• Journal of Ocean Engineering and Technology
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• v.22 no.5
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• pp.142-147
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• 2008

The cases of using new methods of big blocks are largely increasing on Recent large-scale bridge structures. So the accurate data of responses of bridges following environmental causes are required to be quickly recorded in order to predict. For this reason described above, the research on measuring system should be conducted for more knowledge of the details on application and stability of new methods. In this study, the new health monitoring system that can monitor the real behavior and damages of the bridge during all processes of construction is presented by analyzing cases of domestic and overseas bridge health monitoring system, and applied methods of following bridges.

• Smart Structures and Systems
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• v.18 no.2
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• pp.317-334
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• 2016

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.49-52
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• 2007

Terrestrial Laser Scanning(TLS) was developed at the mid-to-late 1990s. This technique enables to perform reconnaissance surveying of regions or structures hard to access. Besides, TLS has been extended its application gradually such as preservation of historical remains, underground surveys, slopes, glaciers monitoring and so on. However, though the technique has a lot of advantages, an application for structural health and safety monitoring is a beginning stage and it need much research. Therefore in this study, as a groundwork, the estimation model on stress of structures using TLS and Finite Element Method(FEM) applied by the Digital Elevation Model(DEM) technique of geoinformatics is proposed. For the verification of this model, experiments were performed with a continuous steel beam subjected to point loads and outputs were compared with those of electrical strain sensors.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.4
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• pp.458-464
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• 2008

The structural health monitoring has been gaining more importance in civil engineering areas such as earthquake and wind engineering. The use of health monitoring system can also provide tools for the validation of structural analytical model. However, only few structures such as historical buildings and some important long bridges have been instrumented with structural monitoring system due to high cost of installation, long and complicated installation of system wires. In this paper, the structural monitoring system based on cheap and wireless monitoring system is investigated. The use of advanced technology of micro-electro-mechanical system(MEMS) and wireless communication can reduce system cost and simplify the installation. Further the application of wireless MEMS system can provide enhanced system functionality and due to low noise densities. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS system estimates system parameters accurately.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1019-1039
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• 2015

Shanghai Tower is a composite structure building with a height of 632 m. In order to verify the structural properties and behaviors in construction and operation, a structural health monitoring project was conducted by Tongji University. The monitoring system includes sensor system, data acquisition system and a monitoring software system. Focusing on the health monitoring in construction, this paper introduced the monitoring parameters in construction, the data acquisition strategy and an integration structural health monitoring (SHM) software. The integration software - Structural Monitoring/ Analysis/ Evaluation System (SMAE) is designed based on integration and modular design idea, which includes on-line data acquisition, finite elements and dynamic property analysis functions. With the integration and modular design idea, this SHM system can realize the data exchange and results comparison from on-site monitoring and FEM effectively. The analysis of the monitoring data collected during the process of construction shows that the system works stably, realize data acquirement and analysis effectively, and also provides measured basis for understanding the structural state of the construction. Meanwhile, references are provided for the future automates construction monitoring and implementation of high-rise building structures.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.10a
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• pp.130-131
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• 2016

This study was performed for application of Structural Health Monitoring system of large structures. In order to evaluate damage of a structure, strain data of truss members that are changing with damage are gained by FEM analysis program. These data are used to train Artificial Neural Network(ANN), and this ANN algorithm can be used to analysis strain data for evaluating damage of the truss members.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.481-486
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• 2012

This thesis examines the development and application of 'Health Monitoring System' which monitors periodically the state of a pipe of petrochemical plant by using magnetostrictive sensor technology. The existing guided-wave inspection methods cannot be applied to welding part inspection in pipe, and has a limit of precision when applied to general parts because of noise, reflected waves, and so on. This technology uses the information on displacement of a defect through periodic monitoring, which makes more precise inspection, and can be utilized very usefully in a petrochemical plant.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1981-1991
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• 2016

In recent, the advancement of wearable devices and wireless body area networking technologies motivate researchers to pay attention to remote healthcare system for monitoring patients health and disease progression effectively. However, in order to implement a practical remote healthcare system, we must consider the security and privacy of patient's personal health information transmitted to healthcare servers through the network. Hence, in this paper, we propose a secure health data transmission protocol in remote healthcare monitoring system to protect patient's health information and prevent privacy from eavesdropping on the network. To achieve our security goals, we design an efficient secure protocol based on the identity-based cryptography with key evolution technique, and then confirm the superiority and the efficiency of the proposed protocol as compared with the existing protocol of Yang et al.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.9-15
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• 2019

As the human population increases in the world, the ratio of health doctors is rapidly decreasing. Therefore, it is an urgent need to create new technologies to monitor the physical and mental health of people during their daily life. In particular, negative mental states like depression and anxiety are big problems in modern societies. Usually this happens due to stressful situations during everyday activities including work. This paper presents a machine learning approach to reliably estimating the level of human mental stress using wearable physiological sensors. And also, this paper presents an Android- and Arduino-based stress monitoring and relief system.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.478-491
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• 2019

This work develops a monitoring system for the population with health concerns. A belt integrated with an on-body circuit and sensors measures a wearer's selected vital signals. The electrocardiogram sensors monitor heart conditions and an accelerometer assesses the level of physical activity. Sensed signals are transmitted to the circuit module through digital yarns and are forwarded to a mobile device via Bluetooth. An interactive application, installed on the mobile device, is used to process the received signals and provide users with real-time feedback about their status. Persuasive functions are designed and implemented in the interactive application to encourage users' physical activity. Two signal processing algorithms are developed to analyze the data regarding heart and activity. A user study is conducted to evaluate the performance and usability of the developed system.