• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.55-64
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• 2016

The development of high-speed rail (HSR) has had a major impact on the power supply grid. Based on the monitored waveforms of HSR, a pattern recognition approach is proposed for the first time in this paper to identify the operating conditions. To reduce the data dimensions for monitored waveforms, the principal component analysis (PCA) algorithm was used to extract the characteristics and their waveforms from the monitored waveforms data. The dynamic time wrapping (DTW) algorithm was then used to identify the operating conditions of the HSR. Cases studies show that the proposed approach is effective and feasible, and that it is possible to identify the real-time operating conditions based on the monitored waveforms.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

• Journal of Korean Society of Rural Planning
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• v.2 no.2
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• pp.91-102
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• 1996

Monitoring techniques for afro-ecological environments were studied, Hydrologic and ecological components in conjunction with water quality were monitored in the Balkan watershed. The hydrologic monitoring program consists of four water level gauging stations along creeks and stream at the watershed having 26.5 km2. Stage - storage relationship of reservoir, rainfall amount of the watershed, and rating curve of the stream gauging stations were established. Soil type, land use, hydrologic soil group, population and economic activities within the watershed were surveyed. Water quality data from the streams were sampled weekly and chemical analysis was conducted. Temporal variations of water quality were investigated and water quality map of each reach of stream was made to identify spatial variations. Seasonal and spatial variations of vegetation densities along stream in the watershed were investigated using grid, Density variations of insect species such as arthropod, flying insect, spider spices, rice insects were also monitored to determine seansonal surveying density. These monitored data will be used to develop monitoring techi%ues and afro - ecological environment models.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.92-101
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• 2015

As one type of power quality (PQ) disturbance sources, high-speed rail (HSR) can have major impacts on the power supply grid. Providing timely and accurate warning information for PQ problems of HSR is important for the safe and stable operation of traction power supply systems and the power supply grid. This study proposes a novel warning approach to identify PQ problems and provide warning prompts based on the monitored data of HSR. To embody the displacement and status change of monitored data, multi-features of different sliding windows are computed. To reflect the relative importance degree of these features in the overall evaluation, an analytic hierarchy process (AHP) is used to analyse the weights of multi-features. Finally, a multi-features similarity algorithm is applied to analyse the difference between monitored data and the reference data of HSR, and PQ warning results based on dynamic thresholds can be analysed to quantify its severity. Cases studies demonstrate that the proposed approach is effective and feasible, and it has now been applied to an actual PQ monitoring platform.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.6
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• pp.111-119
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• 2012

Applicability of 8 day interval flow data for the calibration of hydrologic model was evaluated using Hydrological Simulation Program-Fortran (HSPF) at Kyungan watershed. The 8 day interval flow monitored by Ministry of Environment located at upstream was calibrated and periodically validated during 2004-2008. And continuous daily flow monitored by Ministry of Construction & Transportation (MOCT) and located at the mouth was compared with daily simulated data during 2004-2007 as spatial validation. Automatic calibration tool which is Model-Independent Parameter Estimation & Uncertainty Analysis (PEST) was applied for HSPF calibration procedure. The model efficiencies for calibration and periodic validation were 0.63 and 0.88, and model performances were fair and very good, respectively, based on criteria of calibration tolerances. Continuous daily stream flow at the mouth of Kyungan watershed were good agreement with observed continuous daily stream flow with showing 0.63 NS value. The PEST program is very useful tool for HSPF hydrologic calibration using non-continuous daily stream flow as well as continuous daily stream flow. The 8 day interval flow data monitored by MOE could be used to calibrate hydrologic model if the continuous daily stream flow is unavailable.

• Smart Structures and Systems
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• v.14 no.2
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• pp.209-224
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• 2014

With more and more built long-term structural health monitoring (SHM) systems, it has been considered to apply monitored data to learn the reliability of bridges. In this paper, based on a long-term SHM system, especially in which the sensors were embedded from the beginning of the construction of the bridge, a method to calculate the localized reliability around an embedded sensor is recommended and implemented. In the reliability analysis, the probability distribution of loading can be the statistics of stress transferred from the monitored strain which covered the effects of both the live and dead loads directly, and it means that the mean value and deviation of loads are fully derived from the monitored data. The probability distribution of resistance may be the statistics of strength of the material of the bridge accordingly. With five years' monitored strains, the localized reliabilities around the monitoring sensors of a bridge were computed by the method. Further, the monitored stresses are classified into two time segments in one year period to count the loading probability distribution according to the local climate conditions, which helps us to learn the reliability in different time segments and their evolvement trends. The results show that reliabilities and their evolvement trends in different parts of the bridge are different though they are all reliable yet. The method recommended in this paper is feasible to learn the localized reliabilities revealed from monitored data of a long-term SHM system of bridges, which would help bridge engineers and managers to decide a bridge inspection or maintenance strategy.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1464-1468
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• 2007

The restoration project of Cheonggyecheon was conducted to creates the refreshing water-friendly environment in the downtown Seoul. It already have passed almost 2 years after restoration. This project changed environment of Cheonggyecheon dramatically, so historic hydrological data became useless. There are not so many hydrological data to manage and control this newly restored urban stream. The main purpose of this study is collecting and analysing the hydrological data of Cheonggyecheon. At first, we analysed the mechanism of Cheonggyecheon discharge using the sewage design maps and some GIS data. We also monitored the water levels and discharges of 5 main points of Cheonggyecheon. Rating curves of these 5 points were derived. There were 249 blocks of water gates which were located at both sides of bank. We also monitored the behaviors of these water gates. Through the these monitorings, some equations were derived to give useful information to the manager of Cheonggyecheon.

• Journal of the Korean Geotechnical Society
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• v.18 no.3
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• pp.113-125
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• 2002

Although there are several empirical and semi-empirical formulae available for predicting ground surface settlement, most of them do not simultaneously take into consideration all the relevant factors, resulting in inaccurate predictions. In this study, an artificial neural network (ANN) is incorporated with 113 of monitored field results to predict surface settlement for a tunnel site with prescribed conditions. To achieve this, a format for a database of monitored field data is first proposed and then used for sorting out a variety of monitored data sets available in Korea Institute of Construction Technology. An optimal neural network model is suggested through preliminary parametric studies and introduces a concept of RSE (Yang and Zhang, 1997) in sensitivity analysis for various major factors affecting the surface settlement in tunnelling. It is seen in some examples that the RSE rationally enables to recognize the most significant factors of all the contributing factors. Two verification examples are undertaken with the trained ANN using the database created in this study. It is shown from the examples that the ANN has adequately recognized the characteristics of the monitored data sets retaining a generality fur further prediction.