• 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.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.197-205
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• 2022

The Cyber Physical System(CPS) is an important concept in achieving SMSs(Smart Manufacturing Systems). Generally, CPS consists of physical and virtual elements. The former involves manufacturing devices in the field space, whereas the latter includes the technologies such as network, data collection and analysis, security, and monitoring and control technologies in the cyber space. Currently, all these elements are being integrated for achieving SMSs in which we can control and analyze various kinds of producing and diagnostic issues in the cyber space without the need for human intervention. In this study, we focus on implementing a production equipment monitoring system related to building a SMS. First, we describe the development of a fog-based gateway system that links physical manufacturing devices with virtual elements. This system also interacts with the cloud server in a multimedia network environment. Second, we explain the proposed network infrastructure to implement a monitoring system operating on a cloud server. Then, we discuss our monitoring applications, and explain the experience of how to apply the ML(Machine Learning) method for predictive diagnostics.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.427-427
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• 2021

During the rainy season, heavy downpours are always a source of concern for the world. Flooding and heavy rains can devastate communities, disrupt agriculture, and contribute to traffic accidents.. Weir and flow hall effect sensors are the conventional analytical methods for measuring flow rate; in this paper, we analyzed manhole flowrate statistics. The measurement of the flow rate of a notch/weir is a time-consuming task that necessitates continuous mathematical analysis. . We created three types of IoT sensors in this study: (HC-SR04 ultrasonic, YF-S201 magnetic, and HB100 radar), which take the sensor's real-time input signal and estimate the flow using a notch equation and a previously calibrated optimized coefficient of discharge. The proposed systems are cost-effective, but in terms of accuracy, we found that the HC-SR04 ultrasonic sensor is the best of the three systems

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.393-410
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• 2012

The 610 m high Canton Tower (formerly named Guangzhou New Television Tower) is currently considered as a benchmark problem for structural health monitoring (SHM) of high-rise slender structures. In the benchmark study task I, a set of 24-hour ambient vibration measurement data has been available for the output-only system identification study. In this paper, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower. The identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique. Furthermore, the environmental effects on the estimated modal parameters are also discussed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.97-98
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• 2014

Lots of investment projects of new development and redevelopment for information systems have been not taken care of in the field of administration and evaluation, for these information systems projects have unique characteristics such as technology sensitiveness, network effectiveness, embeddedness, and externality. In fact, quantitative and qualitative evaluation of investments in information systems projects are not sufficient. It is critically important to generally evaluate benefits of development or operation cost, urgency, external effects, and so on. In addition, the efficient monitoring and effective analysis of information systems are surely needed for beneficient results of investment in information systems. We propose an economics evaluation model for information systems projects.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.738-743
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• 2012

This paper describes analysis of long-term experiments on a monitoring system to assess the daily activities of the elderly who live alone. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system installed in their own houses is a typical wireless sensor network system including three kinds of wireless sensors. The server system has a database server and an assessment server. We have installed the system into an elderly house, collected data during over two years continuously, then analyze the data. From the analysis, we could measure the energy consumption profile of three kinds of sensor nodes. The experiment shows all kinds of nodes can operate over one year with two AA-size alkaline batteries. Using a measure of reliability of the monitoring system called 'deadzone', the system has showed the failure operation for 842 hours (4.66 %) during over 18,000 hours total operation period.

• Atmosphere
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• v.30 no.2
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• pp.103-113
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• 2020

In this study, we analyzed and developed the monitoring system in order to confirm the effect of observations on forecast sensitivity on ensemble-based data assimilation. For this purpose, we developed the Ensemble Forecast Sensitivity to observation (EFSO) monitoring system based on Local Ensemble Transform Kalman Filter (LETKF) system coupled with Korean Integrated Model (KIM). We calculated 24 h error variance of each of observations and then classified as beneficial or detrimental effects. In details, the relative rankings were according to their magnitude and analyzed the forecast sensitivity by region for north, south hemisphere and tropics. We performed cycle experiment in order to confirm the EFSO result whether reliable or not. According to the evaluation of the EFSO monitoring, GPSRO was classified as detrimental observation during the specified period and reanalyzed by data-denial experiment. Data-denial experiment means that we detect detrimental observation using the EFSO and then repeat the analysis and forecast without using the detrimental observations. The accuracy of forecast in the denial of detrimental GPSRO observation is better than that in the default experiment using all of the GPSRO observation. It means that forecast skill score can be improved by not assimilating observation classified as detrimental one by the EFSO monitoring system.

• KIEAE Journal
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• v.4 no.1
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• pp.11-20
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• 2004

Innovative daylighting systems in buildings in various climatic zones around the world have been developed under the IEA SHC Task21. The performance assessment were obtained by monitoring the most systems using full-scale test model rooms or actual buildings under real sky conditions. This study aims to analyze the configuration and monitoring system of the nine Mock-up models of the IEA SHC Task21 comparatively. For the purpose, the geometry of the test rooms (length, width, height, window area, glazed area and occupied), reflectance of walls, floor and ceiling, transmittance of glazing (transmittance for hemispherical irradiation, normal irradiation and U-value) were compared. And equipment for measurement (manufacturer, range, calibration, maximum calibration error, cosine response error, fatigue error), and data acquisition system (manufacturer, type, number of differential analogue input channels, A/D converter resolution in bits, data acquisition software) were also analyzed comparatively. Some findings of these experimental methodology of standard monitoring have been proven to be a valuable one for future assessment of advanced daylighting systems in our country.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Fire Science and Engineering
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• v.30 no.4
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• pp.103-110
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• 2016

While the demand for the gas system fire extinguishers increases every year, there are insufficient safety measures for assessing the extinguishing performance, such as system safety and reliability in the preparation of increasing demand, which has emerged as a social problem. One of the most critical causes of accidents occurring with the gas extinguishing system is pressure leakage from the extinguishing agent storage container. This is considered to be one of the critical factors on which the success of fire suppression depends. In this study, its safety measure was studied, Because it was deemed urgently necessary. The newly developed pressure leakage monitoring system is a system monitoring storage condition, pressure, leakage and discharge of the storage container related to agent concentration, which is one of the critical factors for fire suppression. This was developed to be applicable to the $CO_2$ and HFC-23 systems. Therefore, for structural safety analysis, the safety performance was verified by the fluid structure coupling analysis of the safety problems that may occur when the pressure leakage monitoring system is applied to the gas fire extinguisher. For analysis programs, the FloEFD program from Mentor Graphics was used for computational fluid dynamics analysis and ABAQUS from Dassault Systems was used for structural analysis. From the result of numerical analysis, the structure of $CO_2$ did not develop plastic deformation and its safety was verified. However, plastic deformation and deviation issue occurred with the HFC-23 monitoring system and therefore verified the structural safety of pressure leakage monitoring system by data obtained from redesigning and adjusting the condition of numerical interpretation three times.