• Smart Structures and Systems
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• v.29 no.6
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• pp.785-790
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• 2022

Condition monitoring of permanent magnet synchronous motors (PMSMs) and detecting faults such as eccentricity and demagnetization are essential for ensuring system reliability. Motor current signal analysis is the most commonly used precursor for detecting faults in the PMSM drive system. However, the current signature responds sensitively to the load and temperature of the motor, thereby making it difficult to monitor faults in real- applications. Therefore, in this study, a condition monitoring methodology that detects motor faults, including their classification with standstill conditions, is proposed. The objective is to detect and classify faults of PMSMs by using programmable inverter without additional sensors and systems for detection. Both DC and AC were applied through the d-axis of a three-phase motor, and the change in incremental inductance was investigated to detect and classify faults. Simulation with finite element analysis and experiments were performed on PMSMs in healthy conditions as well as with eccentricity and demagnetization faults. Based on the results obtained from experiments, the proposed method was confirmed to detect and classify types of faults, including their severity.

• Smart Structures and Systems
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• v.30 no.6
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• pp.639-648
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• 2022

This study aims at numerically and experimentally investigating torsional guided wave mixing with weak material nonlinearity under acoustoelastic effect in tubular structures. The acoustoelastic effect on single central frequency guided wave propagation in structures has been well-established. However, the acoustoelastic on guided wave mixing has not been fully explored. This study employs a three-dimensional (3D) finite element (FE) model to simulate the effect of stress on guided wave mixing in tubular structures. The nonlinear strain energy function and theory of incremental deformation are implemented in the 3D FE model to simulate the guided wave mixing with weak material nonlinearity under acoustoelastic effect. Experiments are carried out to measure the nonlinear features, such as combinational harmonics and second harmonics in related to different levels of applied stresses. The experimental results are compared with the 3D FE simulation. The results show that the generation combinational harmonic at sum frequency provides valuable stress information for tubular structures, and also useful for damage diagnosis. The findings of this study provide physical insights into the effect of applied stresses on the combinational harmonic generation due to wave mixing. The results are important for applying the guided wave mixing for in-situ monitoring of structures, which are subjected to different levels of loadings under operational condition.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.104-119
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• 2022

Life safety awareness level diagnosis is necessary for customized safety education and continuous safety awareness. As the starting stage of safety education for each life cycle, a scale that has verified the reliability and validity of high school students' life safety awareness has not yet been developed. In this context, the purpose of this study is to develop and validate the life safety awareness scale of high school students and to analyze interindividual differences. Questionnaire data was collected from April to June 2022 from 834 students in the first, second, and third grades of high schools in △△ city in Jeollabuk-do. A final 25-item scale was developed using the preliminary survey, preliminary test, the main test, descriptive statistical analysis, and exploratory and confirmatory factor analysis. This scale consists of four sub-factors: 'safety prevention', 'safety knowledge', 'safety preparation', and 'safety protection'. Good reliability and validity were verified by analysis of content validity and construct validity. The generalizability of the scale was verified by crossover validation between the search group and the crossover group. Based on the interindividual differences analysis, although there was a difference between genders in life safety awareness, there was no difference by grade level and academic achievement. This study is significant in developing the first valid scale that can measure high school students' life safety awareness and providing the necessity and rationale for life safety education by life cycle considering individual gender differences.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.2
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• pp.155-159
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• 2022

Smart livestock, which combines information and communication technology (ICT) with livestock, can be said to be an effective solution to existing livestock problems such as productivity improvement, odors, and diseases. So far, it has hardly been universalized; thus, it is necessary to develop automation devices to reduce labor by localizing automation devices to expand the distribution of ICT technology to farms, and to advance precise specifications and health management technology using biometric information. Weighing scales currently being used in livestock farms are to prevent the spread of diseases by diagnosis and preparation for AI and other diseases in advance, using information on the growing weight of duck breeding. However, accurate values cannot be obtained due to poor breeding conditions. In this paper, we developed a separate data transmission system kit for the weighing scale and placed the sensor on top of the weighing scale so that the sensor wire is not affected by pollutants or ducks on the floor. A display function was provided, and a method of receiving and analyzing the serial port data of the weighing device, and then transmitting them to the data collection server was implemented.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• Korean Journal of Construction Engineering and Management
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• v.19 no.1
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• pp.21-31
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• 2018

The rapid increase of the elderly living alone is a critical issue in worldwide as it leads to a rapid increase of a social support costs (e.g., medical expenses) for the elderly. In early stages of dementia, the activities of daily living (ADL) including self-care tasks can be affected by abnormal patterns or behaviors and used as an evidence for the early diagnosis. However, extracting activities using non-intrusive approach is still quite challenging and the existing methods are not fully visualized to understand the behavior pattern or routine. To address these issues, this research suggests a model to extract the activities from coarse-grained data (spatio-temporal data log) and visualize the behavioral context information. Our approach shows the process of extracting and visualizing the subject's spaceactivity map presenting the context of each activity (time, room, duration, sequence, frequency). This research contributes to show a possibility of detecting subject's activities and behavioral patterns using coarse-grained data (limited to spatio-temporal information) with little infringement of personal privacy.

• Journal of Life Science
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• v.20 no.9
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• pp.1299-1305
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• 2010

Cardiac troponin-I (cTnI), creatine kinase-MB (CK-MB), and C-reactive protein (CRP) are routine cardiac markers for the diagnosis of cardiovascular disease. Recently, brain natriuretic peptide (BNP) has garnered attention as a marker of heart failure. This study was retrospectively designed to investigate the relationships between preoperative BNP, other cardiac markers levels and perioperative parameters in seventy-four adult patients that underwent off-pump coronary artery bypass grafting (OPCAB) and to assess its usefulness for predicting postoperative outcomes. Preoperative levels of BNP, cTnI, CK-MB, and CRP had significantly positive or negative correlations with echocadiographic parameters. There were significantly positive relationships between BNP, cTnI, CK-MB, and CRP concentration. Postoperative mechanical ventilation time had a positive correlation to preoperative levels of cTnI, CK-MB, and CRP, while ICU-staying period had a positive correlation with BNP, cTnI and CK-MB. These results reveal that a preoperative level of BNP is a good predictor and that its combination with cTnI, CK-MB, and CRP might be useful for diagnosis and comprehensive risk stratification of patients with coronary heart diseases, as well as prognosis of perioperative outcomes in OPCAB patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.292-301
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• 2016

Because damages arising from the occurrence of foot-and-mouth disease (FMD) are very great, it is essential to make a preemptive diagnosis to cope with it in order to minimize those damages. The main symptoms of foot-and-mouth disease are body temperature increase, loss of appetite, formation of blisters in the mouth, on hooves and breasts, etc. in a cow or a bull, among which the body temperature check is the easiest and quickest way to detect the disease. In this paper, an algorithm to detect FMD from the hooves of cattle was developed and implemented for preemptive coping with foot-and-mouth disease, and a hoof check test is conducted after the installation of a high-resolution camera module, a thermo-graphic camera, and a temperature/humidity module in the cattle shed. Through the algorithm and system developed in this study, it is possible to cope with an early-stage situation in which cattle are suspected as suffering from foot-and-mouth disease, creating an optimized growth environment for cattle. In particular, in this study, the system to cope with FMD does not use a portable thermo-graphic camera, but a fixed camera attached to the cattle shed. It does not need additional personnel, has a function to measure the temperature of cattle hooves automatically through an image algorithm, and includes an automated alarm for a smart phone. This system enables the prediction of a possible occurrence of foot-and-mouth disease on a real-time basis, and also enables initial-stage disinfection to be performed to cope with the disease without needing extra personnel.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.