• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.309-319
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• 2020

Billiards is a fun and popular sport, but both route planning and cueing prevent beginners from becoming skillful. A beginner in billiards requires constant concentration and training to reach the right level, but without the right motivating factor, it is easy to lose interests. This study aims to induce interest in billiards and accelerate learning by utilizing billiard path prediction and visualization on a highly immersive augmented reality platform that combines a stereo camera and a VR headset. For implementation, the placement of billiard balls is recognized through the OpenCV image processing program, and physics simulation, path search, and visualization are performed in Unity Engine. As a result, accurate path prediction can be achieved. This made it possible for beginners to reduce the psychological burden of planning the path, focus only on accurate cueing, and gradually increase their billiard proficiency by getting used to the path suggested by the algorithm for a long time. We confirm that the proposed AR billiards is remarkably effective as a learning assistant tool.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Composites Research
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• v.35 no.1
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• pp.38-41
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• 2022

In this study, the strain rate dependent tensile and compressive properties of PP-LGF and TPO was investigated under the high strain rate by using the Split Hopkinson Pressure Bar (SHPB). The SHPB is the most widely used apparatus to characterize dynamic mechanical behavior of materials at high strain rates between 100 s^-1 and 10,000 s^-1. The SHPB test is based on the wave propagation theory which was developed to give the stress, strain and strain rate in the specimen using the strains measured in the incident and transmission bars. In addition, to verify the strain data obtained from SHPB, the specimen was photographed with a high-speed camera and compared with the strain data obtained through the Digital Image Correlation (DIC).

• Composites Research
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• v.35 no.3
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• pp.196-200
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• 2022

Split Hopkinson Pressure Bar (SHPB) is a general test equipment for measuring the mechanical properties of high modulus metal and composite materials at high strain rate. However, for the soft plastic material, it is difficult to hold the specimen and achieve dynamic stress equilibrium due to the weak transmitted signals. In this study, SHPB test apparatus were designed to measure accurately the high strain rate stress-strain curve of the soft plastic materials by changing the incident bar materials and the shape of the specimen holder parts. In addition, to verify the high strain-rate tensile strain data obtained from SHPB, the strain distribution of the specimen was measured and analyzed with a high-speed camera and the digital image correlation (DIC), which was compared with the strain history measured from SHPB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of dental hygiene science
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• v.21 no.4
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• pp.219-226
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• 2021

Background: Proper detection and management of dental plaque are essential for individual oral health. We aimed to evaluate the maturation level of dental plaque using a two-tone disclosing agent and to compare it with the fluorescence of dental plaque on the quantitative light-induced fluorescence (QLF) image to obtain primary data for the development of a new dental plaque scoring system. Methods: Twenty-eight subjects who consented to participate after understanding the purpose of the study were screened. The images of the anterior teeth were obtained using the QLF device. Subsequently, dental plaque was stained with a two-tone disclosing solution and a photograph was obtained with a digital single-lens reflex (DSLR) camera. The staining scores were assigned as follows: 0 for no staining, 1 for pink staining, and 2 for blue staining. The marked points on the DSLR images were selected for RGB color analysis. The relationship between dental plaque maturation and the red/green (R/G) ratio was evaluated using Spearman's rank correlation. Additionally, different red fluorescence values according to dental plaque accumulation were assessed using one-way analysis of variance followed by Scheffe's post-hoc test to identify statistically significant differences between the groups. Results: A comparison of the intensity of red fluorescence according to the maturation of the two-tone stained dental plaque confirmed that R/G ratio was higher in the QLF images with dental plaque maturation (p<0.001). Correlation analysis between the stained dental plaque and the red fluorescence intensity in the QLF image confirmed an excellent positive correlation (p<0.001). Conclusion: A new plaque scoring system can be developed based on the results of the present study. In addition, these study results may also help in dental plaque management in the clinical setting.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.77-84
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• 2021

In this paper, we propose a system that uses image data and beacon data to classify authorized and unauthorized perosn who are allowed to enter a group facility. The image data collected through the IP camera uses YOLOv4 to extract a person object, and collects beacon signal data (UUID, RSSI) through an application to compose a fingerprinting-based radio map. Beacon extracts user location data after CNN-LSTM-based learning in order to improve location accuracy by supplementing signal instability. As a result of this paper, it showed an accuracy of 93.47%. In the future, it can be expected to fusion with the access authentication process such as QR code that has been used due to the COVID-19, track people who haven't through the authentication process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.1-8
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• 2022

Recently, Google MedeiaPipe presents a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Though neural network related to hair segmentation is relatively small size, it produces a high-quality hair segmentation mask that is well suited for AR effects such as a realistic hair recoloring. However, it has undesirable segmentation effects according to hair styles or in case of containing noises and holes. In this study, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood function. It is further optimized according to graph cuts algorithm and initial hair region is obtained. Finally, clustering algorithm and image post-processing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. The proposed method is applied to MediaPipe hair segmentation pipeline.

• Journal of radiological science and technology
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• v.46 no.1
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• pp.29-36
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• 2023

To have highly reliable diagnostic performance of it, this study comparatively analyzed spatial resolution of SPECT images and interrelationship depending on the changes of system uniformity of ga㎜a camera through phantom analysis. This study chose 6 kinds of results from quality control (uniformity) of triple head SPECT scanner operated in an university hospital in Seoul for six months. Then, study measured spatial resolutions (FWHM) of the images restructured by injecting radiopharmaceuticals to Jaszczak phantom, and doing SPECT scanning under the same conditions as clinical ones using the analytical program (image J). Quality controls performed by the experimental institution showed that differential uniformity of UFOV ranged from 2.76% to 7.61% (4.46±2.07), and integral uniformity of UFOV ranged from 1.98% to 5.42% (3.01±1.43). Meanwhile, Quantitative analysis evaluations of phantom images depending on the changes of uniformity of SPECT scanner detector showed that as the uniformity values of UFOV and CFOV decreased, FWHM values of phantom images decreased from 8.5 ㎜ to 5.8 ㎜. That is, it was quantitatively identified that the higher uniformity of detector is, the better spatial resolution of images gets (P<0.05). It is very important to perform continuous and consistent quality control of the nuclear medicinal system, and users should be clearly conscious of it.