• Journal of the Society of Naval Architects of Korea
• /
• v.47 no.2
• /
• pp.196-209
• /
• 2010

For the analysis of Acoustic Target Strength(TS) that indicates the scattered acoustic intensity from the underwater vehicles, an analysis program that is applicable to scatterers insonified by spherical wave source in near field is developed. In this program, the Physical Optics(PO) method is embedded as a base component. To increase the accuracy of the program, multiple bounce effects based on Geometrical Optics(GO) method are applied. To implement multiple bounce effects, GO method is used together with PO method. In detail, GO method has a concern in the evaluation of the effective area, and PO method is involved in the calculation of Acoustic Target Strength for the final effective area that is evaluated by GO method. For the embodiment of near field spherical wave source originated multiple bounce effects, image source concept is implemented additively to the existing multiple bounce algorithm which assumes plane wave insonification. Various types of models are tested to evaluate the reliability of the developed program and finally, a submarine is analyzed as an arbitrary scatterer.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.3E
• /
• pp.4-12
• /
• 1996

In this study, we examine the applicability of an artificial neural network(ANN) for filtering underwater random noise and for identifying underlying signals taken from noisy environment. The approach is to find a way of compressing the input data and then decompressing it using an ANN as in image compressing process. It is well known that random signal is hard to compress while ordered information is not. The use of a limited number of processing elements(PEs) in the hidden layer of an Ann ensures that some of the noise would be removed in the reconstruction process. Two types of the signals, synthesized and measured, are used to examine the effectiveness of the ANN-based filter. After training process is completed, the ANN successfully extracts the underlying signals form the synthesized or measured noisy signals. In particular, compared with the results form without filtering or moving averaged, the ANN-based filter gives much better spectrograms to identify underlying signals from the measured noisy data. This filtering process is achieved without using and kind of highly accurate signal processing technique. More experimentation needs to be followed to develop the ANN-based filtering technique to the level of complete understanding.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.10 no.5
• /
• pp.596-608
• /
• 2018

The present study aims to assess test uncertainty assessment method of nominal wake field measurement by a Stereoscopic Particle Image Velocimetry (SPIV) system in a towing tank. The systematic uncertainty of the SPIV system was estimated from repeated uniform flow measurements. In the uniform flow measurement case, time interval between image frames and uniform flow speed were varied to examine the effects of particle displacement and flow around the SPIV system on the systematic standard uncertainty. The random standard uncertainty was assessed by repeating nominal wake field measurements and the estimated random standard uncertainty was compared with that of laser Doppler velocimetry. The test uncertainty assessment method was applied to nominal wake measurement tests of a very large crude oil carrier model ship. The nominal wake measurement results were compared with existing experimental database by other measurement methods, with its assessed uncertainty.

• Journal of Navigation and Port Research
• /
• v.42 no.3
• /
• pp.237-244
• /
• 2018

When underwater rubble leveling work is carried out by a robot, real-time information on the topography around the robot is required for remote control. If the topographical information with respect to the current position of the robot is displayed as a 3D graphic image, it allows the operator to plan the working schedules and to avoid accidents like rollovers. Up until now, the topographical recognition was conducted by multi-beam sonars, which were only used to assess the quality before and after the work and could not be used to provide real-time information for remote control. This research measures the force delivered to the bucket which presses the mound to determine whether contact is made or not, and the contact position is calculated by reading the cylinder length. A variable bang-bang control algorithm is applied to control the heavy robot arms for the positioning of the bucket. The proposed method allows operators to easily recognize the terrain and intuitively plan the working schedules by showing relatively 3-D gratifications with respect to the robot body. In addition, the operating patterns of a skilled operator are programmed for raking, pushing, moving, and measuring so that they are automatically applied to the underwater rubble leveling work of the robot.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.2
• /
• pp.161-167
• /
• 2019

In this paper, the performance verification of an around-view sonar and an excavation depth measuring sonar applicable to track-based ROVs (Remotely Operated underwater Vehicles) for heavy duty work is studied. For the performance verification, an experiment is carried out in a water tank and at sea by attaching the around-view sonar and the excavation depth measuring sonar for a heavy work ROV. In the case of the around-view sonar, image sonars are mounted on ROV in four directions (front, back, left and right) and in the case of the excavation depth measuring sonar, the same kind of MBES (Multi Beam Echo Sounder) is mounted on the front of the ROV. The result of an operation test of the ROV equipped with these sonars shows that the sonar systems are rarely affected by high turbidity due to sedimentation during the operation. In the case of the around-view sonar, it is possible to see rock formation, gravel and sandbank 30 m ahead of the ROV. It is confirmed that the excavation depth can be measured after the ROV has performed the excavation. This experiment demonstrates that the ROV can improve the efficiency of the work by utilizing the around-view sonar and the excavation depth measuring sonar.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1773-1784
• /
• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• The Journal of Korea Robotics Society
• /
• v.12 no.4
• /
• pp.385-394
• /
• 2017

Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).

• Annual Conference of KIPS
• /
• 2015.10a
• /
• pp.1350-1353
• /
• 2015

In this paper, we have researched about how to detect the fish robot objects in aquarium. We had used designed fish robots DOMI ver1.0, which had researched and developed for aquarium underwater robot. The model of the robot fish is analysis to maximize the momentum of the robot fish and the body of the robot is designed through the analysis of the biological fish swimming. We are planned to non-external equipment to find the position and manipulated the position using creating boundary to fish robot to detect the fish robot objects. Also, we focused the detecting fish robot in aquarium by using boundary algorithm. In order to the find the object boundary, it is filtering the video frame to picture frames and changing the RGB to gray. Then, applied the boundary algorithm stand of equations which operates the boundary for objects. We called these procedures is kind of image processing that can distinguish the objects and background in the captured video frames. It was confirmed that excellent performance in the field test such as filtering image, object detecting and boundary algorithm.

• Annual Conference of KIPS
• /
• 2015.10a
• /
• pp.1354-1357
• /
• 2015

In this paper, the designed fish robot is researched and developed for aquarium underwater robot. This paper is a study on how the outside technology merely to find the location of fish robots without specific sensor or internal devices for these fish robot. The model of the fish is designed to detect the position of the optical flow of the Robotic Fish in the Simulink through Matlab. This paper intends to recognize the shape of the tank via a video device such as a camera or camcorder using an image processing technique to identify the location of the robotic fish. Here, we are applied to the image comparing algorithm by using the average color weight algorithm method. In this, position coordinate system is used to find the position coordinates of the fish to identify the position of the Robotic fish. It was verified by the performance test of design robot.

• Journal of the Society of Naval Architects of Korea
• /
• v.58 no.2
• /
• pp.105-111
• /
• 2021

The main source of underwater radiated noise of ships is cavitation generated by propeller blades. After the Cavitation Inception Speed (CIS), noise level at all frequencies increases severely. In determining the CIS, it is based on the results observed with the naked eye during the model test, however accuracy and consistency of CIS values are becoming practical issues. This study was carried out with the aim of developing a technology that can automatically recognize cavitation images using deep learning technique based on a Convolutional Neural Network (CNN). Model tests on a three-dimensional hydrofoil were conducted at a cavitation tunnel, and tip vortex cavitation was strictly observed using a high-speed camera to obtain analysis data. The results show that this technique can be used to quantitatively evaluate not only the CIS, but also the amount and rate of cavitation from recorded images.