuous, and sometimes, high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate the efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer samples compared to traditional sampling methods. For the experiments a built-in rotating system was used and all data were compressively sampled to obtain compressed data. Optimal signal features were then selected without the reconstruction process and were used to detect and classify damage. The experimental results show that the proposed method could improve the data processing speed and the accuracy of condition monitoring of rotating systems.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1055-1057
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• 2003

In this research, the concrete with paint film was classified using hyper-spectral remote sensing. First, spectral characteristics of concrete and concrete with some kinds of paint films were investigated with a spectrometer. Second, using reflectance and first order derivative, spectral characteristics of the normal concrete and the concrete with paint film were classified. By using hyper-spectral remote sensing, not only extraction of crack but also inspection of paint film distribution is possible.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.282-289
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• 2010

Most nondestructive testing techniques require good contact between the sensor and tested concrete surface to obtain reliable data. But the surface preparation is often very time and labor consuming due to the rough surface or limited access of concrete structures. One approach to speed up the data collection process is to eliminate the need for physical contact between the sensor and tested structure. Non-contact air-coupled sensing technique can be a good solution to this problem. An obvious advantage of the non-contact air-coupled sensing technique is which can greatly speed up the data collection in field and thus the damage detection process can be completed very rapidly. In this article, recent developments in non-contact air-coupled sensing technique for rapid detection of damages in concrete structures are summarized to evoke interest, discussion and further developments on this technique to a NDT research community in Korea. It is worth noting that the works in this article have been published in the types of thesis, proceedings, and journals. All published sources are cited in the text and listed in reference.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.117-120
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• 2003

Electro-micromechanical techniques were applied using four-probe method for carbon nanotube (CNT) or nanofiber (CNF)/epoxy composites with their content. Carbon black (CB) was used to compare with CNT and CNF. The fracture of carbon fiber was detected by nondestructive acoustic emission (AE) relating to electrical resistivity for double-matrix composites test. Sensing for fiber tension was performed by electro-pullout test under uniform cyclic strain. The sensitivity for fiber damage such as fiber fracture and fiber tension was the highest for CNT/epoxy composites, and in CB case they were the lowest compared with CNT and CNF. Reinforcing effect of CNT obtained from apparent modulus measurement was the highest in the same content. The results obtained from sensing fiber damage were correlated with the morphological observation of nano-scale structure using FE-SEM. The information on fiber damage and matrix deformation and reinforcing effect of carbon nanocomposites could be obtained from electrical resistivity measurement as a new concept of nondestructive evaluation.

• Structural Engineering and Mechanics
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• v.71 no.6
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• pp.751-761
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• 2019

Lamb wave technology is a promising technology in the field of structural health monitoring and can be applied in the detection and monitoring of defects in plate structures. Based on the reconstruction algorithm for the probabilistic inspection of damage (RAPID), a Lamb-based detection and evaluation method of through-thickness rectangular notches in metal plates was proposed in this study. The influences of through-thickness rectangular notch length and the angle between sensing path and notch length direction on signals were further explored through simulations and experiments. Then a damage index calculation method which focuses on both phase and amplitude difference between detected signals and baseline signals was proposed. Based on the damage index difference between two vertically crossed sensing paths which pass through the notch in a sensor network, the notch direction identification method was proposed. In addition, the notch length was determined based on the damage index distribution along sensing paths. The experimental results showed that the image reconstructed with the proposed method could reflect the information for the evaluation of notches.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Journal of Korean Society of societal Security
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• v.1 no.2
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• pp.73-78
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• 2008

Recently, large scale natural disasters such as floods and typhoons due to climate change have been occurring all over the world causing severe damages. Among the various efforts to reduce and recover damages, recently, advanced information technology and remote sensing techniques are applied in disaster management. In this study, a real-time automated damage estimation system using information technology and spatial imagery was developed to accomplish prompt and accurate disaster damage estimation. This system is able to estimate the damage amounts of public facilities such as roads, rivers, bridges automatically through spatial imageries including ground based digital images, aerial photos, satellite images of disaster sites. Based on these spatial imageries, the damage amounts are analyzed in the Web-GIS based analysis system. Consequently, the digital damage reports such as digital disaster information sheets and damage maps can be made promptly and accurately. This system can be a useful tool to carry out prompt disaster damage estimation and efficient disaster recovery.

• Smart Structures and Systems
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• v.22 no.2
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• pp.231-237
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• 2018

A significant data problem is encountered with condition monitoring because the sensors need to measure vibration data at a continuous and sometimes high sampling rate. In this study, compressive sensing approaches for condition monitoring are proposed to demonstrate their efficiency in handling a large amount of data and to improve the damage detection capability of the current condition monitoring process. Compressive sensing is a novel sensing/sampling paradigm that takes much fewer data than traditional data sampling methods. This sensing paradigm is applied to condition monitoring with an improved machine learning algorithm in this study. For the experiments, a built-in rotating system was used, and all data were compressively sampled to obtain compressed data. The optimal signal features were then selected without the signal reconstruction process. For damage classification, we used the Variance Considered Machine, utilizing only the compressed data. The experimental results show that the proposed compressive sensing method could effectively improve the data processing speed and the accuracy of condition monitoring of rotating systems.