• The Journal of Korean Academic Society of Nursing Education
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• v.28 no.3
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• pp.328-339
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• 2022

Purpose: This study was conducted to identify the mental health status of Korean adults during the COVID-19 pandemic and to verify the predictors and mediating effects of avoidance coping on the relationship between the intolerance of uncertainty and anxiety and the intolerance of uncertainty and depression. Methods: An online survey was completed by 191 Korean adults from 19 to 64 years old. Using the IBM SPSS Win 19.0 program, the data were analyzed through the frequency, mean, t-test or analysis of variance, Pearson's correlation coefficient, linear regression analysis and Sobel test. Results: Of the survey respondents 21.5%, and 33.5% respectively were classified into anxiety and depression risk groups. The predictors of anxiety were intolerance of uncertainty (β=.52), avoidance coping (β=.21), and response efficacy (β=-.15). Those variables explained 47.7% of the respondents' anxiety. The predictors of depression were intolerance of uncertainty (β=.40), avoidance coping (β=.20), and response-efficacy (β=-.12). These variables explained 34.9% of the participants' depression. Among the predictors of anxiety and depression, avoidance coping was the significant mediating variable. Conclusion: The predictors of anxiety and depression during the COVID-19 pandemic were revealed to be intolerance of uncertainty, avoidance coping, and response-efficacy. These results indicate the necessity of providing the cognitive interventions and reducing the use of avoidance coping strategies on a personal level. Community-level efforts, including early detection and health communication strategies, should prioritize risk groups for example young adults. The study suggests it will be necessary to provide sufficient information, psychological support and economic policy alternatives related to the COVID-19 pandemic on the national level.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.63-71
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• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.186-193
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• 2008

We propose a novel real-time obstacle avoidance method for rescue robots. This method, named the ELA(Emergency Level Around), permits the detection of unknown obstacles and avoids collisions while simultaneously steering the mobile robot toward safe position. In the ELA, we consider two sensor modules, PSD(Position Sensitive Detector) infrared sensors taking charge of obstacle detection in short distance and LMS(Laser Measurement System) in long distance respectively. Hence if a robot recognizes an obstacle ahead by PSD infrared sensors first, and judges impossibility to overcome the obstacle based on driving mode decision process, the order of priority is transferred to LMS which collects data of radial distance centered on the robot to avoid the confronted obstacle. After gathering radial information, the ELA algorithm estimates emergency level around a robot and generates a polar histogram based on the emergency level to judge where the optimal free space is. Finally, steering angle is determined to guarantee rotation to randomly direction as well as robot width for safe avoidance. Simulation results from wandering in closed local area which includes various obstacles and different conditions demonstrate the power of the ELA.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.75-84
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• 2013

In this paper, the guidance law for formation flight and collision avoidance of multiple Unmanned Aerial Vehicle (UAV)s is proposed. To construct the physically comprehensible guidance law for formation flight, the virtual structure approach is used. To develop a guidance law for collision avoidance considering both other UAVs and unknown static obstacles, a geometric approach using information such as a relative position vector is utilized. Through the Lyapunov theorem, the stability of the proposed guidance law is proved. To combine guidance commands, the concept of the elastic weighting factor inspired by the elastic behavior of shape memory polymer, which tends to regain its original shape after deformation, is introduced. By using the concept of elastic weighting factor, multiple UAVs are able to cope actively with the situation of a collision between both UAVs and static obstacles during the formation flight. To verify the performance of the proposed method, numerical simulations are performed.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.11
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• pp.119-126
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• 2007

This paper presents an efficient heuristic algorithm to avoid crosstalk which effects to delay of CMOS digital circuit by downsizing and upsizing of Gate. The proposed algorithm divide into two step, step1 performs downsizing of gate, step2 performs upsizing, so that avoid adjacent aggressor to critical path in series. The proposed algorithm has been verified on LGSynth91 benchmark circuits and Experimental results show an average 8.64% Crosstalk Avoidance effect. This result proved new potential of proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5442-5447
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• 2013

This paper focuses on development of SCAAM(Ship Collision Avoidance Assignment Model) for avoiding ship collison at sea. We take a new look at DCPA, TCPA, VCD, collision concept for ship collision avoidance and propose SCAAM using DCPA pre-assignmented by a ship master on information collected by other ship's AIS, GPS (course-speed, destination, length, width, tonnage etc). If A ship is a collision situation, the ship master makes a decision where the ship makes a evasion voyage or not continually using SCCAM. If ship master decides a evasion voyage, the ship is voyaged by CORLEGS (International Regulations for Preventing Collisions at Sea). This paper contributes to safety navigation by decreasing the ship collision accident by human's error.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.2
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• pp.65-71
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• 2018

In order to make the underwater vehicle carry out the mission in a submarine environment, it is needed to plan a safe and efficient route to a given destination and prevent the autonomous submersible from colliding with obstacles while moving along the planned route. The function of collision avoidance makes the travel distance of the autonomous submersible longer. Moreover, it should move slowly near to obstacles against their moving disturbance. As a result, this invokes the degradation of the navigation efficiency in the process of collision avoidance. The side effect of the collision avoidance is not ignorable in the case of high congested environments such as the coast with many obstacles. In this paper, we suggest a path planning method which provides the route with minimum travel time considering collision avoidance in congested environment. For the purpose, we define environmental congestion map related to geometric information and obstacles. And we propose a method to consider the moving cost in the RRT scheme that provides the existing minimum distance path. We verified that the efficiency of our algorithm with simulation experiments.

• Journal of Ocean Engineering and Technology
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• v.30 no.5
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• pp.426-430
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• 2016

Recently, many unmanned surface vehicles (USVs) have been developed and researched for various fields such as the military, environment, and robotics. In order to perform purpose specific tasks, common autonomous navigation technologies are needed. Obstacle avoidance is important for safe autonomous navigation. This paper describes a vector field histogram+ (VFH+) based obstacle avoidance method that uses the monocular vision of an unmanned surface vehicle. After creating a polar histogram using VFH+, an open space without the histogram is selected in the moving direction. Instead of distance sensor data, monocular vision data are used for make the polar histogram, which includes obstacle information. An object on the water is recognized as an obstacle because this method is for USV. The results of a simulation with sea images showed that we can verify a change in the moving direction according to the position of objects.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.169-175
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• 2022

Unexpected events, such as the COVID-19 pandemic, can occur at any time and have an influence on all countries. The COVID-19 pandemic has infected more than 200 nations, including Indonesia. As a result of this phenomenon, Indonesia's state revenue system will need to be adjusted. Therefore, the goal of this research is to see if there are any differences in taxation in Indonesia as a result of the COVID-19 incident. The data was collected using the base years of 2018, 2019, and 2020. The information came from the financial statements of companies in the industrial sector that are publicly traded on the Indonesian Stock Exchange (IDX). Purposive sampling was used, and there were 54 companies represented in the samples that met the criterion. In this study, the difference test was used as an analytical technique. According to the findings, there was no difference in the pattern of tax avoidance between pre-COVID-19 in 2019 and during the COVID-19 period in terms of leverage and fixed asset intensity. It occurred because the tax avoidance policy was implemented as a short-term fiscal strategy to ensure the company's existence. Finally, because these findings were restricted to the Indonesian environment, their generalizability was limited.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.91-99
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• 2024

Collision avoidance system is important to improve the robustness and functional safety of autonomous vehicles. This paper proposes an object-level distance estimation method to develop a collision avoidance system, and it is applied to golfcarts utilized in country club environments. To improve the detection accuracy, we continually trained an object detection model based on pseudo labels generated by a pre-trained detector. Moreover, we propose object-aware depth estimation (OADE) method which trains a depth model focusing on object regions. In the OADE algorithm, we generated dense depth information for object regions by utilizing detection results and sparse LiDAR points, and it is referred to as object-aware LiDAR projection (OALP). By using the OALP maps, a depth estimation model was trained by backpropagating more gradients of the loss on object regions. Experiments were conducted on our custom dataset, which was collected for the travel distance of 22 km on 54 holes in three country clubs under various weather conditions. The precision and recall rate were respectively improved from 70.5% and 49.1% to 95.3% and 92.1% after the continual learning with pseudo labels. Moreover, the OADE algorithm reduces the absolute relative error from 4.76% to 4.27% for estimating distances to obstacles.