• Wind and Structures
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• v.20 no.2
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• pp.315-326
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• 2015

To overcome the drawbacks of the traditional contact-type sensor for structural displacement measurement, the vision-based technology with the aid of the digital image processing algorithm has received increasing concerns from the community of structural health monitoring (SHM). The advanced vision-based system has been widely used to measure the structural displacement of civil engineering structures due to its overwhelming merits of non-contact, long-distance, and high-resolution. However, seldom currently-available vision-based systems are capable of realizing the synchronous structural displacement measurement for multiple points on the investigated structure. In this paper, the method for vision-based multi-point structural displacement measurement is presented. A series of moving loading experiments on a scale arch bridge model are carried out to validate the accuracy and reliability of the vision-based system for multi-point structural displacement measurement. The structural displacements of five points on the bridge deck are measured by the vision-based system and compared with those obtained by the linear variable differential transformer (LVDT). The comparative study demonstrates that the vision-based system is deemed to be an effective and reliable means for multi-point structural displacement measurement.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.7-14
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• 2014

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.12-18
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• 2021

This paper proposes a framework for the development of the health recommender system, designed to cater COVID-19 symptoms' self-assessment and monitoring as well as to provide recommendations for self-care and medical treatments. The aim is to provide an online platform for Patient Under Investigation (PUI) and close contacts with positive COVID-19 cases in Malaysia who are under home quarantine to perform daily self-assessment in order to monitor their own symptoms' development. To achieve this, three main phases of research methods have been conducted where interviews have been done to thirty former COVID-19 patients in order to investigate the symptoms and practices conducted by the Malaysia Ministry of Health (MOH) in assessing and monitoring COVID-19 patients who were under home quarantine. From the interviews, an algorithm using user-based collaborative filtering technique with Pearson correlation coefficient similarity measure is designed to cater the self-assessment and symptoms monitoring as well as providing recommendations for self-care treatments as well as medical interventions if the symptoms worsen during the 14-days quarantine. The proposed framework will involve the development of the health recommender system for COVID-19 self-assessment and treatments using the progressive web application method with cloud database and PHP codes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.201-205
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• 2005

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by piezoelectric Impedance-based technique associated with longitudinal wave propagaation. The natural frequency of the object has a tendency of frequency shifting according to hole size corresponded to real structure crack and crack size. In order to estimate the damage condition numerically, we suggest the evaluation method of Impedance peak frequency shift hF in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.860-863
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• 2005

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by PZT impedance-based technique associated with longitudinal wave propagation. The bolt fastening condition is adjusted by torque wrench In order to estimate the damage condition numerically, we suggest the evaluation method of impedance peak frequency shift $\Delta$F in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.889-893
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• 2004

This work presents a study on development of a practical and quantitative technique for assessment of the structural health condition by piezoelectric impedance-based technique associated with longitudinal wave propagation method. The bolt fastening condition is adjusted by torque wrench. In order to estimate the damage condition numerically, three damage indices, impedance peak frequency shift ${\Delta}F$ is proposed in this paper. Furthermore, an assessment method is described for estimation of the damage by using these three damage indices.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.950-956
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• 2014

Special purpose sensor design using MEMS (Micro-Electro-Mechanical Systems) technique is commonly used in Nondestructive Testing (NDT) research for the evaluation of existing structures and for the safety control and requirements. Various sensors and network have been developed for general infrastructures as well as safety-critical applications, e.g., aerospace, defense, and nuclear system, etc. In this paper, one of sensor technique using Fiber Bragg Gratings (FBG) and Finite Element Method (FEM) evaluation is discussed. The experimental setup and data collection technique is also demonstrated. The factors influencing test result and the advantages/limitations of this technique are also reviewed using various methods.

• Journal of Korean Public Health Nursing
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• v.35 no.3
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• pp.430-447
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• 2021

Purpose: The purpose of this study was to analyze the role and tasks of nurses who were working for the elderly in the visiting health services at the public health centers. Methods: Literature reviews, two rounds of meetings with 5 experts and a two-round Delphi technique with 15 experts were performed in this study. Results: The nurses' role and job analysis revealed 5 roles, 16 duties and, 71 tasks. The nurses' roles, including discovery and registration of households/groups in visiting health service in the community, case manager, administrative management, program planning, operation and evaluation, and development of job competency. Sixteen duties included client registration and management, need assessment and plan establishment, education, consultation and support, seasonal health care, prevention and monitoring of infectious diseases, basic nursing care, chronic disease management, linkage and utilization of resources, team cooperation and coordination, home environment management, monitoring and support for intervention outcomes, evaluation, administrative management, program planning, operation and evaluation, development of professional competency and, adoption of fourth industrial revolution technology. Conclusions: Based on the results, the government should provide sufficient nursing personnel to provide universal preventive health services for the elderly and a job training program to perform these roles well.

• Journal of the Korea institute for structural maintenance and inspection
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• v.8 no.1
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• pp.165-174
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• 2004

This research develops a technique which uses energy dissipation ratio in order to monitor the structural health on real time basis. For real-time monitoring, we employ the NExT and the ERA which enable us to obtain real-time data. Energy dissipation ratio is calculated from those data only with the damping and natural frequency of the structure, and from the calculated values we develop an algorithm (Energy dissipation method) which decides the damage degree of structure. The Energy dissipation method developed in this research is proved to be valid by comparison with other methods like the eigenparameter method and the MAC. Especially this method enables us to save measuring time and data which are the most important in real-time monitoring, and its use of the ambient vibration also makes it easy to monitor the whole structure and its damage points.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.