• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.231-247
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• 2014

This paper focuses on the dynamic behaviour of Mirandola City Hall (a XV century Renaissance Palace) that was severely damaged during May 2012 Emilia earthquake in Northern Italy. Experimental investigations have been carried out on this monumental building. Firstly, detailed investigations have been carried out to identify the identification of the geometry of the main constructional parts as well as the mechanical features of the constituting materials of the palace. Then, Ambient Vibration Tests (AVT) have been applied, for the detection of the main dynamic features. Three output-only identification methods have been compared: (i) the Frequency Domain Decomposition, (ii) the Random Decrement (RD) and the (iii) Eigensystem Realization Algorithm (ERA). The modal parameters of the Palace were difficult to be identified due to the severe structural damage; however the two bending modes in the perpendicular directions were identified. The comparison of the three experimental techniques showed a good agreement confirming the reliability of the three identification methods.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• Korean Journal of Health Education and Promotion
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• v.26 no.1
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• pp.81-92
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• 2009

Objectives: This study set out to investigate the relationship among the factors of metabolic syndrome diagnosis criteria, their risk factors including general characteristics, and the distribution of the diagnosis criteria and risk among the adult residents of a rural community. Methods: Among 1,968 residents, those who had three or more of the risk factors of metabolic syndrome, which include blood pressure, blood glucose, triglyceride, abdominal obesity, and HDL-C, were categorized as the metabolic syndrome group. And their correlations were analyzed. Results: As for the risk ratio with five factors of the metabolic syndrome diagnosis criteria, it was high according to age and smoking. In addition, the results show that body fat percentage, hs-CRP, insulin, BMI, PP2, total cholesterol, and W/Ht also had much impact on increasing the risk ratio of the metabolic syndrome diagnosis criteria. It turned out that metabolic syndrome was affected by the body mass index(BMI), insulin, waist to height ratio(W/Ht), and hs-CRP. It was 2.51 times crude odds ratio that BMI over the 25kg/m2 in the ratio of the fact of metabolic syndrome and adjusted for sex odds ratio 2.50times and W/Ht was 3.31times, adjusted for sex odds ratio 3.25 times. Conclusion: BMI, W/Ht and smoking of the general characteristics seem to have close relationships with high correlations between the metabolic syndrome diagnosis criteria and the risk factors. Thus there is an urgent need to evaluate them and take interventions and monitoring measures for the clustering of risk factors.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.546-548
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• 2010

This paper proposes a new baseline-free TR-based SHM method in which the time-reversal (TR) property of the guided Lamb waves is utilized. The new TR-based SHM method has two distinct features when compared with the other existing SHM techniques: (1) The measurement- based backward TR process is replaced by the computation-based process (2) In place of the comparison method most commonly used for SHM, the TOF information of the damage signal extracted from the reconstructed signal is utilized for the damage diagnosis. For the damage diagnosis, the imaging method is adopted to efficiently detect damage by representing the damage as an image. The proposed TR-based SHM technique is then validated through the damage diagnosis experiment for an aluminum plate with a damage at different locations.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.19 no.2
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• pp.139-142
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• 2016

The coexistence of Wilson disease with Alport syndrome has not previously been reported. The diagnosis of Wilson disease and its ongoing monitoring is challenging when associated with an underlying renal disease such as Alport syndrome. Proteinuria can lead to low ceruloplasmin since it is among serum proteins inappropriately filtered by the damaged glomerulus, and can also lead to increased urinary loss of heavy metals such as zinc and copper. Elevated transaminases may be attributed to dyslipidemia or drug induced hepatotoxicity. The accurate diagnosis of Wilson disease is essential for targeted therapy and improved prognosis. We describe a patient with a diagnosis of Alport syndrome who has had chronic elevation of transaminases eventually diagnosed with Wilson disease based on liver histology and genetics.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.23-30
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• 2014

This paper presents an on-line diagnosis system for identifying health and faulted conditions in squirrel-cage induction motors using stator current, temperature, and partial discharge signals. The proposed diagnosis system can diagnose induction motor faults such as broken rotor bars, air-gap eccentricities, stator winding insulations, and bearing faults. Experimental results obtained from induction motors show that the proposed system is capable of detecting induction motor faults.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.1-7
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• 2000

This paper presents the approach involving fault detection and diagnosis of gears using pattern recognition and Wavelet transform. It describes result of the comparison between KDI (Kullback Discrimination Information) with the nearest neighbor classification rule as one of pattern recognition methods and Wavelet transform to know a way to detect and diagnosis of gear damages experimentally. To model the damages 1) Normal (no defect), 2) one tooth is worn out, 3) All teeth faces are worn out 4) One tooth is broken. The vibration sensor was attached on the bearing housing. This produced the total time history data that is 20 pieces of each condition. We chose the standard data and measure distance between standard and tested data. In Wavelet transform analysis method, the time series data of magnitude in specified frequency (rotary and mesh frequency) were earned. As a result, the monitoring system using Wavelet transform method and KDI with nearest neighbor classification rule successfully detected and classified the damages from the experimental data.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.303-308
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• 2019

The personal health record platform can store and manage medical records, health-monitoring data such as blood pressure and blood sugar, and life logs generated from various wearable devices. It provides services such as international standard-based medical document management, data pattern analysis and an intelligent inference engine, and disease prediction and domain contents. This study aims to construct a foundation for the transmission of international standard-based medical documents by mapping the diagnosis items of a general health examination, special health examination, life logs, health data, and life habits with the international standard terminology systems. The results of mapping with international standard terminology systems show a high mapping rate of 95.6%, with 78.8% for LOINC, 10.3% for SNOMED, and 6.5% when mapped with both LOINC and SNOMED.

• Journal of Aerospace System Engineering
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• v.11 no.3
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• pp.22-30
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• 2017

This paper examines a survey on recent research regarding health monitoring and management for liquid rocket engines (LRE). For this, we investigated precedent techniques applied to LRE development. Particularly, we focused on open-cycle LRE to apply to KSLV-II (Korea Space Launch Vehicle II). Through this study, we subdivided health monitoring algorithms and analyzed fault detection and diagnosis algorithm developed in China, since China researched open-cycle LRE that have the same cycle as KSLV-II rocket engines. We discuss significant points to be considered regarding development of the KSLV-II.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.436-442
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• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.