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Underwater Robot Navigation through Compensation of Sensor Misalignment in Non-linear Space |
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Da Bin Jeong(Chosun University, Korea), Woong Choi(Kangnam University, Korea), Nak Yong Ko(Chosun University, Korea) |
| This paper presents a novel approach to estimate and compensate for the misalignment between the Inertial Measurement Unit (IMU) and Doppler Velocity Log (DVL) sensors. Estimating the location and attitude of an ROV usually combines the attitude by IMU and the velocity by the DVL provided in the sensor coordinate system. The attitude is regarded as the attitude of the ROV and the velocity in the sensor coordinate frame is converted to the velocity in the inertial coordinate frame. Misalignment in the DVL and IMU causes errors in the converted velocity and results in the degradation of the navigation performance. The proposed method represents the misalignment using unit quaternion.
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Developing an experimental system for investigating texture image recognition mechanisms in virtual reality |
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HyoKyeom Kim, HyunSoo Shin, SeungMin Jeon(Kangnam University, Korea), Liang Li(Ritsumeikan University, Japan), Akira Asano(Kansai University, Japan), Chie Muraki Asano(Sapporo Campus, Hokkaido University of Education, Japan), Xiaoying Guo(Shanxi University, China), NakYong Ko(Chosun University, Korea), Woong Choi(Kangnam University, Korea) |
| The study of human perception of visual complexity is crucial for both virtual world development and understanding the parallels with real-world environments. The system is proposed to quantitatively assess human visual complexity perception. It displays the textures in the virtual space while monitoring participants' gaze, enabling the observation of their gaze patterns through heatmaps and numbers. The future improvement of the system involves incorporating 3D objects to explore their impact on human visual complexity. |
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Localization Algorithms for Drones in a Wireless Communication Environment with Presence of Data Missing. |
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Sin Kim, Sung Shin, Nak Yong Ko, Sung Hyun You(Chosun University, Korea) |
| This paper introduces a localization algorithm for accurate drone hovering position estimation in the case of data missing. In wireless communication environments, missing of data transmission and reception frequently occurs. The proposed algorithm estimates the position of the drone by using the residual values of predicted measurements in the case of data missing. By using distance information between the anchors serve as transmitter and the drone-attached tag serves as receiver, the algorithm aims to estimate the position of the drone in the case of data missing. |
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Integral Sliding Mode Controller Design for Trajectory Following of a Six Degrees Freedom Underwater Vehicle |
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Eyasu Derbew Tegen, Nak Yong Ko(Chosun University, Korea) |
| This paper presents a robust integral sliding mode controller for trajectory following of six degrees of freedom underwater vehicle. Sliding mode controller is robust to bounded external disturbances caused by ocean currents, parametric uncertainties, and inevitable modeling errors; but, it is prone to chattering phenomenon. Thus, to address this problem, we proposed an integral sliding mode controller. The controller was designed, and its stability was checked using a Lyapunov approach. The proposed controller was implemented using MATLAB/Simulink software. A simulation results were presented, and also, its performance was compared with the conventional sliding mode controller. |
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Deep-sea Organisms Classification Using Vision Transformer |
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Jiyoun Moon(Chosun University, Korea) |
| The exploration of the deep sea and the performance of diverse tasks in the deep sea are currently receiving considerable attention. The main objective of deep-sea exploration is to understand the seabed topography and utilize deep-sea biological resources. To utilize deep-sea biological resources, it must be possible to classify deep-sea organisms. This study proposes a method for classifying deep-sea organisms using a vision transformer model that treats images as sequential data. The proposed method is validated with a self-composed deep-sea organism dataset. |
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