• Smart Structures and Systems
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• v.14 no.2
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• pp.145-158
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• 2014

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

• Smart Structures and Systems
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• v.21 no.2
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• pp.225-234
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• 2018

E-Health allows you to supersede the central patient wireless healthcare system. Wireless Body Sensor Network (WBSN) is the first phase of the e-Health system. In this paper, we aim to understand e-Health architecture and configuration, and attempt to minimize energy consumption and latency in transmission routing protocols during restrictive latency in data delivery of WBSN phase. The goal is to concentrate on polling protocol to improve and optimize the routing time interval and schedule communication to reduce energy utilization. In this research, two types of network models routing protocols are proposed - elemental and clustering. The elemental model improves efficiency by using a polling protocol, and the clustering model is the extension of the elemental model that Destruct Supervised Decision Tree (DSDT) algorithm has been proposed to solve the time interval conflict transmission. The simulation study verifies that the proposed models deliver better performance than the existing BSN protocol for WBSN.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.792-798
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• 2015

Joint angle generally can be measured to check the recovery process in rehabilitation field. This paper suggests to measure joint angle with Infrared sensor based on triangulation principle. We performed various experiments to find the optimal condition of the sensor’s attached distance, height, and angle. The results were compared with commercial goniometer for accuracy. The proposed infrared joint angle sensor can be effectively used in rehabilitation and sport science fields.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2006.03a
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• pp.613-619
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• 2006

This paper introduces a new structural health monitoring using a nano sensor. The sensor is made of nano smart composite material based on carbon nanotubes. The nano sensor is fabricated as a thin and narrow polymer film sensor that is bonded or deposited onto a structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensorcan form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods.

• ETRI Journal
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• v.43 no.3
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• pp.511-523
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• 2021

The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser-based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor-based sensor or tapered element oscillating microbalance-based sensor. However, an LLS-based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP-GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS-based PM measuring sensors. We conclude that our HP-GAN is a cutting-edge model for anomaly detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1832-1853
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• 2018

The personal health record (PHR) system is a promising application that provides precise information and customized services for health care. To flexibly protect sensitive data, attribute-based encryption has been widely applied for PHR access control. However, escrow, exposure and abuse of private keys still hinder its practical application in the PHR system. In this paper, we propose a coordinated ciphertext policy attribute-based access control with user accountability (CCP-ABAC-UA) for the PHR system. Its coordinated mechanism not only effectively prevents the escrow and exposure of private keys but also accurately detects whether key abuse is taking place and identifies the traitor. We claim that CCP-ABAC-UA is a user-side lightweight scheme. Especially for PHR receivers, no bilinear pairing computation is needed to access health records, so the practical mobile PHR system can be realized. By introducing a novel provably secure construction, we prove that it is secure against selectively chosen plaintext attacks. The analysis indicates that CCP-ABAC-UA achieves better performance in terms of security and user-side computational efficiency for a PHR system.

• International Journal of High-Rise Buildings
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• v.4 no.1
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• pp.27-37
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• 2015

Structural health monitoring is becoming more and more important in the domain of civil engineering as a proper mean to increase and maintain the safety, especially in the land of earthquakes like Japan. In many civil structures, the deformations are the most relevant parameter to be monitored. In this context, a monitoring technology based on the use of long-gage fiber optic deformation sensor, SOFO is being applied to a 33-floors tall building in Tokyo. Sensors were installed on the $2^{nd}$ floor's steel columns of the building on May 2005 in the early stage of the construction. The installed SOFO sensors were dynamic compatible ones which enable both static and dynamic measurements. The monitoring is to be performed during the whole lifespan of the building. During the construction, static deformations of the columns had been measured on a regular basis using a reading unit for static measurement and dynamic deformation measurements were occasionally conducted using a reading unit for dynamic measurement. The building was completed on August 2006. After the completion, static and dynamic deformation measurements have been continuing. This paper describes a health monitoring technology, SOFO system which is applicable to high-rise buildings and monitoring results of a 33-floors tall building in Tokyo from May 2005 to October 2010.

• Smart Structures and Systems
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• v.6 no.3
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• pp.335-347
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• 2010

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.

• Journal of Sensor Science and Technology
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• v.32 no.5
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• pp.259-266
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• 2023

The use of various adulterants and harmful chemicals is rapidly increasing in various sectors such as agriculture, food, and pharmaceuticals, and they are also present in our surroundings in the form of pollutants. The regular and repeated intake of harmful chemicals often adversely affects human health. The prolonged exposure of living beings to such adverse components can lead to severe health complications. To avoid the unlimited utilization of these chemical components, a sensing technology that is sensitive and reliable for low-concentration detection is beneficial. Surface-enhanced Raman spectroscopy (SERS) is a powerful method for identifying low-range concentrations of analytes, leading to great applications in molecular identification, including various diagnostic biomarkers. SERS in chemical, gas, and biological sensors can be an excellent approach in the sensing world to achieve rapid and multiple-analyte detection, leading to a new and efficient approach in healthcare monitoring.