• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1431-1438
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• 2018

In this paper, we proposed a river water level detection and tracking of the river or dams based on image processing system. In past, most of the water level detection system used various water sensors. Those water sensors works perfectly but have many drawbacks such as high cost and harsh weather. Water level monitoring system helps in forecasting early river disasters and maintenance of the water body area. However, the early river disaster warning system introduces many conflicting requirements. Surveillance camera based water level detection system depends on either the area of interest from the water body or on optical flow algorithm. This proposed system is focused on water scaling area of a river or dam to detect water level. After the detection of scale area from water body, the proposed algorithm will immediately focus on the digits available on that area. Using the numbers on the scale, water level of the river is predicted. This proposed system is successfully tested on different water bodies to detect the water level area and predicted the water level.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.679-690
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• 2023

Leakage is one of the representative abnormal conditions in Water distribution systems (WDSs). Leakage can potentially occur and cause immediate economic and hydraulic damage upon occurrence. Therefore, leakage detection is essential, but WDSs are located underground, it is difficult. Moreover, when multiple leakage occurs, it is required to prioritize restoration according to the scale and location of the leakage, applying for an optimal restoration framework can be advantageous in terms of system resilience. In this study, various leakage scenarios were generated based on the WDSs hydraulic model, and leakage detection was carried out containing location and scale using a Deep learning-based model. Finally, the leakage location and scale obtained from the detection results were used as a factor for the priority of leakage restoration, and the results of the priority of leakage restoration were derived. The priority of leakage restoration considered not only hydraulic factors but also socio-economic factors (e.g., leakage scale, important facilities).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.14 no.6
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• pp.616-624
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• 2003

The exhaustion of our water resource due to the leakage of waterworks renders it urgent to detect water leakage effectively. In the paper, the detection of water leakage makes use of a pound-penetrating radar(GPR). The region of water leakage is implemented by an acryl box filled with methanol, and then the scale-down experiments are performed by using the GPR system developed in our laboratory. The validity of GPR experiments is assured by showing that the measured data agree well with those finite-difference time-domain(FDTD) simulated results in the same situation. The feasibility of GPR system for the detection of water leakage is investigated by displaying B-scan images according to the distribution of water leakage.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3256-3263
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• 2021

The main steam line break accident is an essential initiating event of the pressurized water reactor. In present work, the fuzzy set theory and the signal-based fault detection method has been used to detect the occurrence and diagnosis of the location and break area for the small scale MSLB. The models are validated by the AP1000 accident simulator based on MAAP5. From the test results it can be seen that the proposed approach has a rapid and proper response on accident detection and location diagnosis. The method proposed to evaluate the break area shows good performances for small scale MSLB with the relative deviation within ±3%.

• Journal of Food Hygiene and Safety
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• v.8 no.2
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• pp.13-21
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• 1993

Vibrio septicemia,, resulting in high mortality, has been caused by Vibrio vulnificus. Ingestion of marine products or contact with sea water contaminated with Vibrio vulnificus can cause septicemia. Vibrio vulnificus has been detected world wide and west sea area of Korea, Kum river estuary in particular, showed high detection rate. Higher detection rate of Vibrio vulnificus were reported in the bottom deposit with low depth of water, low salinity, and high COD. Man with the liver disease can easily come down with Vibrio septicemia and the main source is the sliced raw fish dish. The preventive measure for this disease is to wash raw fish material thoroughly with tap water and handle in sanitary conditions. Washing with sea water is strictly prohibited . It may be necessary to forbid the small-scale businessmen from selling the sliced ray fish dish in the vicinity of seashore. Man with the liver disease of diabetes should not swim or consume the raw fish dish in the contaminated area during summer.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.1
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• pp.1-6
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• 2003

Since the scale in pipes reduces the flow rate, a quantitative evaluation of the scale is essential for the proper maintenance of pipes. Guided waves were employed to estimate the amount of scale in water supplying pipes. Using variable angle wedge, several modes of guided waves wire generated and their propagation charcteristics along the pipes with stale were analyzed. It was experimentally observed that the amplitude of F(M,2) modes at $f{\times}d=1.5MHz\;mm$ decreased significantly with increasing amount of scale. The present study showed that F(M,2) modes were optima) to evaluate the scale in water supplying pipes.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1777-1788
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• 2022

The reservoir is a major water supply source in the domestic agricultural environment, and the monitoring of water storage of reservoirs is important for the utilization and management of agricultural water resource. Remote sensing via satellite imagery can be an effective method for regular monitoring of widely distributed objects such as reservoirs, and in this study, image classification and image segmentation algorithms are applied to Sentinel-1 Synthetic Aperture Radar (SAR) imagery for water body detection in 53 reservoirs in South Korea. Six algorithms are used: Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), Otsu, Watershed (WS), and Chan-Vese (CV), and the results of water body detection are evaluated with in-situ images taken by drones. The correlations between the in-situ water surface area and detected water surface area from each algorithm are NN 0.9941, SVM 0.9942, RF 0.9940, Otsu 0.9922, WS 0.9709, and CV 0.9736, and the larger the scale of reservoir, the higher the linear correlation was. WS showed low recall due to the undetected water bodies, and NN, SVM, and RF showed low precision due to over-detection. For water body detection through SAR imagery, we found that aquatic plants and artificial structures can be the error factors causing undetection of water body.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.545-559
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• 2010

This paper presents the results of a pilot study and verification of a concept of a novel methodology for damage detection and assessment of water distribution system. The unique feature of the proposed noninvasive methodology is the use of accelerometers installed on the pipe surface, instead of pressure sensors that are traditionally installed invasively. Experimental observations show that a sharp change in pressure is always accompanied by a sharp change of pipe surface acceleration at the corresponding locations along the pipe length. Therefore, water pressure-monitoring can be transformed into acceleration-monitoring of the pipe surface. The latter is a significantly more economical alternative due to the use of less expensive sensors such as MEMS (Micro-Electro-Mechanical Systems) or other acceleration sensors. In this scenario, monitoring is made for Maximum Pipe Acceleration Gradient (MPAG) rather than Maximum Water Head Gradient (MWHG). This paper presents the results of a small-scale laboratory experiment that serves as the proof of concept of the proposed technology. The ultimate goal of this study is to improve upon the existing SCADA (Supervisory Control And Data Acquisition) by integrating the proposed non-invasive monitoring techniques to ultimately develop the next generation SCADA system for water distribution systems.

• 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.