• Journal of Korea Game Society
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• v.20 no.3
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• pp.85-94
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• 2020

AI-based smart factory technologies are only increase short-term productivity. To solve this problem, collaborative intelligence combines human teamwork, creativity, AI speed, and accuracy to actively compensate for each other's shortcomings. However, current automation equipmens require high safety measures due to the high disaster intensity in the event of an accident. In this paper, we design and implement a factory safety control system that uses a depth camera to implement workers and facilities in the virtual world and to determine the safety of workers through simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1248-1258
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• 2019

This paper presents a moth-flame optimization (MFO)-based maximum power point tracking (MPPT) method for photovoltaic (PV) systems. The MFO algorithm is a new optimization method that exhibits satisfactory performance in terms of exploration, exploitation, local optima avoidance, and convergence. Therefore, the MFO algorithm is quite suitable for solving multiple peaks of PV systems under partial shading conditions (PSCs). The proposed MFO-MPPT is compared with four MPPT algorithms, namely the perturb and observe (P&O)-MPPT, incremental conductance (INC)-MPPT, particle swarm optimization (PSO)-MPPT and whale optimization algorithm (WOA)-MPPT. Simulation and experiment results demonstrate that the proposed algorithm can extract the global maximum power point (MPP) with greater tracking speed and accuracy under various conditions.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

• Journal of IKEEE
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• v.25 no.2
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• pp.337-343
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• 2021

Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.4
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• pp.37-44
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• 2021

In case of South Korea, the airspace which airlines can operate is extremely limited due to the military operational area located within the Incheon flight information region. As a result, safety problems such as mid-air collision between aircraft or Traffic alert and Collision Avoidance System Resolution Advisory (TCAS RA) may occur with higher probability than in wider airspace. In order to prevent such safety problems, an mid-air collision risk detection model based on Detect-And-Avoid (DAA) well clear metrics is investigated. The model calculates the risk of mid-air collision between aircraft using aircraft trajectory data. In this paper, the practical use of DAA well clear metrics based model has been validated. Aviation safety data such as aviation safety mandatory report and Automatic Dependent Surveillance Broadcast is used to measure the performance of the model. The attributes of individual aircraft track data is analyzed to correct the threshold of each parameter of the model.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.123-128
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• 2022

This paper proposes a mobile robot platform for education that can experiment with various autonomous driving algorithms such as obstacle avoidance and path planning. The platform consists of a robot module and a remote controller module, both of which are based on the Arduino Nano 33 IoT embedded board. The robot module is designed as a differential drive type using two encoder motors, and the speed of the motor is controlled using PID control. In the case of the remote controller module, a command to control the robot platform is received with a 2-axis joystick input, and an elliptical grid mapping technique is used to convert the joystick input into a linear and angular velocity command of the robot. WiFi and Zigbee are used for communication between the robot module and the remote controller module. The proposed robot platform was tested by measuring and comparing the linear velocity and angular velocity of the actual robot according to the linear velocity and angular velocity commands of the robot generated by the input of the joystick.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1838-1854
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• 2023

This paper presents a hybrid algorithm to solve the multi-objective path planning (MOPP) problem for mobile robots in a static nuclear accident environment. The proposed algorithm mimics a real nuclear accident site by modeling the environment with a two-layer cost grid map based on geometric modeling and Monte Carlo calculations. The proposed algorithm consists of two steps. The first step optimizes a path by the hybridization of improved ant colony optimization algorithm-modified A^* (IACO-A^*) that minimizes path length, cumulative radiation dose and energy consumption. The second module is the high radiation dose rate avoidance strategy integrated with the IACO-A^* algorithm, which will work when the mobile robots sense the lethal radiation dose rate, avoiding radioactive sources with high dose levels. Simulations have been performed under environments of different complexity to evaluate the efficiency of the proposed algorithm, and the results show that IACO-A^* has better path quality than ACO and IACO. In addition, a study comparing the proposed IACO-A^* algorithm and recent path planning (PP) methods in three scenarios has been performed. The simulation results show that the proposed IACO-A^* IACO-A^* algorithm is obviously superior in terms of stability and minimization the total cost of MOPP.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.117-133
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• 2024

This research analyzes users' cognitive responses to Out-Of-Home advertising content based in virtual environments. Specifically, it distinguishes the depth of advertisements as 2D or 3D and categorizes animation effects of light as a representation of virtuality into 'none', 'blinking', and 'flowing'. For this purpose, virtual reality content was developed, and after experiencing the content while wearing HMD, users' cognitive responses were investigated. Involving 60 participants, the study found that the interaction effects between depth perception and animation effects were not statistically significant, nor was the primary effect of depth perception. However, the main effect of animation showed statistically significant differences in users' attention, vividness, perceived intrusiveness, avoidance tendency, and flow. This study presents design elements that can be utilized in the production of virtual environment-based Out-Of-Home advertising and experimentally demonstrates that animation effects can serve as an effective means of emphasis in virtual environment advertising. These findings provide important insights for the creation and strategic planning of advertisements in virtual environments.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.196-202
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• 2024

Path-planning, a critical component of mobile robot navigation, comprises both local and global planning. Previous studies primarily focused on enhancing the individual performance of these planners, avoiding obstacles, and computing an optimal global path from a starting position to a target position. In this study, we introduce a practical path-planning framework that employs a target planner to bridge the local and global planners; this enables mobile robots to navigate seamlessly and efficiently toward a global target position. The proposed target planner assesses the visibility of waypoints along the global path, and it selects a reachable navigation target, which can then be used to generate efficient control commands for the local planners. A visibility-based target planner can handle situations, wherein the current, target waypoint is occupied by unknown obstacles. Real-world experiments demonstrated that the proposed pathplanning framework with the visibility-based target planner allowed the robot to navigate to the final target position along a more efficient path than the framework without a target planner.