• International Journal of Contents
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• v.2 no.3
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• pp.27-32
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• 2006

We present an effective algorithm for automatic tracing of retinal vessel structure and vascular landmark extraction of bifurcations and ending points. In this paper we deal with vascular patterns from RGB images for personal identification. Vessel tracing algorithms are of interest in a variety of biometric and medical application such as personal identification, biometrics, and ophthalmic disorders like vessel change detection. However eye surface vasculature tracing in RGB images has many problems which are subject to improper illumination, glare, fade-out, shadow and artifacts arising from reflection, refraction, and dispersion. The proposed algorithm on vascular tracing employs multi-stage processing of ten-layers as followings: Image Acquisition, Image Enhancement by gray scale retinal image enhancement, reducing background artifact and illuminations and removing interlacing minute characteristics of vessels, Vascular Structure Extraction by connecting broken vessels, extracting vascular structure using eight directional information, and extracting retinal vascular structure, and Vascular Landmark Extraction by extracting bifurcations and ending points. The results of automatic retinal vessel extraction using jive different thresholds applied 34 eye images are presented. The results of vasculature tracing algorithm shows that the suggested algorithm can obtain not only robust and accurate vessel tracing but also vascular landmarks according to thresholds.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.3-5
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• 2005

Vessel boundary detection and modeling is a difficult but a necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. In this paper we present a method of analyzing the structure by means of an active contour model(using GVF Snake) for vessel boundary detection and 3D reconstruction. For this purpose we used a virtual vessel model and produced a phantom model. From these phantom images we obtained the contours of the vessel by GVF Snake and then reconstructed a 3D structure by using the coordinates of snakes.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.253-256
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• 2005

The use of satellite remote sensing in maritime safety and security can aid in the detection of illegal fishing activities and provide more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which use the ground-based radar system have some difficulties in detecting moving ships due to the limited detection range. A virtual vessel traffic control system is introduced to contribute to prevent a marine accident such as collision and stranding from happening. Existing VTS has its limit. The virtual vessel traffic control system consists of both data acquisition by satellite remote sensing and a simulation of traffic environment stress based on the satellite data, remotely sensed data. And it could be used to provide timely and detailed information about the marine safety, including the location, speed and direction of ships, and help us operate vessels safely and efficiently. If environmental stress values are simulated for the ship information derived from satellite data, proper actions can be taken to prevent accidents. Since optical sensor has a high spatial resolution, JERS satellite data are used to track ships and extract their information. We present an algorithm of automatic identification of ship size and velocity. This paper lastly introduce the field testing results of ship detection by RADARSAT SAR imagery, and propose a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.95-99
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• 2004

In this study, the leak detection technique of pressure vessel by using acoustic emission(AE) signal is suggested experimentally. The leak of pressure vessel is located at the welding line due to welding defects. we measured the AE signal using Rl5I sensor, and examined the AE parameters in leak condition. It is investigated that the mean value of AE signal is dependent on leak source location. So the absolute mean value of AE signal is adopted as dominant AE parameter. We proposed leak detection algorithm using AE signal mean value for monitoring the leak source location.

• Proceedings of KOSOMES biannual meeting
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• 2006.05a
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• pp.41-45
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• 2006

From the 1978 Seasat synthetic aperture radar(SAR) to present systems, spaceborne SAR has demonstrated the capability to image the Earth's ocean and land features over broad areas, day and night, and under most weather conditions. The application of SAR for surveillance of commercial fishing grounds can did in the detection of illegal fishing activities and provides more efficient use cf limited aircraft or patron craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which uses the ground-based radar system has some difficulties in detecting moving ships due to the limited detection range cf about 10 miles. This paper introduces the field testing results of ship detection by RADARSAT SAR imagery, and proposes a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• Proceedings of KOSOMES biannual meeting
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• 2007.05a
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• pp.127-132
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• 2007

From the 1978 Seasat synthetic aperture radar(SAR) to present systems, spaceborne SAR has demonstrated the capability to image the Earth's ocean and land features over broad areas, day and night, and under most weather conditions. The application of SAR for surveillance of commercial fishing grounds can aid in the detection of illegal fishing activities and provides more efficient use of limited aircraft or patrol craft resources. In the area of vessel traffic monitoring for commercial vessels, Vessel Traffic Service (VTS) which uses the ground-based radar system has some difficulties in detecting moving ships due to the limited detection range of about 10 miles. This paper introduces the field testing results of ship detection by RADARSAT SAR imagery, and proposes a new approach for a Vessel Monitoring System(VMS), including VTS, and SAR combination service.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.532-548
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• 2021

Nuclear power plants contain several monitoring systems that can identify the in-vessel phenomena of a severe accident (SA). Though a lot of analysis and research is carried out on SA, right from the development of the nuclear industry, not all the possible circumstances are taken into consideration. Therefore, to improve the efficacy of the safety of nuclear power plants, additional analytical studies are needed that can directly monitor severe accident phenomena. This paper presents an interacting multiple model (IMM) based fault detection and diagnosis (FDD) approach for the identification of in-vessel phenomena to provide the accident propagation information using reactor vessel (RV) out-wall temperature distribution during severe accidents in a nuclear power plant. The estimation of wall temperature is treated as a state estimation problem where the time-varying wall temperature is estimated using IMM employing three multiple models for temperature evolution. From the estimated RV out-wall temperature and rate of temperature, the in-vessel phenomena are identified such as core meltdown, corium relocation, reactor vessel damage, reflooding, etc. We tested the proposed method with five different types of SA scenarios and the results show that the proposed method has estimated the outer wall temperature with good accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.737-746
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• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.282-292
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• 2020

In order to minimize casualties from marine vessel accidents that occur frequently at home and abroad, it is important to ensure the safety of the passengers aboard the vessel in the event of an accident. There is an EPIRB system as a system for disaster preparedness in the marine situation currently on the market, but there is a problem that the price is very expensive. In order to overcome the cost problem, which is a disadvantage of previous system, LoRaWAN-based communication is used. LoRaWAN communication-based vessel positioning and risk detection system based on LoRaWAN communication transmits measurement data of each module using two Beacon and GPS modules to stably perform position measurement for both indoor and outdoor situations. The rider danger situation detection system can detect the safety status of the rider using the 3-axis acceleration sensor, collect data from the rider positioning system and the rider safety status detection system, and send to server using LoRa communication. When conducting communication experiments in the long-distance maritime situation and actual communication experiments using the implemented system, it was found that the two experiments showed over 90% communication success rate on average.