• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.1
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• pp.1-9
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• 2015

In this study it was adopted on sea dike monitoring that the safety monitoring with statistical limits which was adapted usually on safety monitoring by measuring pressures, stresses or deformations. And also the hydraulic head loss rate change according to passage of time was calculated for the purpose of safety monitoring. Safety monitoring by setting the statistical limit on the measured pore water pressure graphs need to be supplemented with an additional method of monitoring because the difference between the rise and fall of the tide was irregular. Safety monitoring by the limits set from values predicted by linear regression and standard errors on the hydraulic head loss graph was not affected by irregularity of tide. But if the condition of an embankment is changed gradually and slowly, it will not be detected on the hydraulic head loss graph. The graph of hydraulic head loss rate for every 24 hours vs date showed clearly that the sea water blocking state was getting better or not even though it was changed gradually and slowly.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.105-113
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• 2014

Most operating civil structures measure response data continuously by various types of sensors and evaluate their health conditions. Measurement control criteria for such civil structures are usually defined in the first operating stage by experts working at a construction or engineering company. However, a few studies have been carried to examine the adequacy of these measurement control criteria based on the actual measured data. The paper introduces a systematic way of resetting the measurement control criteria for the measured monitoring data based on the statistical aspects of the measured data. The proposed statistical approach has been examined with actually measured time-history data from a bridge structure.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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• pp.556-562
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• 2007

This paper presents the use of a novel fiber optic accelerometer system to monitor ambient vibration (both wind-induced one and vehicle-induced) of a real bridge structure. This sensor system integrates the $Moir\'{e}$ fringe phenomenon with fiber optics to achieve accurate and reliable measurements. A low-cost signal processing unit implements unique algorithms to further enhance the resolution and increase the dynamic bandwidth of the sensors. The fiber optic accelerometer has two major benefits in using this fiber optic accelerometer system for monitoring civil engineering structures. One is its immunity to electromagnetic (EM) interference making it suitable for difficult applications in such environments involving strong EM fields, electrical spark-induced explosion risks, and cabling problems, prohibiting the use of conventional electromagnetic accelerometers. The other is its ability to measure both low- and high-amplitude vibrations with a constantly high resolution without pre-setting a gain level, as usually required in a conventional accelerometer. The second benefit makes the sensor system particularly useful for real-time measurement of both ambient vibration (that is often used for structural health monitoring) and strong motion such as earthquake. Especially, the semi-strong motion and the small ambient one are successfully simulated and measured by using the new fiber optic accelerometer in the experiment of the structural health monitoring of a real bridge.

• Journal of Environmental Science International
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• v.20 no.6
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• pp.783-788
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• 2011

Different from the main land of South Korea, Jeju Island has been in difficulties for measuring discharge. Due to high infiltration rate, most of streams in Jeju Island are usually in the dried state except six streams with the steady base flow, and the unique geological characteristics such as steep slope and short traveling distance of runoff have forced rainfall runoff usually to occur during very short period of time like one or two days. While discharge observations in Jeju Island have been conducted only for 16 sites with fixed electromagnetic surface velocimetry, effective analysis and validation of observed discharge data and operation of the monitoring sites still have been limited due to very few professions to maintain such jobs. This research is sponsored by Ministry of Land, Transport and Maritime Affairs to build water cycle monitoring and management system of Jeju Island. Specifically, the research focuses on optimizing discharge measurement techniques adjusted for Jeju Island, expanding the monitoring sites, and validating the existing discharge data. First of all, we attempted to conduct discharge measurements in streams with steady base flow, by utilizing various recent discharge monitoring techniques, such as ADCP, LSPIV, Magnetic Velocimetry, and Electromagnetic Wave Surface Velocimetry. ADCP has been known to be the most accurate in terms of discharge measurement compared with other techniques, thus that the discharge measurement taken by ADCP could be used as a benchmark data for validation of others. However, there are still concerns of using ADCP in flood seasons; thereby LSPIV would be able to be applied for replacing ADCP in such flooded situation in the stream. In addition, sort of practical approaches such as Magnetic Velocimetry, and Electromagnetic Wave Surface Velocimetry would also be validated, which usually measure velocity in the designated parts of stream and assume the measured velocity to be representative for whole cross-section or profile at any specified location. The result of the comparison and analysis will be used for correcting existing discharge measurement by Electromagnetic Wave Surface Velocimetry and finding the most optimized discharge techniques in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1706-1712
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• 2012

The Beta-ray absorption method (BAM) gives a good solution for measuring the mass concentration of atmospheric particles(PM10 and PM2.5). To determine particular matters (PM) concentration, a ratio of the number of detected beta-ray intensity passing through the clean filter and the dust-sampled filter is used. These intensity data measured in air pollution monitoring such as PM10 and PM2.5 usually contained the additive noise(thermal noise, power supply noise and etc.). Therefore, the estimation performance of mass concentration can be deteriorated by these noises. In this paper, we present a new noise reduction method that is essentially required to develope an automatic continuous PM monitoring system using beta-ray absorption method. By combining the block data averaging technique and curve fitting, in the proposed method, the additive noise can be reduced in the measured data. To evaluate the performance of the proposed method, computer simulations were performed with computer generated signals as the input.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.3
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• pp.93-100
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• 2014

This study presents the recomposition of MBM (measurement-based model) using natural frequencies and modes from the usually measured data, and the evaluation of cable tension in service from the analysis results upon MBM of existing CSB (cable stayed bridge). The cable tension is shown to be different according to the position attached to cable and loading type. The measured cable tensions are not different distinctly according to position attached cable under dead and live loads, but larger than those under design loads. The distribution of cable tension calculated upon the MBM is similar to those of measured tension although the former is more than those of cable tension upon the design model. Considering to long-term behaviors of cable, therefore, the design of cable in CSB needs to apply the analysis results on MBM. For this purpose, future study needs lots of measured data and MBM is used to analyze the long-term behavior of cable in CSB.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.6
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• pp.709-715
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• 2011

Chattering in cutting operations are usually a cumbersome part of the manufacturing process in mechanical. Particular, machining performance such as that of the boring process is limited by cutting condition at the movable components. Among various sources of chatter vibration, detrimental point in cutting condition is found a mechanical condition on overhang. It limits cutting speed, depth, surface roughness and tool wear failure as result because the all properties are varying with the metal removal process. In this case, we have to observe the resonance frequencies of a boring bar for continuous cutting. In the established research, boring bar vibration of cutting system has been measured with the aid of accelerometer. However, the inherent parameters of internal turning operations are severely limit for the real time monitoring on accelerometers. At this point, this paper is proposed other method for real time monitoring during continuous cutting with optical fiber at the inside of boring bar. This method has been used a plastic fiber in the special jig on boring bar by based on experimental modal analysis. In this study, improvement of monitoring system on continuous internal cutting was attempted using optical fiber sensor of inside type because usually chattering is investigated experimentally measuring the variation in chip thickness. It is demonstrated that the optical fiber sensor is possibility to measure of chattering with real time in boring process.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1315-1318
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• 2003

Wastewater treatment processes are usually located in the outskirts of cities. But these processes should be dealt with continuous maintenance by expert operators. Therefore, in this paper, unmaned and automated control system is designed for the SBR(Sequencing Batch Reactor) plant. This plant is constructed in Gimhae city. Networks and wireless modules are employed for the data transmission. A local controller is in the SBR plant as a client and a monitoring system is located in the other place as a server. Remote control and monitoring system are constructed at the laboratory of ours. Measured data from plant sensors are translated to the remote site using communication modules, and then the data could be displayed and analyzed by means of remote monitoring and control systems.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.113-118
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• 2009

It is of utmost important that track loads at rail induced by running train was usually obtained for track performance evaluation. A reaction force of track was measured wheel loads and lateral force by strain gauges on the rail, and then it was obtained to derailment coefficient and variational ratio of wheel load from its relationship. In this study, a existing monitoring system methods with many manpower working were reviewed about measurements, process of testing, and how to obtain accurately measured data.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.