• Journal of Korea Water Resources Association
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• v.51 no.8
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• pp.703-711
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• 2018

Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.246-252
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• 2016

Since thermal power plant (TPP) equipment is operated under very high pressure and temperature, failures of the equipment give rise to severe losses of life and property. To prevent the losses, fault detection method is, therefore, absolutely necessary to identify abnormal operating conditions of the equipment in advance. In this paper, we present Mahalanobis distance (MD) based fault detection method for steam boiler tube in TPP. In the MD-based method, it is supposed that abnormal data samples are far away from normal samples. Using multivariate samples collected from normal target system, mean vector and covariance matrix are calculated and threshold value of MD is decided. In a test phase, after calculating the MDs between the mean vector and test samples, alarm signals occur if the MDs exceed the predefined threshold. To demonstrate the performance, a failure case due to boiler tube leakage in 200MW TPP is employed. The experimental results show that the presented method can perform early detection of boiler tube leakage successfully.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.81-88
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• 2010

This paper describes a development of next generation information remote retrieval and warning system that enables the user to make slope information retrieval remotely for a rockfall and landslide disaster reduction from mobile environments. And this system will be able to warn with a real-time stability condition about the slope which circumference are contiguous in standard user location. Slope information which provides to the user, become the service which upgrades from depth deep information directness will be able to confirm in order from field with applies multimedia style information which is various. In order to retrieve slope information with the wire and wireless internet from the remote place, we used mobile PC carrying is simple. Also this system attached GPS receiver to mobile PC in order to confirm user location as a real-time from the electronic map from field. Specially this system user location divide the safety of the slope which within the area where are fixed in the center are representative with 'safe area', 'collapse area' and 'collapse forecast area' etc. And to indicate with the icon of each other different color simultaneously in the electronic map. With like that reason, this system which sees the user even while moving safety condition about circumferential slope from the electronic map is having the strong point will be able to grasp with a real-time in one eye. Also warning message leads at the case real-time when the collapse will occur in specific slope, to inform to the user. Therefore this system which sees will be able to reduce the disaster which is caused by in landslide a very big strong point and has.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.74-84
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• 2017

Since a soldier manages a hand-held landmine detector by hands, it is necessary to develop a system that can detect the target quickly and accurately. However, the hand-held landmine detector used in Korea has a problem that it can only detect the metal mines. Therefore, it is important to solve the problem and to develop a hand-held landmine detection system suitable for the Korean environment. In this paper, we propose a hand-held landmine detection system suitable for the Korean environment using ground penetrating radar. The proposed system uses depth compensation, matched filtering, and frequency shifting filtering for preprocessing. Then, in the detection step, the system detects the target using the edge ratio. In order to evaluate the proposed system, we buried landmines in sandy loam which is most of the soil in Korea and obtained a set of ground penetrating radar data by using a hand-held landmine detector. By using the obtained data, we carried out some experiments on the size and position of the patch and the shifting frequency to find the optimal parameter values and measured the detection performance using the optimized values. Experimental results show that the proposed preprocessing algorithms are suitable for detecting all landmines at low false alarm rate and the performance of the proposed system is superior to that of previous works.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.29-38
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• 2012

The data quality and measurements along consecutive detector stations can vary much even in the same traffic conditions due to variety in detector types, calibration and maintenance effort, field operation periods, minor geometric changes of roads and so on. These faulty situations often create 10% or more of inherent difference in important traffic measurements between two stations even under stable low flow condition. Low detection rates(DR) and high false alarm rates(FAR) therefore sets in among many popular Automatic Incident Detection Algorithms(AIDA). This research is two-folded and aims mainly to develop a new AIDA for uninterrupted flow. For this purpose, a technique which utilizes a Simple Arithmetic Operation(SAO) of traffic variables is introduced. This SAO technique is designed to address the inherent discrepancy of detector data observed successive stations, and to overcome the degradation of AIDA performance. It was found that this new algorithm improves DR as much as 95 percent and above. And mean time to detection(MTTD) is found to be 1 minutes or less. When it comes to FAR, this new approach compared to existing AIDAs reduces FAR up to 31.0 percent. And capability in persistency check of on-going incidents was found excellent as well.

• Spatial Information Research
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• v.16 no.3
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• pp.279-290
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• 2008

Landslide, one of the serious natural disasters, has Incurred a large loss of human and material resources. Recently, many forecasting or alarm systems based on various kinds of measuring equipment have been developed to reduce the damage of landslide. However, only a few of these equipments are guaranteed to evaluate the safety of whole side of land slope with their accessibility to the slope. In this study, we performed some experiments to evaluate the applicability of a terrestrial LiDAR as a surveying tool to measure the displacement of a land slope surface far a slope collapsing protection system. In the experiments, we had applied a slope stability method to a land slope and then forced to this slope with a load increasing step by step. In each step, we measured the slope surface with both a total station and a terrestrial LiDAR simultaneously. As the result of Slope Fracturing analysis using all targets, the LiDAR system showed that three was 1cm RMSE on X-axis, irregularity errors on Y-axis and few errors on Z-axis compare with Total Station. As the result of Slope Fracturing analysis using continuous targets, the pattern of Slope Fracturing was different according to the location of continuous targets and we could detect a continuous change which couldn't be found using Total station. The accuracy of the LiDAR data was evaluated to be comparable to that of the total station data. We found that a LiDAR system was appropriate to measuring the behaviour of land slope. The LiDAR data can cover the whole surface of the land slope, whereas the total station data are available on a small number of targets. Moreover, we extracted more detail information about the behavior of land slope such as the volume and profile changes using the LiDAR data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.107-115
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• 2011

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.1-9
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• 2014

In this paper, we design and implement location information system and user mapping system using DSDV(Destination Sequenced Distance Vector) routing algorithm in ad-hoc network environment to efficient manage a number of mobile devices. The software part in proposed system construct ad-hoc network using DSDV routing algorithm and it activate alarm system, such as vibration, when one of devices disappears in the network. The hardware system, called u_LIN (User Location Information Node) construct ad-hoc network and it helps to find a disappeared device by using warning system. When we evaluate the performance of our prototype system, we have checked a correct operation, within the range of 250m in case of 1:1 communication and within the range of 100m in case of 1:N communication. The implemented system in this paper is highly expected to flexibly use in juvenile protection system, stray-child protection system, tourist guide system and so on.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.122-130
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• 2008

In many applications of wireless sensor networks, sensed data can be classified either normal or urgent data according to its time criticalness. Normal data such as periodic monitoring is loss and delay tolerant, but urgent data such as fire alarm is time critical and should be transferred to a sink with reliable. In this paper, by exploiting these data characteristics, we propose a novel energy-efficient data-aware routing protocol for wireless sensor networks, which provides a high reliability for urgent data and energy efficiency for normal data. In the proposed scheme, in order to enhance network survivability and reliability for urgent data, each sensor node forwards only urgent data when its residual battery level is below than a threshold. Also, the proposed scheme uses different data delivery mechanisms depending on the data type. The normal data is delivered to the sink using a single-path-based data forwarding mechanism to improve the energy-efficiency. Meanwhile, the urgent data is transmitted to the sink using a directional flooding mechanism to guarantee high reliability. Simulation results demonstrate that the proposed scheme could significantly improve the network lifetime, along with high reliability for urgent data delivery.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.