• The Journal of Internal Korean Medicine
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• v.37 no.2
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• pp.374-380
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• 2016

Objective: Although the incidence of chronic hepatitis B has decreased around the world due to widespread national preventative control measures, mortality from the same condition can increase if the condition leads to liver cancer or liver cirrhosis. In most cases, herbal medicine does not show any statistically significant effects related to liver damage, but preconceptions do exist that herbal medicine can be toxic and cause such liver damage. To investigate this situation, this study therefore investigated a patient with hepatitis B who had combined traditional Korean medicine therapy and the use of analgesic drugs during a hospitalization period.Method: A patient with hepatitis B was given combined traditional Korean medicine therapy and the use of analgesic drugs during a hospitalization period.Results: Within 26 days, the patient was free from liver damage during the hospitalization period. She was followed up with a liver function test and was discharged after her condition improved; she also reported decreased back pain.

• 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.

• Geomechanics and Engineering
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• v.15 no.1
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• pp.615-620
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• 2018

In order to analyze the healing effectiveness of rock salt cracks affected by the applied stresses and time, we used the ultrasonic technology to monitor the ultrasonic pulse velocity (UPV) variations for different initial stress-damaged rock salts during self-healing experiments. The self-healing experiments were to create different conditions to improve the microcracks closure or recrystallized, which the self-healing effect of damaged salt specimens were analyzed during the recovery period about 30 days. We found that: The ultrasonic pulse velocity of the damaged rock salts increases rapidly during the first 9 days recovery, and the values gradually increase to reach constant values after 30 days. The damaged value and the healed value were identified based on the variation of the wave velocity. The damaged values of the specimens that are subject to higher initial damage stress are still keeping in large after 30 days recovery under the same recovery condition It is interesting that the damage and the healing were not in the linear relationship, and there also existed a damage threshold for salt cracks healing ability. When the damage degree is less than the threshold, the self-healing ratio of rock salt is increased with the increase in damage degree. However, while the damage degree exceeds the threshold, the self-healing ratio is decreased with the increase in damage.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.87-94
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• 2012

A damage estimation method of wind turbine tower using natural frequency and mode shape is presented for effective condition monitoring. Dynamic analysis for a wind turbine was carried out to obtain the response of tower from which modal properties were identified. A neural network was learned based on training patterns generated by the changes of natural frequency and mode shape due to various damages. The changes of modal property were calculated using a program for modal parameter estimation. Damage locations and severities could be successfully estimated for 10 damage cases including multi-damage cases using the trained neural network. The damage severities for very small damages generally tends to be slightly under-estimated however, the identified damage locations agreed reasonably well with the accurate locations. Enhancement of the estimation result for very small damage and verification of the proposed method through experiment will be carried out by further study.

• Smart Structures and Systems
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• v.18 no.3
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Journal of the Korean Geosynthetics Society
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• v.6 no.4
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• pp.29-37
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• 2007

Reduction Factor for Installation Damage required for calculation of design strength of geogrid used in MSEW(mechanically stabilized earth wall) design is usually obtained in the field test simulating real construction condition. However, damages occurred in geogrid during backfill work are influenced by many factors such as polymer types, unit weight per area, backfill construction method and gradation of backfill material and field test considering these factors demands lots of time and costs. In this study, factors affecting installation damage are analyzed and empirical method for evaluating reduction factor for installation damage using maximum particle size in backfill material is suggested.

• Smart Structures and Systems
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• v.15 no.2
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• pp.395-408
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• 2015

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.4
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• pp.347-355
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• 2013

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

• Journal of the Korean Society of Clothing and Textiles
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• v.22 no.2
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• pp.224-232
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• 1998

Protease is mixtured in detergent to remove protein-soil easily. It must not act on the any fiber except protein-soil during laundry. So the purpose of this study is to investigate how protease is affect the fiber, particulary the protein-fiber. For this purpose, silk, wool and nylon are selected as samples, and the extent of the damage was estimated as tensile strength and surface condition (that is fibrillation). The results are as follows. The tensile strength of fiber treated with protease were lowered at enzyme concentration 0.1%, temperature 4$0^{\circ}C$ , and, as washing time was longer, it was lowered more. And it was showed that the surface of fibers were fiblliated by protease during washing. From this results, it was found that protease damaged protein-fiber. The damage of silk was the largest of all, and wool was less damaged than silk, because it has the scale (cuticle) on the outside. Additionary, an influence of surfactant on damage of fiber was little about three fibers, but, the fibers were damaged more by the binary nonionic-surfactant and protease mixture than by protease only.

• Journal of the Society of Naval Architects of Korea
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• v.43 no.4 s.148
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• pp.494-503
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• 2006

Inherent damage zone model is presented to explain the fatigue properties near the fatigue limit and the crack growth threshold consistently Inherent damage zone model assumes that the stress at a point which is located at a small distance, $r_0$, an inherent length of the material that represents the size of effective damage zone, from the crack initiation position governs the fatigue characteristics regardless of the geometric configuration of the specimen; smooth specimen, notched specimen or cracked specimens with short and long crack length. A special feature of the paper is using the exact stress distributions of notched and cracked specimens at the strength evaluations. Analytical elastic solutions by Neuber and Westergaard are employed for this purpose Relationship between fatigue limit of smooth specimen and threshold stress of cracked specimen, occurrence condition of non-propagating crack at the root of elliptic notch and circular hole and relationship between stress concentration factor and fatigue notch factor are discussed quantitatively based on the proposed model.