• Smart Structures and Systems
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• v.23 no.1
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• pp.15-29
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• 2019

This paper addresses the problem of damage detection in suspension bridge hangers, with an emphasis on the modal flexibility method. It aims at evaluating the capability and the accuracy of the modal flexibility method to detect and locate single and multiple damages in suspension bridge hangers, with different level of severity and various locations. The study is conducted numerically and experimentally on a laboratory suspension bridge mock-up. First, the covariance-driven stochastic subspace identification is used to extract the modal parameters of the bridge from experimental data, using only output measurements data from ambient vibration. Then, the method is demonstrated for several damage scenarios and compared against other classical methods, such as: Coordinate Modal Assurance Criterion (COMAC), Enhanced Coordinate Modal Assurance Criterion (ECOMAC), Mode Shape Curvature (MSC) and Modal Strain Energy (MSE). The paper demonstrates the relative merits and shortcomings of these methods which play a significant role in the damage detection ofsuspension bridges.

• Structural Monitoring and Maintenance
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• v.7 no.3
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• pp.261-281
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• 2020

Civil structures may experience progressive deterioration and damage under environmental and operational conditions over their service life. Finite element (FE) model updating method is one of the most important approaches for damage identification in structures due to its capabilities in structural health monitoring. Although various damage detection approaches have been investigated on structures, there are limited studies on large-sized space structures. Thus, this paper aims to investigate the applicability and efficiency of sensitivity-based FE model updating framework for damage identification in large space structures from a distinct point of view. This framework facilitates modeling and model updating in large and geometric complicated space structures. Considering sensitivity-based FE model updating and vibration measurements, the discrepancy between acceleration response data in real damaged structure and hypothetical damaged structure have been minimized through adjusting the updating parameters. The feasibility and efficiency of the above-mentioned approach for damage identification has finally been demonstrated with two numerical examples: a flat double layer grid and a double layer diamatic dome. According to the results, this method can detect, localize, and quantify damages in large-scaled space structures very accurately which is robust to noisy data. Also, requiring a remarkably small number of iterations to converge, typically less than four, demonstrates the computational efficiency of this method.

• Structural Engineering and Mechanics
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• v.82 no.6
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• pp.771-783
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• 2022

Structural damage identification (SDI) methods have been proposed to monitor the safety of structures. However, the traditional SDI methods using modal parameters, such as natural frequencies and mode shapes, are not sensitive enough to structural damage. To tackle this problem, this paper proposes a new SDI method based on transmissibility assurance criterion (TAC) and weighted Schatten-p norm regularization. Firstly, the transmissibility function (TF) has been proved a useful damage index, which can effectively detect structural damage under unknown excitations. Inspired by the modal assurance criterion (MAC), TF and MAC are combined to construct a new damage index, so called as TAC, which is introduced into the objective function together with modal parameters. In addition, the weighted Schatten-p norm regularization method is adopted to improve the ill-posedness of the SDI inverse problem. To evaluate the effectiveness of the proposed method, some numerical simulations and experimental studies in laboratory are carried out. The results show that the proposed method has a high SDI accuracy, especially for weak damages of structures, it can precisely achieve damage locations and quantifications with a good robustness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1277-1282
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• 2011

Recently, many car drivers were damaged by gas station which support similar oil product and not quantitative gas. It were expected to increase above-mentioned damages. By using similar oil products, caused damage are working of lubrication in the fuel line, elf-cleaning function, the part of the early deterioration, impure accumulation in the fuel line, toxicity material in exhaust emissions and unidentified chemical reaction. To prevent these damages, proposed system use in-vehicle state data with OBD-II protocol, measure quantitative gas and similar oil. In this paper, there have implemented similar oil identification and quantitative gas system through OBD-II scanner to provide WiFi communcation by using WinCe development Board.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1276-1283
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• 2016

The scale of financial damages on voice fishing has not been decreased despite of national and social efforts to reduce the amounts of voice fishing damage. One of these reasons is a sophisticated and vernacular speech style that makes it difficult to recognize the offenders. Furthermore, nowadays, young men have intensively been deceived by not only sophisticated and vernacular speech style which is used the employer of real public offices but also obtained personal information. As a result, this lead directly to the financial damages of younger people who has a stronger judgement than older. For this, we investigated the comparison and analysis between the criminals of voice fishing and the same generation younger people for identifying voice features. The experiment was carried out based on the pitch, bandwidth of pitch, energy, speech speed and voice color for searching the difference of voice characteristics between the criminals of voice fishing and the same generation younger people since 2011. The experimental result shows that there is a significant difference in energy and speech speed between the criminals of voice fishing and the same generation younger people.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Smart Structures and Systems
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• v.21 no.1
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• pp.1-14
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• 2018

A method is proposed to detect structural damages in the presence of damping using noisy data. This method uses Frequency Response Function (FRF) and Mode-Shapes as the input parameters for a system of Couple Sparse Coding (CSC) to study the healthy state of the structure. To obtain appropriate patterns of FRF for CSC training, Principal Component Analysis (PCA) technique is adopted to reduce the full-size FRF to overcome over-fitting and convergence problems in machine-learning training. To verify the proposed method, a numerical two-story frame structure is employed. A system of individual CSCs is trained with FRFs and mode-shapes, and then termed ensemble to detect the health condition of the structure. The results demonstrate that the proposed method is accurate in damage identification even in presence of up to 20% noisy data and 5% unconsidered damping ratio. Furthermore, it can be concluded that CSC ensemble is highly efficient to detect the location and the severity of damages in comparison to the individual CSC trained only with FRF data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.93-100
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• 2018

Cyber defamation is the act of damaging the reputation of the other person on the Internet, and the act of attacking by the commenting the article through a word or blog. The reason why punishment is stronger than general contempt is that the nature of crime about defamation is worse than contempt. Also, punishment intensity is higher than defamation because the nature of cyber information spreads widely. Honor is not only a question of self-esteem or identity, but also a function that economically reduces the cost of seeking information or socially trustworthy. Through these two functions, it has been developed as a legal system to protect the honor as well as asking the legal sanction for defamation. However, although honor is used in various meanings in everyday life, the honor of legal level is understood in a more limited sense. It is because the law cannot actively lead and protect all honor feelings for one's feelings or mood occurred by hurt. However, if the social evaluation of a group or individual is undermined through a certain distortion of the truth, the law will actively intervene. However, due to the ambiguity of the legal sanctions standards and the identification of the parties involved in the defamation of cyberspace, it was difficult to solve the problems related to defamation in fact. Therefore, this paper will try to find out the problems of civil legal remedy due to the cyber defamation, and seek a solution for civil legal remedy.

• Korean Journal of Microbiology
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• v.38 no.1
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• pp.19-25
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• 2002

Colletotrichum species are important fungal pathogen that cause great damages on various host plant species worldwide. In Korea, Colletotrichum species cause massive economic losses on apple, peach, grape, and essecially, sweet persimon productions. In the past, Identification of the pathogen and the studies on the genetic relationships among the pathogenic isolates were mainly based on morphology, cultural characteristics, and the difference in pathogenicity. However, in recent years, these traditional methods have been replaced with molecular methods to solve the difficulty of classification on pathogens. Therefore, in this study, RAPD and PCR-RFLP methods were employed for the studies of genetic relationship among the different isolates of Colletotrichum species that cause damages on sweet persimon. As a results of genetic relationship analysis, Colletotrichum species tested were divided into two big groups or five small groups.