• Smart Structures and Systems
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• v.5 no.4
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• pp.357-367
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• 2009

Structural Health Monitoring (SHM), particularly remote monitoring, is an emerging field with great potential to help infrastructure owners obtain more and up-to-date knowledge of their structures. The methodology could provide supplemental information to guide the frequency and extent of visual inspections, and the possible need for maintenance. The instrumentation for a SHM system needs to be developed with longevity and the objectives for the system in mind. Sensors need to be selected for reliability and durability, sited where they provide the maximum information for the objectives, and where they can be accessed and replaced should the need arise over the monitoring period. With the rapid changes now occurring with sensors and software, flexibility needs to be in place to allow the system to be upgraded over time. Damage detection needs to be considered in terms of the type of damage that needs to be detected, informing maintenance requirements, and how detection can be achieved. Current vibration analysis techniques appear not yet to have achieved the necessary sensitivity for that purpose. Societal factors will influence the design of a SHM system in terms of the sophistication of the instrumentation and methodology employed.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.10-21
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• 2015

Structural health monitoring (SHM) is a technique to diagnose an accurate and reliable condition of civil infrastructure by collecting and analyzing responses from distributed sensors. In recent years, aging civil structures have been increasing and they require further developed SHM technology for development of sustainable society. Wireless smart sensor and network technology, which is one of the recently emerging SHM techniques, enables more effective and economic SHM system in comparison to the existing wired systems. Researchers continue on development of the capability and extension of wireless smart sensors, and implement performance validation in various in-laboratory and outdoor full-scale experiments. This paper presents a summary of recent (mostly after 2010) researches on smart sensors, focused on the newly developed hardware, software, and validation examples of the developed smart sensors.

• Smart Structures and Systems
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• v.5 no.4
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• pp.345-355
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• 2009

This article describes the development of a fiber optic accelerometer based on Fiber Bragg Gratings (FBG). The accelerometer utilizes the stiffness of the optical fiber and a lumped mass in the design. Acceleration is measured by the FBG in response to the vibration of the fiber optic mass system. The wavelength shift of FBG is proportional to the change in acceleration, and the gauge factor pertains to the shift in wavelength as a function of acceleration. Low frequency version of the accelerometer was developed for applications in monitoring bridges. The accelerometer was first evaluated in laboratory settings and then employed in a demonstration project for condition assessment of a bridge. Laboratory experiments involved evaluation of the sensitivity and resolution of measurements under a series of low frequency low amplitude conditions. The main feature of this accelerometer is single channel multiplexing capability rendering the system highly practical for application in condition assessment of bridges. This feature of the accelerometer was evaluated by using the system during ambient vibration tests of a bridge. The Frequency Domain Decomposition method was employed to identify the mode shapes and natural frequencies of the bridge. Results were compared with the data acquired from the conventional accelerometers.

• Smart Structures and Systems
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• v.11 no.4
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• pp.349-364
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• 2013

Recently, researchers in the field of structural health monitoring (SHM) have been rigorously striving to replace the conventional NDE techniques with the smart material based SHM techniques, employing smart materials such as piezoelectric materials. For instance, the electromechanical impedance (EMI) technique employing piezo-impedance (lead zirconate titanate, PZT) transducer is known for its sensitivity in detecting local damage. For practical applications, various external factors such as fluctuations of temperature and loading, affecting the effectiveness of the EMI technique ought to be understood and compensated. This paper aims at investigating the damage monitoring capability of EMI technique in the presence of axial stress with fixed boundary condition. A compensation technique using effective frequency shift (EFS) by cross-correlation analysis was incorporated to compensate the effect of loading and boundary stiffening. Experimental tests were conducted by inducing damages on lab-sized aluminium beams in the presence of tensile and compressive forces. Two types of damages, crack propagation and bolts loosening were simulated. With EFS for compensation, both cross-correlation coefficient (CC) index and reduction in peak frequency were found to be efficient in characterizing damages in the presence of varying axial loading.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.263-266
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• 2019

In this paper, we suggest an activity and health monitoring system to observe the status of the dogs in real time. We also propose a k-days algorithm which helps monitoring pet health status using classified activity data from a machine learning approach. One of the best machine learning algorithm is used for the classification activity of dogs. Dog health status is acquired by comparing current activity calculation with passed k-days activities average. It is considered as a good, warning and bad health status for differences between current and k-days summarized moving average (SMA) > 30, SMA between 30 and 50, and SMA < 50, respectively.

• Smart Structures and Systems
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• v.33 no.1
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• pp.17-26
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• 2024

The new metro crossing the existing metro will cause the settlement or floating of the existing structures, which will have safety problems for the operation of the existing metro and the construction of the new metro. Therefore, it is necessary to monitor and predict the settlement of the existing metro caused by the construction of the new metro in real time. Considering the complexity and uncertainty of metro settlement, a Gaussian Prior Bayesian Emulator (GPBE) probability prediction model based on Bayesian optimization (BO) is proposed, that is, BO-GPBE. Firstly, the settlement monitoring data are analyzed to get the influence of the new metro on the settlement of the existing metro. Then, five different acquisition functions, that is, expected improvement (EI), expected improvement per second (EIPS), expected improvement per second plus (EIPSP), lower confidence bound (LCB), probability of improvement (PI) are selected to construct BO model, and then BO-GPBE model is established. Finally, three years settlement monitoring data were collected by structural health monitoring (SHM) system installed on Nanjing Metro Line 10 are employed to demonstrate the effectiveness of BO-GPBE for forecasting the settlement.

• Journal of Advanced Navigation Technology
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• v.18 no.6
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• pp.625-632
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• 2014

The u-health service is using portable device such as smart device and it consists of small computing device. The u-health service carry out same performance with desktop computer. We designed message structure based on Bluetooth HDP. This message structure is used to transmit patient's biometric data on the smart device of medical team, patient and family over the mobile network environment. ISO/IEEE 11073 PHD standard was defined based on the method of communication between the agent and the manager. And We are confirmed the reliable transmission of biometric data at the smart device by implementing the android OS based patient information monitoring application to check the status of patient for medical team, patient and family.

• Smart Structures and Systems
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• v.5 no.4
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• pp.427-442
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• 2009

In this paper, the acceptability of system identification results for health monitoring of instrumented bridges is addressed. This is conducted by comparing the confidence intervals of identified modal parameters for a bridge in California, namely Truckee I80/Truckee river bridge, with the change of these parameters caused by several damage scenarios. A challenge to the accuracy of the identified modal parameters involves consequences regarding the damage detection and health monitoring, as some of the identified modal information is essentially not useable for acquiring a reliable damage diagnosis of the bridge system. Use of strong motion data has limitations that should not be ignored. The results and conclusions underline these limitations while presenting the opportunities offered by system identification using strong motion data for better understanding and monitoring the health of bridge systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1791-1798
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• 2016

Recently, research about ESS(Emergency Support System) has been actively carried out to provide a variety of medical services using smart devices and wearable devices. Smart healthcare provides a personalized health care service using various types of bio-signal measuring sensors and smart devices. For the smart healthcare using a smart device, it is need to research about personal health monitoring using a smart wearable devices, and also need to research on service methods for first aid measures after an emergency. In this paper, we proposed about group management based emergency support system, that is monitoring about personal bio signal using smart devices and wearable devices to protect patient's life. The system notices to the medical volunteers based on the position information when an emergency situation. In addition, we have designed and implemented an emergency support system providing the information of the patient on the display when transmitting a picture of a patient using a smart device to the server.

• Computers and Concrete
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• v.23 no.6
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• pp.399-408
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• 2019

Structural health monitoring is important for the safety of lives and asset management. In this study, numerical models were developed for the piezoresistive behavior of smart concrete based on finite element (FE) method. Finite element models were calibrated with experimental data collected from compression test. The compression test was performed on smart concrete cube specimens with 75 mm dimensions. Smart concrete was made of cement CEM II 42.5 R, silica fume, fine and coarse crushed limestone aggregates, brass fibers and plasticizer. During the compression test, electrical resistance change and compressive strain measurements were conducted simultaneously. Smart concrete had a strong linear relationship between strain and electrical resistance change due to its piezoresistive function. The piezoresistivity of the smart concrete was modeled by FE method. Twenty-noded solid brick elements were used to model the smart concrete specimens in the finite element platform of Ansys. The numerical results were determined for strain induced resistivity change. The electrical resistivity of simulated smart concrete decreased with applied strain, as found in experimental investigation. The numerical findings are in good agreement with the experimental results.