• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.04a
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• pp.83-88
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• 2013

The purpose of this study is to develop the algorithm for dynamic interaction analysis of submerged floating tunnel and vehicles. The dynamic behavior characteristic of submerged floating tunnel is certainly different with general structures, because the submerged floating tunnel is floating in the middle of water, and subjected to constant buoyance. Therefore the analyses in various aspects should be carried out to secure structural stability and practicality of structures. To conduct the dynamic interaction analysis, the structure is modeled by commercial FEM program ABAQUS to investigate modal characteristic. Also the added mass concept is applied to represent the inertial force by a fluid, and then dynamic interaction analyses are conducted with superposition method when the KTX is moving along the submerged floating tunnel. And the time histories are presented for vertical and lateral displacement at the center of the tunnel.

• The Journal of Korea Robotics Society
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• v.8 no.1
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• pp.37-42
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• 2013

In this paper, a localization algorithm and an autonomous controller for PETASUS system II which is an underwater vehicle-manipulator system, are proposed. To estimate its position and to identify manipulation targets in a structured environment, a multi-rate extended Kalman filter is developed, where map information and data from inertial sensors, sonar sensors, and vision sensors are used. In addition, a three layered control structure is proposed as a controller for autonomy. By this controller, PETASUS system II is able to generate waypoints and make decisions on its own behaviors. Experiment results are provided for verifying proposed algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5219-5226
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• 2014

This paper proposes an improved scheme of pedestrian position information system using neural network theory in a GPS-disabled area. Through a learning/obtaining gait pattern and step distance about walk, run, duck walk, crab walk and crawl, the position estimation error could be minimized by rejecting the inertial navigation drift. A portable hardware module was implemented to evaluate the performance of the proposed system. The performance and effectiveness of the suggested algorithm was verified by experiments indoors.

• Journal of Navigation and Port Research
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• v.33 no.9
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• pp.603-608
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• 2009

In this paper, a 3m-class free running model ship will be introduced with its manoeuvring performance experiments. The results of turning circle test and zig-zag test will be explained. The developed system are equipped with GPS, main control computer, wireless LAN, IMU (Inertial Measurement Unit), self-propulsion propeller and driving rudder. Its motion can be controlled by RC (Radio Control) and wireless LAN from land based center. Automatic navigation is also available by pre-programmed algorithm. The trajectory of navigation can be acquired by GPS and it provides us with important data for ship's motion control experiments. The results of manoeuvring performance experiment have shown that the developed free running model ship can be used to verify the test of turning circle and zig-zag. For next step, other experimental researches such as ship collision avoidance system and automatic berthing can be considered in the future.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.974-981
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• 2012

This paper studied about the measurement method of the instantaneous dynamic load characteristics. this experimental study, we derived the instantaneous washing load characteristics and inertial moment characteristics according to the amount of laundry and water level. Also, this studied about the dynamic driving characteristics simulation method for the prediction of washing performance based on this load characteristics analysis. For this study, the design parameters of the driving motor are obtained by FEM analysis and the experiment. By using theses motor parameters and load characteristics, the instantaneous driving characteristics simulation is accomplished and it is verified with the experimental result of various driving conditions. The results of this paper would be very useful to the prediction of washing mode operation characteristics, and it can be also utilized to the washer motor control algorithm design for the washing performance improvement.

• Nonlinear Functional Analysis and Applications
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• v.26 no.2
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• pp.411-432
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• 2021

In this paper, an inertial Halpern-type forward backward iterative algorithm for approximating solution of a monotone inclusion problem whose solution is also a fixed point of some nonlinear mapping is introduced and studied. Strong convergence theorem is established in a real Hilbert space. Furthermore, our theorem is applied to variational inequality problems, convex minimization problems and image restoration problems. Finally, numerical illustrations are presented to support the main theorem and its applications.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.1-11
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• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10a
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• pp.932-938
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• 1987

The dynamic equations of robotic manipulators can be derived from either Newton-Euler equation or Lagrangian equation. Model parameters which appear in the resulting dynamic equation are the nonlinear functions of both the inertial parameters and the geometric parameters of robotic manipulators. The identification of the model parameters is important for advanced robot control. In the previous methods for the identification of the model parameters, the geometric parameters are required to be predetermined, or the robotic manipulators are required to follow some special motions. In this paper, we propose an approach to the identification of the model parameters, in which prior knowledge of the geometric parameters is not necessary. We show that the estimation equation for the model parameters can be formulated in an upper block triangular form. Utilizing the special structures, we obtain a simplified least-square estimation algorithm for the model parameter identification. To illustrate the practical use of our method, a 4DOF SCARA robot is examined.