• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.571-576
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• 2021

This study was carried out to overcome the problems of the existing fitness business and to build a fitness system that can meet the increased demand in the Corona situation. As a platform technology for non-face-to-face fitness edutainment service, it is a next-generation fitness exercise device that can use various body parts and synchronize network-type information. By synchronizing the exercise information of the fitness equipment, it was composed of learning contents through MR-based avatars. A quantified result was derived from examining the applicability of the customized evaluation system through momentum analysis with A.I analysis applying the LSTM-based algorithm according to the cumulative exercise effect of the user. It is a motion capture and 3D visualization fitness program for the application of systematic exercise techniques through academic experts, and it is judged that it will contribute to the improvement of the user's fitness knowledge and exercise ability.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.1
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• pp.1-6
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• 2022

Vocal fold injection (VFI) is widely accepted as a first line treatment in treating unilateral vocal fold paralysis and other vocal fold diseases. Although VFI is advantageous for its minimal invasiveness and efficiency, the invisibility of the needle tip remains an essential handicap in precise localization. Real-time light-guided vocal fold injection (RL-VFI) is a novel technique that was developed under the concept of performing simultaneous injection with precise placement of the needle tip under light guidance. RL-VFI has confirmed its possibility of technical implementation and the feasibility in injecting the needle from various directions through ex vivo animal studies. Further in vivo animal study has approved the safety and feasibility of the procedure when various transcutaneous approaches were applied. Currently, RL-VFI device is authorized for clinical use by the Ministry of Food and Drug Safety in South Korea and is clinically applied to patients with safe and favorable outcome. Several clinical studies are currently under process to approve the safety and the efficiency of RL-VFI. RL-VFI is expected to improve the complication rate and the functional outcome of voice. Furthermore, it will support laryngologists in overcoming the steep learning curve by its intuitive guidance.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.75-82
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• 2022

There are various exhibition halls, shopping malls, theme parks around us and analysis of interest in exhibits or contents is mainly done through questionnaires. These questionnaires are mainly depend on the subjective memory of the person being investigated, resulting in incorrect statistical results. Therefore, it is possible to identify an exhibition space with low interest by tracking the movement and counting the number of visitors. Based on this, it can be used as quantitative data for exhibits that need replacement. In this paper, we use deep learning-based artificial intelligence algorithms to recognize visitors, assign IDs to the recognized visitors, and continuously track them to identify the movement path. When visitors pass the counting line, the system is designed to count the number and transmit data to the server for integrated management.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.29-34
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• 2022

We propose a novel network architecture and build dataset for recognizing clickable objects on mobile device screens. The data was collected based on clickable objects on the mobile device screen that have numerous resolution, and a total of 24,937 annotation data were subdivided into seven categories: text, edit text, image, button, region, status bar, and navigation bar. We use the Deconvolution Single Shot Detector as a baseline, the backbone network with Squeeze-and-Excitation blocks, the Single Shot Detector layer structure to derive inference results and the Feature pyramid networks structure. Also we efficiently extract features by changing the input resolution of the existing 1:1 ratio of the network to a 1:2 ratio similar to the mobile device screen. As a result of experimenting with the dataset we have built, the mean average precision was improved by up to 101% compared to baseline.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.812-815
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• 2022

Images displayed on a digital devices under the sunlight are generally perceived to be darker than the original images, which leads to a decrease in visibility. For better visibility, global luminance compensation or tone mapping adaptive to ambient lighting is required. However, the existing methods have limitations in chrominance compensation and are difficult to use in real world due to their heavy computational cost. To solve these problems, this paper propose a piece-wise linear curves (PLECs)-based image enhancement method to improve both luminance and chrominance. At this time, PLECs are regressed through deep learning and implemented in the form of a lookup table to real-time operation. Experimental results show that the proposed method has better visibility compared to the original image with low computational cost.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.685-694
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• 2022

Building information is essential for various urban spatial analyses. For this reason, continuous building monitoring is required, but it is a subject with many practical difficulties. To this end, research is being conducted to extract buildings from satellite images that can be continuously observed over a wide area. Recently, deep learning-based semantic segmentation techniques have been used. In this study, a part of the structure of the context-based ResU-Net was modified, and training was conducted to automatically extract a building from a 30 cm Worldview-3 RGB image using SpaceNet's building v2 free open data. As a result of the classification accuracy evaluation, the f1-score, which was higher than the classification accuracy of the 2nd SpaceNet competition winners. Therefore, if Worldview-3 satellite imagery can be continuously provided, it will be possible to use the building extraction results of this study to generate an automatic model of building around the world.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.872-884
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• 2022

With the development of the IoT industry, different types of time series data are being generated in various industries, and it is evolving into research that reproduces and utilizes it through re-integration. In addition, due to data processing speed and issues of the utilization system in the actual industry, there is a growing tendency to compress the size of data when using time series data and integrate it. However, since the guidelines for integrating time series data are not clear and each characteristic such as data description time interval and time section is different, it is difficult to use it after batch integration. In this paper, two integration methods are proposed based on the integration criteria setting method and the problems that arise during integration of time series data. Based on this, integration framework of a heterogeneous time series data was constructed that is considered the characteristics of time series data, and it was confirmed that different heterogeneous time series data compressed can be used for integration and various machine learning.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.