• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.418-424
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• 2018

Detection and classification of underwater targets is an important issue for both military and non-military purposes. Recently, many performance improvements are being reported in the field of pattern recognition with the development of deep learning technology. Among the results, DBN showed good performance when used for pre-training of DNN. In this paper, DBN was used for the classification of underwater targets using active sonar, and the results are compared with that of the conventional BPNN. We synthesized active sonar target signals using 3-dimensional highlight model. Then, features were extracted based on FrFT. In the single aspect based experiment, the classification result using DBN was improved about 3.83% compared with the BPNN. In the case of multi-aspect based experiment, a performance of 95% or more is obtained when the number of observation sequence exceeds three.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.543-548
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• 2019

The tonal signal caused by the machinery component of a vessel such as an engine, gearbox, and support elements, can be modeled as a sparse signal in the frequency domain. Recently, compressive sensing based techniques that recover an original signal using a small number of measurements in a short period of time, have been applied for the tonal frequency detection. These techniques, however, cannot avoid a basis mismatch error caused by the discretization of the frequency domain. In this paper, we propose a method to detect the tonal frequency with a small number of measurements in the continuous domain by using the atomic norm minimization technique. From the simulation results, we demonstrate that the proposed technique outperforms conventional methods in terms of the exact recovery ratio and mean square error.

• Journal of the Korea Society for Simulation
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• v.25 no.3
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• pp.53-63
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• 2016

We consider simulation study combining static and mobile decoys for survivability of a surface warship against torpedo attack. It is assumed that an enemy torpedo is a passive acoustic homing torpedo and detects a target within its maximum target detection range and search beam angle by computing signal excess via passive sonar equation, and a warship conducts an evasive maneuvering with deploying static and mobile decoys simultaneously to counteract a torpedo attack. Suggesting the four different decoy deployment plans to achieve the best plan, we analyze an effectiveness for a warship's survival probability through Monte Carlo simulation, given a certain experimental environment. Furthermore, changing the speed and the source level of decoys, the maximum torpedo detection range of warship, and the maximum target detection range of torpedo, we observe the corresponding survival probabilities, which can provide the operational capabilities of an underwater defense system.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.192-198
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• 2022

As an effective weapon system for anti-submarine warfare, DIrectional Frequency Analysis and Recording (DIFAR) sonobuoy detects underwater targets via beamforming with three channels composed of an omni-direcitonal and two directional channels. However, ambient noise degrades the detection performance of DIFAR sonobouy in specific direction (0°, 90°, 180°, 270°). Thus, an ambient noise redcution technique is proposed for performance improvement of acoustic target detection of DIFAR sonobuoy. The proposed method is based on OTA (Order Truncate Average), which is widely used in sonar signal processing area, for ambient noise estimation and Wiener filtering, which is widely used in speech signal processing area, for noise reduction. For evaluation, we compare mean square errors of target bearing estmation results of conventional and proposed methods and we confirmed that the proposed method is effective under 0 dB signal-to-noise ratio.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.16-24
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• 2011

The sound speed of seawater can be calculated by the empirical formula as a function of temperature, salinity and pressure. It is little affected by salinity because the average salinity is 34 psu and varies within a few psu seasonally and spatially in the ocean. Recently, low-salinity water of 24 psu flows into the western sea area of Jeju Island due to the flood of the Yangtze River in China during summer, affecting sound speed profile. In this paper, it was analyzed how environmental changes affected to the underwater communication - the sound speed of low-salinity water was calculated, and the communication channel was estimated by the simulated acoustic rays while the transmitting and receiving depth and the range were varied with and without the low-salinity layer. And The BER (Bit error rate) was calculated by BPSK(Binary phase shift key) modulation and the effects of the low-salinity water on the BER was investigated. The sound speed profile was changed to have positive slope by the low-salinity layer at the sub-surface up to 20 m of depth, forming acoustic wave propagation channel at the sub-surface resulting in the decrease of most of the BER Consequently, this paper suggests that it is important to consider changes of the ocean environment for correctly analyzing the underwater communication and the detection capability.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.237-250
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• 2022

In this study, we propose a method to estimate the biomass of invertebrate grazers from the videos with underwater drones by using a multi-object tracking model based on deep learning. In order to detect invertebrate grazers by classes, we used YOLOv5 (You Only Look Once version 5). For biomass estimation we used DeepSORT (Deep Simple Online and real-time tracking). The performance of each model was evaluated on a workstation with a GPU accelerator. YOLOv5 averaged 0.9 or more mean Average Precision (mAP), and we confirmed it shows about 59 fps at 4 k resolution when using YOLOv5s model and DeepSORT algorithm. Applying the proposed method in the field, there was a tendency to be overestimated by about 28%, but it was confirmed that the level of error was low compared to the biomass estimation using object detection model only. A follow-up study is needed to improve the accuracy for the cases where frame images go out of focus continuously or underwater drones turn rapidly. However,should these issues be improved, it can be utilized in the production of decision support data in the field of invertebrate grazers control and monitoring in the future.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.25-31
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• 2023

In order to estimate the radial speed of an underwater object so-called target with active sonar, Continuous Wave (CW) pulse is generally used, but if a target is slow and at near distance, it is not easy to estimate the radial velocity of the target due to acoustic reverberation in the ocean. In 2017, Wang et al. utilized broadband signal of two Hyperbolic Frequency Modulation (HFM) pulses, which is known as a doppler-invariant pulse, with equal frequency band and in opposite sweep directions to overcome this problem and successfully estimate the radial speed of slow-moving nearby target. They demonstrated the estimation of the radial velocity with computer simulation using the parameters of two HFM starting time differences and receiving times. However, for it uses two HFM pulses with equal frequency, cross-correlation between the two pulses negatively affect the detection performance. To mitigate this cross-correlation effect, we suggest using two different band HFM with the opposite sweep directions. In this paper, a method of radial velocity estimation is derived and simulated using two HFM pulses with the pulse length of 1 second and bandwidth of 400 Hz. Applying the suggested method, the radial velocity was estimated with approximately 6 % of relative error in the simulation.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.1-14
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• 2024

An image-reconstruction technology, involving the deployment of an unmanned mobility equipped with high-speed LiDAR (Light Detection And Ranging) has been proposed to reconstruct the shape of abandoned mine. Unmanned mobility operation is remarkably useful in abandoned mines fraught with operational difficulties including, but not limited to, obstacles, sludge, underwater and narrow tunnel with the diameter of 1.5 m or more. For cases of real abandoned mines, quadruped robots, quadcopter drones and underwater drones are respectively deployed on land, air, and water-filled sites. In addition to the advantage of scanning the abandoned mines with 2D solid-state lidar sensors, rotation of radiation at an inclination angle offers an increased efficiency for simultaneous reconstruction of mineshaft shapes and detecting obstacles. Sensor and robot posture were used for computing rotation matrices that helped compute geographical coordinates of the solid-state lidar data. Next, the quadruped robot scanned the actual site to reconstruct tunnel shape. Lastly, the optimal elements necessary to increase utility in actual fields were found and proposed.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.