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Se-Hoon Jung

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Journal article
Published: 29 June 2021 in Healthcare
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The need for non-face-to-face online health care has emerged through the era of “untact”. However, there is a lack of standardization work and research cases on the exercise effect of immersive content. In this study, the possibility of the exercise effect of VR e-sports among e-sports cases were presented through a visual algorithm analysis. In addition, the evaluation criteria were established. The research method compares and analyzes e-sports cases and VR e-sports cases by applying existing evaluation research cases. It also sets up a new evaluation standard. As for the analysis result, the device immersion method and interaction range were set through an algorithm analysis; FOV and frame immersion were set through typification; the user recognition method and interaction method were set through the visual diagram. Then, each derived result value was quantified and a new evaluation criterion was proposed.

ACS Style

Sang-Guk Lim; Se-Hoon Jung; Jun-Ho Huh. Visual Algorithm of VR E-Sports for Online Health Care. Healthcare 2021, 9, 824 .

AMA Style

Sang-Guk Lim, Se-Hoon Jung, Jun-Ho Huh. Visual Algorithm of VR E-Sports for Online Health Care. Healthcare. 2021; 9 (7):824.

Chicago/Turabian Style

Sang-Guk Lim; Se-Hoon Jung; Jun-Ho Huh. 2021. "Visual Algorithm of VR E-Sports for Online Health Care." Healthcare 9, no. 7: 824.

Journal article
Published: 23 November 2020 in Electronics
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This study set out to invent an Information and Communication Technologies (ICT)-based smart Acer mono sap collection electric device to make efficient use of the labor force by reducing inefficient activities of old manual work to record sap exudation and state information. Based on the assumption that environmental information would have close connections with Acer mono sap exudation to reinforce the competitive edge of production in forest products, the study analyzed correlations between Acer mono sap exudation and environmental information and predicted Acer mono exudation. A smart collection of electric devices would gather data about Acer mono sap exudation per hour on outdoor temperature, humidity, conductivity, and wind direction and velocity, and was installed in four areas in the Republic of Korea, including Sancheong, Gwangyang, Geoje, and Inje. Collected data were used to analyze correlations between environmental information and Acer mono sap exudation using four different algorithms, including linear regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and random forest, to predict Acer mono sap exudation. Remarkable outcomes were obtained across all the algorithms except for linear regression, demonstrating close connections between environmental information and Acer mono sap exudation. The random forest model, which showed the most outstanding performance, was used to make a mobile app capable of providing predicted Acer mono sap exudation and collected environmental information.

ACS Style

Se-Hoon Jung; Jun-Yeong Kim; Jun Park; Jun-Ho Huh; Chun-Bo Sim. A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model. Electronics 2020, 9, 1979 .

AMA Style

Se-Hoon Jung, Jun-Yeong Kim, Jun Park, Jun-Ho Huh, Chun-Bo Sim. A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model. Electronics. 2020; 9 (11):1979.

Chicago/Turabian Style

Se-Hoon Jung; Jun-Yeong Kim; Jun Park; Jun-Ho Huh; Chun-Bo Sim. 2020. "A Study on Acer Mono Sap Integration Management System Based on Energy Harvesting Electric Device and Sap Big Data Analysis Model." Electronics 9, no. 11: 1979.

Journal article
Published: 17 August 2020 in Entropy
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Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

ACS Style

Se-Hoon Jung; Hansung Lee; Jun-Ho Huh. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy 2020, 22, 902 .

AMA Style

Se-Hoon Jung, Hansung Lee, Jun-Ho Huh. A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost. Entropy. 2020; 22 (8):902.

Chicago/Turabian Style

Se-Hoon Jung; Hansung Lee; Jun-Ho Huh. 2020. "A Novel Model on Reinforce K-Means Using Location Division Model and Outlier of Initial Value for Lowering Data Cost." Entropy 22, no. 8: 902.

Journal article
Published: 31 January 2020 in Sensors
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Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

ACS Style

Hansung Lee; So-Hee Park; Jang-Hee Yoo; Se-Hoon Jung; Jun-Ho Huh. Face Recognition at a Distance for a Stand-Alone Access Control System †. Sensors 2020, 20, 785 .

AMA Style

Hansung Lee, So-Hee Park, Jang-Hee Yoo, Se-Hoon Jung, Jun-Ho Huh. Face Recognition at a Distance for a Stand-Alone Access Control System †. Sensors. 2020; 20 (3):785.

Chicago/Turabian Style

Hansung Lee; So-Hee Park; Jang-Hee Yoo; Se-Hoon Jung; Jun-Ho Huh. 2020. "Face Recognition at a Distance for a Stand-Alone Access Control System †." Sensors 20, no. 3: 785.

Journal article
Published: 26 June 2019 in Sustainability
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This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

ACS Style

Se-Hoon Jung; Jun-Ho Huh. A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL. Sustainability 2019, 11, 3499 .

AMA Style

Se-Hoon Jung, Jun-Ho Huh. A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL. Sustainability. 2019; 11 (13):3499.

Chicago/Turabian Style

Se-Hoon Jung; Jun-Ho Huh. 2019. "A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL." Sustainability 11, no. 13: 3499.