• Smart Structures and Systems
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• v.26 no.5
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• pp.591-603
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• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• ETRI Journal
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• v.33 no.6
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• pp.857-863
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• 2011

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.930-936
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• 2008

CMOS image monitoring system for optimal measuring the activity of biosensor is developed using genetic algorithm. Most of living organism in water as water flea, fish, etc are frequently used as biological sensor for monitoring the water quality. It is very difficult to measure the activity of biosensor by image sensor because the value of measurement is varied with gathering method of biosensor images. The suggested monitoring system can optimally measures the activity of biosensor by genetic algorithm. The system is implemented with FPGA into the small hardware which is excellent in terms of the price and performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.449-450
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• 2015

This paper describes performance evaluation results of a new dynamic bandwidth allocation algorithm for an XG-PON (10-Gbps-capable passive optical network) system without using an explicit request. An ONU (optical network unit) does not send its request to an OLT (optical line termination). The OLT monitors the upstream bandwidth usage of the ONU to estimate the request of the ONU.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.743-744
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• 2008

By using DirectX technology, an improved Visual Monitoring System implemented in this paper. The proposed Visual Monitoring System is developed based on the S3C2440 processor. The Windows CE 5.0 is adopted as an operating system, and Visual Monitoring System transfer image 15 frame per second using UDP/IP and by using AdaBoost Algorithm, detect face region and save face image.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.89-110
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• 2014

Structural Health Monitoring (SHM) is an effective alternative to conventional inspections which are time-consuming and subjective. SHM can detect damage early and reduce maintenance cost and thereby help reduce the likelihood of catastrophic structural events to infrastructure such as bridges. After reviewing the Damage Index Method (DIM), an Iterative Damage Index Method (IDIM) is proposed to improve the accuracy of damage detection. These two damage detection techniques are compared based on damage on two structures, a simply supported beam and a pedestrian bridge. Compared to the traditional damage detection algorithm, the proposed IDIM is shown to be less arbitrary and more accurate.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.146-153
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• 2009

One of the most important issues in the wearable healthcare sensors is to minimize the motion artifacts in the vital signals for continuous monitoring. This paper presents a reflected type photoplethysmograph (PPG) sensor for monitoring heart rates at the artery of the wrist. Active noise cancellation algorithm was applied to compensate the distorted signals by motions with Least Mean Square (LMS) adaptive filter algorithms, using acceleration signals from a MEMS accelerometer. Experiments with a watch type PPG sensor were performed to validate the proposed algorithm during typical daily motions such as walking and running. The developed sensor is suitable for ubiquitous healthcare system and monitoring vital arterial signals during surgery.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.45-45
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.769-775
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.