• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.5
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• pp.391-398
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• 2016

Acoustic emission (AE) is one of the most powerful techniques for detecting damages and identify damage location during operations. However, in case of the source location technique, there is some limitation in conventional AE technology, because it strongly depends on wave speed in the corresponding structures having heterogeneous composite materials. A compressed natural gas(CNG) pressure vessel is usually made of carbon fiber composite outside of vessel for the purpose of strengthening. In this type of composite material, locating impact damage sources exactly using conventional time arrival method is difficult. To overcome this limitation, this study applied the previously developed Contour D/B map technique to four types of CNG storage tanks to identify the source location of damages caused by external shock. The results of the identification of the source location for different types were compared.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.253-262
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• 2011

Cognitive-communicative disorders after traumatic brain injury(TBI) and right hemisphere damage(RHD) are different from other neurological disorders in nature. Therefore, it is not desirable to use aphasia tests in evaluating individuals with TBI or RHD. The aim of this study is to review assessment protocols on TBI and RHD, and literature related with them. As a result, it is recommended that individuals with TBI be examined in scope of the cognition including attention, memory, organization, reasoning, as well as the functional communication. Similarly, it is useful to consider high-order language related to various cognitive domains in assessing cognitive-communicative ability after RHD. In conclusion, we need to focus on the overall cognitive-communicative domains in an evaluative process of TBI and RHD. Furthermore, it is necessary to develop multiple items for individuals with cognitivecommunicative disorders for the purpose of differentiating these heterogeneous groups from other neurological disorders such as aphasia, and of making good use of them as a therapeutic manual.

• The Journal of the Acoustical Society of Korea
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• v.19 no.1
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• pp.32-39
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• 2000

In this paper, we have introduced the feature extraction for the ultrasonic signal analysis of the undamaged and damaged concrete specimens. Since the concrete has the heterogeneous nature by itself, it has been difficult to classify the feature with using ultrasonic signal which is acquired from the undamaged and damaged concrete specimens. Therefore, in this paper, we proposed the combinational analysis which is using both the damage coefficient and the number of zerocrossing for the feature extraction. And the pulse velocity method and the damage coefficient, which was proposed by Suaris, were reviewed. In this experiment, two types of concrete specimen have been considered: 180kg/㎠ and 240kg/㎠. The ultrasonic signals were acquired in normal direction. As a result, it has been that combinational analysis method, which is proposed in this paper, shows the better performance than the traditional ultrasonic pulse velocity method and the damage coefficient using maximum amplitude of the ultrasonic signal in the feature extraction.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.17-26
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• 2023

A smart city is a massive internet of things (IoT) environment, where all terminal devices are connected to a network to create and share information. In accordance with massive IoT environments, millions of IoT devices are connected, and countless data are generated in real time. However, since heterogeneous IoT devices are used, collecting the logs for each IoT device is difficult. Due to these issues, when an IoT device is invaded or is engaged in malicious behavior, such as infection with malware, it is difficult to respond quickly, and additional damage may occur due to information leakage or stopping the IoT device. To solve this problem, in this paper, we propose identifying the attack technique used for initial access to IoT devices through MITRE ATT&CK, collect the logs that can be generated from the identified attack technique, and use them to identify the cause of malware infection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.23-28
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• 2013

Because characteristics of diversity, large-scale and unpredictability of disaster are very difficult to make predict of disaster, rapid disaster response activity is important to reduce damage from disaster. Therefore emergency communication technology is essential to information sharing between field personnel in the field. Because the heterogeneous radios defined as some of 100MHz VHF, 400MHz UHF, and 800MHz emergency communication RF band are used in Korea, the field personnels need to have multiple radios. In order to use one radio per personnel, radio voice intercommunication is very necessary. We study on modular 3-Band RF to communicate between heterogeneous radios through single multi-band antenna and verify scalable of the number of equipped radios.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.485-495
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• 2015

The Tohoku Earthquake, which hit Japan on March 11, 2011, was a massive magnitude 9.0 earthquake, with the earthquake itself causing damage and the resulting tsunami additionally causing enormous material and human damage. The crustal deformation at that time reached a maximum of 5.24 m in Japan, Neighboring countries South Korea and China as well as the Southeast Asian region also witnessed crustal deformation ranging from a few centimeters to a few meters. The detailed analysis in this study based on data from 72 of the sites in South Korea where GNSS CORS was installed showed that South Korea underwent heterogeneous crustal deformation from the Tohoku earthquake, with a maximum of 55.5 mm, a minimum of 9.2 mm, and an average of 22.42 mm. A crustal deformation model was developed, applied, and evaluated for accuracy in this study for a prompt revision of the survey results of the control points that were changed by the crustal deformation. The survey results were revised by applying a crustal deformation model to the 1,195 unified control points installed in South Korea prior to the Tohoku earthquake. The comparison of these 1,195 points with their new survey results showed that the RMSE decreased from 14.1 to 3.4 mm and that the maximum result difference declined from 39 to 10 mm. Revision of the survey results of the control points using the crustal deformation model is deemed very useful considering that the accuracy of the survey results of the unified control points in South Korea is 3 cm.

• Toxicological Research
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• v.32 no.1
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• pp.47-56
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• 2016

The identification of biomarkers for the early detection of acute kidney injury (AKI) is clinically important. Acute kidney injury (AKI) in critically ill patients is closely associated with increased morbidity and mortality. Conventional biomarkers, such as serum creatinine (SCr) and blood urea nitrogen (BUN), are frequently used to diagnose AKI. However, these biomarkers increase only after significant structural damage has occurred. Recent efforts have focused on identification and validation of new noninvasive biomarkers for the early detection of AKI, prior to extensive structural damage. Furthermore, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Our previous study suggested that pyruvate kinase M2 (PKM2), which is excreted in the urine, is a sensitive biomarker for nephrotoxicity. To appropriately and optimally utilize PKM2 as a biomarker for AKI requires its complete characterization. This review highlights the major studies that have addressed the diagnostic and prognostic predictive power of biomarkers for AKI and assesses the potential usage of PKM2 as an early biomarker for AKI. We summarize the current state of knowledge regarding the role of biomarkers and the molecular and cellular mechanisms of AKI. This review will elucidate the biological basis of specific biomarkers that will contribute to improving the early detection and diagnosis of AKI.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.385-392
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• 2011

Recently, the concern of IT-in-Agriculture convergence technology that combines information technology and agriculture is increasing rapidly. Especially, the crop cultivation related prediction services by spatial data mining (SDM) can play an important role in reducing the damage of natural disaster and enhancing crop productivity. However, the data conversion and integration procedure to acquire the learning dataset of SDM for the prediction service need a lot of effort and time, because of their heterogeneity between distributed data. In addition, calculating spatial neighborhood relationships between spatial and non-spatial data necessitates requires the complicated calculation procedure for large dataset. In this paper, we suggest a SOA-based data integration framework that can effectively integrate distributed heterogeneous data by treating each data source as a service unit and support to find the optimal prediction service by improving productivity of learning dataset for SDM. In our experiment, we confirmed that our framework can be effectively applied to find the optimal prediction service for the frost damage area, by considering the case of peach crop cultivation in Icheon in Korea.

• Computers and Concrete
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• v.9 no.4
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• pp.257-273
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• 2012

Based on the heterogeneous characterization of concrete at mesoscopic level, Realistic Failure Process Analysis ($RFPA^{3D}$) code is used to simulate the failure process of concrete-filled tubular (CFT) stub columns. The results obtained from the numerical simulations are firstly verified against the existing experimental results. An extensive parametric study is conducted to investigate the effects of different concrete strength on the behaviour and load-bearing capacity of the CFT stub columns. The strength of concrete considered in this study ranges from 30 to 110 MPa. Both the load-bearing capacity and load-displacement curves of CFT columns are evaluated. In particular, the crack propagation during the deformation and failure processes of the columns is predicted and the associated mechanisms related to the increased load-bearing capacity of the columns are clarified. The numerical results indicate that there are two mechanisms controlling the failure of the CFT columns. For the CFT columns with the lower concrete strength, they damage when the steel tube yields at first. By contrast, for the columns with high concrete strength it is the damage of concrete that controls the overall loading capacity of the CFT columns. The simulation results also demonstrate that $RFPA^{3D}$ is not only a useful and effective tool to simulate the concrete-filled steel tubular columns, but also a valuable reference for the practice of engineering design.