• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.35-43
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• 2012

In general, load carrying capacity, one of the load & resistance capacities in bridges, has more margins than the load carrying capacity evaluated with theoretical methods, unless there are severe damages, defects or material deterioration phenomena that can have a great impact on the behavior of bridges. However, errors have been already included in the current processes of loading tests and structural analysis for measuring load carrying capacity, thus devaluing the reliability of response adjustment factor. Therefore, this study found out the problems of existing electric resistance strain and displacement sensors in sensor suite to solve the problems with sensors and the errors in the appropriateness of structural analysis model, thereby leading to the changes into an optical fiber smart sensor with excellent performance. Besides, the study attempted to ensure the accuracy of response adjustment factor by selecting the optimal models through the interpretation of various structural analysis models.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.687-692
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location caused by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• Explosives and Blasting
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• v.29 no.1
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• pp.41-47
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• 2011

In order to estimate blast damage zone and control rock fragmentation in blasting, it is important to obtain information regarding blast hole pressure. In this study, drop impact tests of acrylic, aluminium, steel sensors were performed to investigate the dynamic response characterizations of the sensors through the strain signals. As a result, the strain signals obtained from the steel sensors showed less sensitivity to impact force level and experienced small changes with various length of the sensors. The steel sensors were applied to measure the impact force of an electric detonator.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.11
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• pp.1143-1149
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• 2008

In a nuclear power plant, loose part monitoring and its diagnostic technique is one of the major issues for ensuring the structural integrity of the reactor system. Typically, accelerometers are mounted on the surface of a reactor vessel to localize impact location cavsed by the impact of metallic substances on the reactor system. However, in some cases, the number of the accelerometers is not enough to estimate the impact location precisely. In such a case, one of alternative plan is to utilize another type sensors that can measure the vibration of the reactor structure even though the measuring frequency ranges are different from each others. The AE sensors installed on the reactor structure can be utilized as additional sensors for loose part monitoring. In this paper, we proposed a new method to estimate impact location by using both accelerometer signal and AE signal, simultaneously. The feasibility of the proposed method is verified by an experiment. The experimental results demonstrate that we can enhance the reliability and precision of the loose part monitoring.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.4B
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• pp.338-348
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• 2009

The Wireless Sensor Network (WSN) has recently attracted considerable attention due to the low price and ease to deploy it. In particular, in a hostile or harsh regions where sensors cannot be deployed manually, WSNs can be established just by dropping sensors from the air. In this case, however, most likely sensors are not placed at optimal positions, although the location of sensors does have a drastic impact on the WSN performance. Moreover, randomized deployment algorithm can leave holes in terms of coverage in the sensing area. This paper proposes a sensor relocation scheme where mobile sensors move to patch up the holes by appropriate coverage. Simulation results show that the proposed algorithm outperforms prior existing schemes in terms of coverage and lifespan of WSNs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.3
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• pp.83-89
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• 2010

This paper presents the impacts of Ultra Wide-Band(UWB) sensor using frequency of 4.5 GHz on Broadband Wireless communication system which uses frequency of 4.5 GHz. The Minimum Coupling Loss (MCL) method and Spectrum Engineering Advanced Monte Carlo Analysis Tool (SEAMCAT) is used to evaluate the interference effects of UWB sensor on Broadband Wireless communication system, respectively. The minimum protection distance between single UWB sensor and mobile station of Broadband Wireless communication system should be more than 1.2 m to guarantee the co-existence. In case of multiple UWB sensors, UWB transmitting PSD of around -68.5 dBm/MHz below should be required to guarantee interference probability of 5% below for mobile station of Broadband Wireless communication system.

• ETRI Journal
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• v.39 no.5
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• pp.683-694
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• 2017

Several studies have focused on multimedia transmission over wireless sensor networks (WSNs). In this paper, we propose a comprehensive and robust model to transmit images over cognitive radio WSNs (CRWSNs). We estimate the spectrum sensing frequency and evaluate its impact on the peak signal-to-noise ratio (PSNR). To enhance the PSNR, we attempt to maximize the number of pixels delivered to the receiver. To increase the probability of successful image transmission within the maximum allowed time, we minimize the average number of packets remaining in the send buffer. We use both single- and multi-channel transmissions by focusing on critical transmission events, namely hand-off (HO), No-HO, and timeout events. We deploy our advanced updating method, the dynamic parameter updating procedure, to guarantee the dynamic adaptation of model parameters to the events. In addition, we introduce our ranking method, named minimum remaining packet best channel selection, to enable us to rank and select the best channel to improve the system performance. Finally, we show the capability of our proposed image scrambling and filtering approach to achieve noticeable PSNR improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1324-1327
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• 2009

Social infrastructure is the basis of public welfare and should be recognized and managed as important assets. Bridge is one of the most important infrastructures to be managed systematically because the impact of the failure is critical. It is essential to monitor the performance of bridges in order to manage them as an asset. But current analytical methods such as predictive modeling and structural analysis are very complicated and difficult to use in practice. To apply these methods, structural and material condition data collection should be performed in each element of bridge. But it is difficult to collect these detailed data in large numbers and various kinds of bridges. Therefore, it is necessary to collect data of major measurement items and predict the life of bridges roughly with advanced information technologies. When certain measurement items reach predefined limits in the monitoring bridges, precise performance measurement will be done by detailed site measurement. This paper describes the selection of major measurement items that can represent the tendency of bridge life and introduces automated bridge data collection test-bed using wireless sensor network technology. The following will be major parts of this paper: 1) Examining the features of conventional bridge management system and data collection method 2) Mileage concept as a bridge life indicator and measuring method of the indicator 3) Test-bed of automated and real-time based bridge life indicator monitoring system using wireless sensor network

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.4
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• pp.344-365
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• 2009

The performance of a tracking system is greatly increased if multiple types of sensors are combined to achieve the objective of the tracking instead of relying on single type of sensor. To conduct the multi-modal tracking, we have previously developed a multi-modal sensor-based tracking model where acoustic sensors mainly track the objects and visual sensors compensate the tracking errors [1]. In this paper, we find a network synchronization problem appearing in the developed tracking system. The problem is caused by the different location and traffic characteristics of multi-modal sensors and non-synchronized arrival of the captured sensor data at a processing server. To effectively deliver the sensor data, we propose a time-based packet aggregation algorithm where the acoustic sensor data are aggregated based on the sampling time and sent to the server. The delivered acoustic sensor data is then compensated by visual images to correct the tracking errors and such a compensation process improves the tracking accuracy in ideal case. However, in real situations, the tracking improvement from visual compensation can be severely degraded due to the aforementioned network synchronization problem, the impact of which is analyzed by simulations in this paper. To resolve the network synchronization problem, we differentiate the service level of sensor traffic based on Weight Round Robin (WRR) scheduling at the routers. The weighting factor allocated to each queue is calculated by a proposed Delay-based Weight Allocation (DWA) algorithm. From the simulations, we show the traffic differentiation model can mitigate the non-synchronization of sensor data. Finally, we analyze expected traffic behaviors of the tracking system in terms of acoustic sampling interval and visual image size.