• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.147-154
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• 2020

This study is about the development of ICT-based wearable devices for vision recovery that can cause functional myopia improvement through accommodation training. Vision Training Device(OTUS) is a head mount type wearable device, which naturally stimulates the contraction and relaxation of the ciliary muscles of eye. Users can conduct customized vision training based on personal vision information stored through the device. In the experiment, the effects of improvement of the symptoms by the accommodation training were compared and analysed for the two groups (16 comparative group and 16 accommodation training group) after causing functional myopia. The result showed the functional myopia improved average 0.44D±0.35 (p<0.05) at the accommodation training group compared to the comparative group. This study proved the effectiveness of vision training device(OTUS) on functional myopia, but further clinical trials are judged necessary to prove the possibility of long-term control of the functional myopia.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.8 s.173
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• pp.192-198
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• 2005

In this paper, we propose a new training system for the improvement of equilibrium sense using unstable platform. The equilibrium sense, which provides orientation with respect to gravity, is important to integrate the vision, somatosensory and vestibular function to maintain the equilibrium sense of the human body. In order to improve the equilibrium sense, we developed the software program such as a block game, pingpong game using Visual C++. These training system for the equilibrium sense consists of unstable platform, computer interface and software program. The unstable platform was a simple structure of elliptical-type which included tilt sensor, wireless RF module and the device of power supply. To evaluate the effect of balance training, we measured and evaluated the parameters as the moving time to the target, duration to maintain cursor in the target of screen and the error between sine curve and acquired data. As a results, the moving time to the target and duration to maintain cursor in the target was improved through the repeating training of equilibrium sense. It was concluded that this system was reliable in the evaluation of equilibrium sense. This system might be applied to clinical use as an effective balance training system.

• Oral Biology Research
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• v.42 no.4
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• pp.235-240
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• 2018

Standard oral and maxillofacial three-dimensional model was developed with patients' medical data while virtual reality (VR) simulator was developed in conjunction with head mount display (HMD) and Haptic device. The objective of this study was to evaluate the preclinical use of a VR training simulator in tooth preparation practice. Eighty-nine dental students were trained how to operate the simulator. The participants were then given sufficient time on the simulator to practice dental preparation. The students experience and opinion was then taken in through filling of questionnaires. On average content received 1.8 points, anatomy had 2.5 points, 2.6 points for the applicability, and 2.0 for the usability. As for the detailed items scores, queries about the possible development of the simulator and the interest of the learning process through the simulator were the highest at 3.1 and 3.0 points, respectively. Question about the benefit of the HMD and the haptic device during the practice had 1.5 and 1.6 points, respectively. The average total score was 2.2 points. VR tooth preparation simulator in the field of clinical dental education has powerful potential in regard to realistic models, environments, vision, posture, and economical efficiency.

• Korean Journal of Computational Design and Engineering
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• v.18 no.3
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• pp.155-166
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• 2013

We present recent human-vehicle interaction (HVI) research conducted in the area of defense and military application. Research topics discussed in this paper include: training simulation for overland navigation tasks; expertise effects in overland navigation performance and scan patterns; pilot's perception and confidence on an overland navigation task; effects of UAV (Unmanned Aerial Vehicle) supervisory control on F-18 formation flight performance in a simulator environment; autonomy balancing in a manned-unmanned teaming (MUT) swarm attack, enabling visual detection of IED (Improvised Explosive Device) indicators through Perceptual Learning Assessment and Training; usability test on DaViTo (Data Visualization Tool); and modeling peripheral vision for moving target search and detection. Diverse and leading HVI study in the defense domain suggests future research direction in other HVI emerging areas such as automotive industry and aviation domain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.490-493
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• 2021

This paper proposes an embedded system that detects mask and face recognition based on a microprocessor instead of Nvidia Jetson Board what is popular development kit. We use a class of efficient models called Mobilenets for mobile and embedded vision applications. MobileNets are based on a streamlined architechture that uses depthwise separable convolutions to build light weight deep neural networks. The device used a Maix development board with CNN hardware acceleration function, and the training model used MobileNet_V2 based SSD(Single Shot Multibox Detector) optimized for mobile devices. To make training model, 7553 face data from Kaggle are used. As a result of test dataset, the AUC (Area Under The Curve) value is as high as 0.98.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.421-434
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• 2008

As computer graphics, virtual reality and augmented reality technologies have been developed, in many application areas based on those techniques, interaction for 3D space is required such as selection and manipulation of an 3D object. In this paper, we propose a framework for a vision-based 3D interaction which enables to simulate functions of an expensive 3D mouse for a desktop environment. The proposed framework includes a specially manufactured interaction device using three-color LEDs. By recognizing position and color of the LED from video sequences, various events of the mouse and 6 DOF interactions are supported. Since the proposed device is more intuitive and easier than an existing 3D mouse which is expensive and requires skilled manipulation, it can be used without additional learning or training. In this paper, we explain methods for making a pointing device using three-color LEDs which is one of the components of the proposed framework, calculating 3D position and orientation of the pointer and analyzing color of the LED from video sequences. We verify accuracy and usefulness of the proposed device by showing a measurement result of an error of the 3D position and orientation.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1057-1064
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• 2024

The construction industry has witnessed a concerning rise in ladder-related accidents, necessitating the implementation of stricter safety measures. Recent statistics highlight a substantial number of accidents occurring while using ladders, emphasizing the mandatory need for preventative measures. While prior research has explored computer vision-based automatic monitoring for specific aspects such as ladder stability with and without outriggers, worker height, and helmet usage, this study extends existing frameworks by introducing a rule set for co-workers. The research methodology involves training a YOLOv5 model on a comprehensive dataset to detect both the worker on the ladder and the presence of co-workers in real time. The aim is to enable smooth integration of the detector into a mobile application, serving as a portable real-time monitoring tool for safety managers. This mobile application functions as a general safety tool, considering not only conventional risk factors but also ensuring the presence of a co-worker when a worker reaches a specific height. The application offers users an intuitive interface, utilizing the device's camera to identify and verify the presence of coworkers during ladder activities. By combining computer vision technology with mobile applications, this study presents an innovative approach to ladder safety that prioritizes real-time, on-site co-worker verification, thereby significantly reducing the risk of accidents in construction environments. With an overall mean average precision (mAP) of 97.5 percent, the trained model demonstrates its effectiveness in detecting unsafe worker behavior within a construction environment.

• Smart Structures and Systems
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• v.33 no.1
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• pp.55-64
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• 2024

This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.161-166
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• 2014

Recently, indoor sports simulator equipped with virtual reality devices, like screen golf system, are riding high. There have been many attempts to develop the indoor simulator systems which can make people enjoy exercises in various sports area. A real horseback riding could not have been popularized, because of the cost involved, difficulty to learn and its dangerousness. In this research, a robotic horseback riding platform based on parallel mechanism and virtual reality device is proposed. The proposed platform provides realistic riding feels and various levels of riding difficulty. The equipped motion capture system with a vision sensor enables riders to correct their riding posture based on expert's one. The developed horseback riding platform make it possible to enjoy a horseback riding in all weather, and also can be used for systematic horseback riding training.