• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.399-404
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• 2011

For the underwater wireless sensor networks, we propose the packet transmission method which distinguishes more important packet than others. Because the ocean underwater transmission environments are extremely unstable, we use SON(Self Organizing Network) techniques to adapt to the constantly varying underwater acoustic communication channels and randomly deployed sensor nodes. Especially we suppose two kinds of packets which have different priorities, and through the simulations we show that high priority packets arrive at the source node faster than lower priority packets with a proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.239-260
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• 2023

The underwater acoustic wireless communication networks are generally formed by the different autonomous underwater acoustic vehicles, and transceivers interconnected to the bottom of the ocean with battery deployed modems. Orthogonal frequency division multiplexing (OFDM) has become the most popular modulation technique in underwater acoustic communication due to its high data transmission and robustness over other symmetrical modulation techniques. To maintain the operability of underwater acoustic communication networks, the power consumption of battery-operated transceivers becomes a vital necessity to be minimized. The OFDM technology has a major lack of peak to average power ratio (PAPR) which results in the consumption of more power, creating non-linear distortion and increasing the bit error rate (BER). To overcome this situation, we have contributed our symmetry research into three dimensions. Firstly, we propose a machine learning-based underwater acoustic communication system through long short-term memory neural network (LSTM-NN). Secondly, the proposed LSTM-NN reduces the PAPR and makes the system reliable and efficient, which turns into a better performance of BER. Finally, the simulation and water tank experimental data results are executed which proves that the LSTM-NN is the best solution for mitigating the PAPR with non-linear distortion and complexity in the overall communication system.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2643-2645
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• 2004

The underwater system autonomously is navigated by using the wireless communication system, which receives the control signal from surface ship. The study proposes the new media access control protocol for underwater vehicles network in the view of communication distance and as CSMA(Carrier Sense Multiple Access) for the existing networks is intended to communication network using the high speed media such as electric signal or microwave signal, and thus it may introduce the reduction in throughput when applying the protocols to underwater communication network.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.883-884
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• 2008

This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.367-375
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• 2001

In Underwater Flight Vehicle depth control system, the followings must be required. First, it needs robust performance which can get over modeling error, parameter variation and disturbance. Second, it needs accurate performance which have small overshoot phenomenon and steady state error to avoid colliding with ground surface or obstacles. Third, it needs continuous control input to reduce the acoustic noise and propulsion energy consumption. Finally, it needs interpolation method which can sole the speed dependency problem of controller parameters. To solve these problems, we propose a depth control method using Fuzzy Sliding Mode Controller with feedforward control-plane bias term and Neural Network Interpolator. Simulation results show the proposed method has robust and accurate control performance by the continuous control input and has no speed dependency problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.97-100
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• 2011

Underwater Wireless Acoustic Sensor Networks have become an important area of research over the recent decades. Designing an underwater network, especially a media access control (MAC) protocol, faces many challenges due to the peculiarities of underwater environment. One of the most important problems is resulted from long and variable propagation delay of the acoustic wave. In this paper, we propose a new method, namely Dynamic Timeout over Multiple Access with Collision Avoidance (DT/MACA), which is designed to handle long and high variable propagation delay in underwater acoustic sensor networks. In this proposed method, the difference timeout intervals are evaluated and applied to each network transmission. Simulation results show that our work not only improves the network throughput, but also decreases the unnecessary retransmission and end-to-end delay.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.135-141
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• 2016

Recently, the needs for the development and application of marine robots are increasing as marine accidents occur frequently. However, it is very difficult to acquire the information by utilizing marine robots in the marine environment. Therefore, the needs for the researches of sensor networks which are composed of underwater, surface and aerial robots are increasing in order to acquire the information effectively as the information from heterogeneous robots has less limitation in terms of coverage and connectivity. Although various researches of the sensor network which is based on marine robots have been executed, all of the underwater, surface and aerial robots have not yet been considered in the sensor network. To solve this problem, a collaborative control method based on the acoustic information and image by the sonars of the underwater robot, the acoustic information by the sonar of the surface robot and the optical image by the camera of the static-floating aerial robot is proposed. To verify the performance of the proposed method, the collaborative control of a MUR(Micro Underwater Robot) with an OAS(Obstacle Avoidance Sonar) and a SSS(Side Scan Sonar), a MSR(Micro Surface Robot) with an OAS and a BMAR(Balloon-based Micro Aerial Robot) with a camera are executed. The test results show the possibility of real applications and the need for additional studies.

• Journal of Ocean Engineering and Technology
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• v.18 no.2
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• pp.33-38
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• 2004

All adaptive neural network controller has been developed for a model of an underwater vehicle. This controller combines a radial basis neural network and sliding mode control techniques. No prior off-line training phase is required, and this scheme exploits the advantages of both neural network control and sliding mode control. An on-line stable adaptive law is derived using Lyapunov theory. The number of neurons and the width of Gaussian function should be chosen carefully. Performance of the controller is demonstrated through computer simulation.

• The Journal of the Acoustical Society of Korea
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• v.32 no.1
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• pp.86-94
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• 2013

In this paper, we propose a network coding scheme that is one of the most promising techniques for overcoming transmission errors in underwater acoustic communications. It is assumed that the proposed scheme operates in a Code Division Multiple Access (CDMA) network where multiple sensor nodes share the underwater acoustic channel in both the frequency and the time domains by means of orthogonal codes. The network topology deploys multi-hop transmission with relaying between multiple source nodes and one destination node via multiple relay nodes. The proposed scheme is evaluated in terms of the successful packet delivery ratio of end-to-end transactions under varying packet loss rates. A computer simulation shows that the successful delivery ratio is maintained at over 95% even when the packet loss rate reaches 50%.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.