• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.5
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• pp.551-557
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• 2014

This paper presents a robust lateral controller for autonomous guidance of a farm tractor in field operations. Although mechanical steering actuators have recently been used for passenger vehicles, the steering actuator of the farm tractor is based on a hydraulic system, resulting in limited bandwidth and a larger time delay. Based on a kinematic tractor model with steering actuator dynamics, a nonlinear control technique called dynamic surface control is applied to design a robust lateral controller that compensates for uncertainty owing to steering actuator and road geometry. Finally, tracking performance and robustness of the proposed controller are validated via commercial tractor simulations, with respect to the time delay of the steering actuator and road geometry (e.g., up and down hills), on a given field with a constant friction coefficient.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.213-216
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• 2007

The paper deals with a approach to underwater acoustic based Ad-hoc communication, which allows major design strategies for Media Access Control (MAC) within a group of the autonomous underwater vehicles(AUV). The proposed MAC aims at deploying AUV-centric star topology, which minimizes overhead of sensor nodes and improves energy-efficiency. Furthermore, that is also well under long and unknown propagation delays of the underwater acoustic medium. The implemented MAC protocol makes it easier to achieve frame synchronization than TDMA due to deploying localized schedule time, in addition to saving energy consumption by letting nodes sleep. It is also superior to MACA and MACAW in terms of propagation delay. This scalable centralized protocol has the potential to serve as a primer for development of MAC protocol for future underwater acoustic based ad-hoc networks.

• Journal of Ocean Engineering and Technology
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• v.29 no.6
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• pp.488-498
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• 2015

This paper presents the dynamic model of an AUV called HW200 for underwater surveillance. The mathematical model of HW200 is briefly introduced, considering its shape. The maneuvering coefficients were initially estimated using empirical formulas and a database of vehicles with similar shapes. A motion simulator, based on Simulink of Mathworks, was developed to evaluate the mathematical model of the vehicle and to tune the maneuvering coefficients. The parameters were finely tuned by comparing the experimental results and simulated responses generated with the simulator by applying the same control inputs as the experiment. The velocity of HW200 in the tuning process was fixed at a constant forward speed of 1.83 m/s. Simulations with variable speed commands were conducted, and the results showed good consistency in the motion response, attitude, and velocity of the vehicle, which were similar to those of the experiment even under the speed variation. This paper also discusses the feasibility of its application to a model-based integrated navigation system (INS) using the auxiliary information on the velocities generated by the model.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.106-113
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• 2017

In this paper, we propose a suitable network topology for the Ethernet based Train Communication Network (TCN) for control system in a autonomous multi-articulated vehicle. We propose a network topology considering the structural constraints such as the number of cables and ports, and the performance constraints such as network response time and maximum throughput. We compare the network performances of star topology and daisy chain topology as well as hybrid topology, which is proposed in previous studies and a compromise between daisy chain and star topology. Here, the appropriate number of nodes in a group is obtained for the configuration of the hybrid topology. We first derive estimates of the network performance through simulation with different topologies, and then, implement the network by connecting the actual devices with each network topology. The performance of each topology is measured using various network performance measurement programs and the superiority of the proposed topology is described through comparison.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.231-243
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• 2017

The purpose of this study is to improve the social acceptance of AVs by analyzing the citizen's perception using an emotional analysis technique which belongs to a type of text mining. The source of the data is originated from 3 year accumulated internet articles and comments on AV from 164 newspapers and Naver. According to the study results, there exists a positive perception on AVs, although negative ones are more frequent than the positive. Also most of people take neutral position on AV due to the unfamiliarity and lack of experience on AVs And these problems needs to be responded before AV's commercialization through continuous analyses on the perception and social acceptance.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.671-678
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• 2020

In this paper, a wireless measurement system has been developed which can measure the deformation of a tire in real time by utilizing strain gauge sensors and Zigbee wireless communication. A strain gauge sensor is mounted inside the tire and then the strain on the tire is obtained using the DSP module. The acquired sensor values are transmitted into the vehicle by radio communication. The wireless receiver module installed inside the vehicle can monitor the deformation of the tire in real time. The deformation of the tire can be used for measuring the load applied to each tire or the speed of the tire. The load or speed applied to the tires are essential parameters for the stable control of autonomous vehicles.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2002.10a
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• pp.142-148
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• 2002

Autonomous underwater vehicles (AUVs) are unmanned underwater vessels to investigate sea environments, oceanography and deep-sea resources autonomously. Docking systems are required to increase the capability of the AUVs to recharge the batteries and to transmit data in real time for specific underwater works, such as repeated jobs at sea bed. This paper presents a visual servo control system for an AUV to dock into an underwater station with a camera mounted at the nose center of the AUV. To make the visual servo control system, this paper derives an optical flow model of a camera, where the projected motions of the image plane are described with the rotational and translational velocities of the AUV. This paper combines the optical flow equation of the camera with the AUVs equation of motion, and derives a state equation for the visual servoing AUV. This paper proposes a discrete-time MIMO controller minimizing a cost function. The control inputs of the AUV are automatically generated with the projected target position on the CCD plane of the camera and with the AUVs motion. To demonstrate the effectiveness of the modeling and the control law of the visual servoing AUV, simulations on docking the AUV to a target station are performed with the 6-dof nonlinear equations of REMUS AUV and a CCD camera.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.352-360
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• 2019

This paper proposes a docking assessment algorithm for an autonomous underwater vehicles (AUVs) with sensor uncertainties. The proposed algorithm consists of two assessments, state assessment and probability assessment. The state assessment verifies the reachability by comparing forward distance to the docking station with expected distance to reach same depth as the docking station and necessity for correcting its route by comparing calculated inaccessible areas based on turning radius of the AUV to position of the docking station. When the AUV and the docking station is close enough and the state assessment is satisfied, the probability assessment is conducted by computing success probability of docking based on the direction angle, relative position to the docking station, and sensor uncertainties of the AUV. The final output of the algorithm is decided by comparing the success probability to threshold whether to try docking or to correct its route. To verify the validation of the suggested algorithm, the scenario that the AUV approaches to the docking station is implemented through Matlab simulation.

• Journal of IKEEE
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• v.22 no.4
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• pp.1073-1078
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• 2018

As interest in the 4th Industrial Revolution increases, the CPS, in which things existing in the reality and things existing in the virtual interact with each other, is attracting attention as an important technology. Complex systems such as electric vehicles, autonomous driving, smart factories, and smart grid system are considered as core technology fields of the 4th Industrial Revolution, and many types of research have been conducted to develop it. The reliability of the system is directly related to the safety of people in case of the autonomous driving, and verification of the actual vehicle's hardware and software of ADAS is essential. In this paper, we proposed distributed simulation middleware supporting HILS for reliable verification of the complex hybrid systems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.