• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.165-171
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• 2004

Robots will be able to coexist with humans and support humans effectively in near future. One of the most important aspects in the development of human-friendly robots is to cooperation between humans and robots. In this paper, we proposed a method for multi-object identification in order to achieve such human-centered system and robot localization in intelligent space. The intelligent space is the space where many intelligent devices, such as computers and sensors, are distributed. The Intelligent Space achieves the human centered services by accelerating the physical and psychological interaction between humans and intelligent devices. As an intelligent device of the Intelligent Space, a color CCD camera module, which includes processing and networking part, has been chosen. The Intelligent Space requires functions of identifying and tracking the multiple objects to realize appropriate services to users under the multi-camera environments. In order to achieve seamless tracking and location estimation many camera modules are distributed. They causes some errors about object identification among different camera modules. This paper describes appearance based object representation for the distributed vision system in Intelligent Space to achieve consistent labeling of all objects. Then, we discuss how to learn the object color appearance model and how to achieve the multi-object tracking under occlusions.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.3
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• pp.274-282
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• 2011

This paper is focused on dynamic modeling and control system design as well as vision based collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). Multi-rotor UAVs are defined as rotary-winged UAVs with multiple rotors. These multi-rotor UAVs can be utilized in various military situations such as surveillance and reconnaissance. They can also be used for obtaining visual information from steep terrains or disaster sites. In this paper, a quad-rotor model is introduced as well as its control system, which is designed based on a proportional-integral-derivative controller and vision-based collision avoidance control system. Additionally, in order for a UAV to navigate safely in areas such as buildings and offices with a number of obstacles, there must be a collision avoidance algorithm installed in the UAV's hardware, which should include the detection of obstacles, avoidance maneuvering, etc. In this paper, the optical flow method, one of the vision-based collision avoidance techniques, is introduced, and multi-rotor UAV's collision avoidance simulations are described in various virtual environments in order to demonstrate its avoidance performance.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.3
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• pp.252-259
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• 2011

This paper is focused on dynamic modeling and control system design as well as vision based collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). Multi-rotor UAVs are defined as rotary-winged UAVs with multiple rotors. These multi-rotor UAVs can be utilized in various military situations such as surveillance and reconnaissance. They can also be used for obtaining visual information from steep terrains or disaster sites. In this paper, a quad-rotor model is introduced as well as its control system, which is designed based on a proportional-integral-derivative controller and vision-based collision avoidance control system. Additionally, in order for a UAV to navigate safely in areas such as buildings and offices with a number of obstacles, there must be a collision avoidance algorithm installed in the UAV's hardware, which should include the detection of obstacles, avoidance maneuvering, etc. In this paper, the optical flow method, one of the vision-based collision avoidance techniques, is introduced, and multi-rotor UAV's collision avoidance simulations are described in various virtual environments in order to demonstrate its avoidance performance.

• The Journal of Korean Academic Society of Nursing Education
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• v.22 no.2
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• pp.171-181
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• 2016

Purpose: This study examined the effects of nursing professionalism, academic failure tolerance and social self-efficacy on college life satisfaction among nursing students. Methods: Data were collected between September 1 and October 16, 2015 via a self-reported questionnaire from 170 nursing students using convenient sampling methods. The survey included questions about nursing professionalism, academic failure tolerance, social self-efficacy, and college life satisfaction. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and hierarchical multiple regression with IBM SPSS/WIN 20.0. Results: Establishment vision about nursing science (${\beta}=.27$, p=.006), academic failure tolerance (${\beta}=.17$, p=.031) and social self-efficacy (${\beta}=.19$, p=.012) of nursing students were identified as significant predictors of college life satisfaction, after adjusting for establishment vision about nursing science and satisfaction in nursing science. This model explained 21.0% of the college life satisfaction in nursing students (F=6.38, p<.001). Conclusion: These results suggest that academic failure tolerance and social self-efficacy were significant factors influencing the college life satisfaction of nursing students. Also, as a strategy for improving the college life satisfaction of nursing students, it is necessary to develop programs that can help to establish apparent vision and to improve satisfaction in nursing science.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.10a
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• pp.33-34
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• 2015

The voyage and cargo volume passing through the Arctic route (NSR) have been gradually increased. The ship-ice collision is one of the most biggest factors threatening the safety navigation of ice-going ships. A lot of researchers are trying to reveal the ship-ice collision mechanism. In this study, some tests that a model ship is forced to collide with disk-shaped synthetic ice are carried out in a towing tank. Then, ice floe's motion (velocity and trajectory) is measured by machine vision inspection.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.4
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• pp.97-104
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• 2005

Vision-based lane sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system is developed with considering sensor configuration aspects. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3-dimensional road geometry can be reconstructed in a robust manner. A new monitoring model for estimating the road geometry parameters is constructed to reduce the number of the measured signals. The selection of the sensor configuration and specifications is investigated by utilizing the characteristics of standard highways. Based on the sensor configurations, it is shown that appropriate sensing region on the camera image coordinate can be determined. The proposed system is implemented on a passenger car and verified experimentally.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.22-30
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• 2019

For effective human-robot interaction, robots need to understand the current situation context well, but also the robots need to transfer its understanding to the human participant in efficient way. The most convenient way to deliver robot's understanding to the human participant is that the robot expresses its understanding using voice and natural language. Recently, the artificial intelligence for video understanding and natural language process has been developed very rapidly especially based on deep learning. Thus, this paper proposes robot vision to audio description method using deep learning. The applied deep learning model is a pipeline of two deep learning models for generating natural language sentence from robot vision and generating voice from the generated natural language sentence. Also, we conduct the real robot experiment to show the effectiveness of our method in human-robot interaction.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.126-130
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• 2021

The ball grid array is one of the packaging methods that used in high density printed circuit board. Solder void defects caused by voids in the solder ball during the BGA process do not directly affect the reliability of the product, but it may accelerate the aging of the device on the PCB layer or interface surface depending on its size or location. Void inspection is important because it is related in yields with products. The most important process in the optical inspection of solder void is the segmentation process of solder and void. However, there are several segmentation algorithms for the vision inspection, it is impossible to inspect all of images ideally. When X-Ray images with poor contrast and high level of noise become difficult to perform image processing for vision inspection in terms of software programming. This paper suggests the solution to deal with the suggested problem by means of using Mask R-CNN instead of digital image processing algorithm. Mask R-CNN model can be trained with images pre-processed to increase contrast or alleviate noises. With this process, it provides more efficient system about complex object segmentation than conventional system.

• Smart Structures and Systems
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• v.30 no.1
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• Journal of Acupuncture Research
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• v.40 no.1
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• pp.44-52
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• 2023

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion: The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.