• IMMUNE NETWORK
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• v.23 no.2
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• pp.18.1-18.15
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• 2023

It has been reported that some exercise could enhance the anti-viral antibody titers after vaccination including influenza and coronavirus disease 2019 vaccines. We developed SAT-008, a novel digital device, consists of physical activities and activities related to the autonomic nervous system. We assessed the feasibility of SAT-008 to boost host immunity after an influenza vaccination by a randomized, open-label, and controlled study on adults administered influenza vaccines in the previous year. Among 32 participants, the SAT-008 showed a significant increase in the anti-influenza antibody titers assessed by hemagglutination-inhibition test against antigen subtype B Yamagata lineage after 4 wk of vaccination and subtype B Victoria lineage after 12 wk (p<0.05). There was no difference in the antibody titers against subtype "A." The SAT-008 also showed significant increase in the plasma cytokine levels of IL-10, IL-1β, and IL-6 at weeks 4 and 12 after the vaccination (p<0.05). A new approach using the digital device may boost host immunity against virus via vaccine adjuvant-like effects.

• Journal of Power Electronics
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• v.12 no.1
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• pp.172-180
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• 2012

Z-source inverters (ZSI) are used to realize both DC voltage boost and DC-AC inversion in single stage with a reduced number of power switching devices. A traditional MPPT control algorithm provides a shoot-through interval which should be inserted in the switching waveforms of the inverter to output the maximum power to the Z-network. At this instant, the voltage across the Z-source capacitor is equal to the output voltage of a PV array at the maximum power point (MPP). The control of the Z-source capacitor voltage beyond the MPP voltage of a PV array is not facilitated in traditional MPPT algorithms. This paper presents a unified MPPT control algorithm to simultaneously achieve MPPT as well as Z-source capacitor voltage control. Development and implementation of the proposed algorithm and a comparison with traditional results are discussed. The effectiveness of the proposed unified MPPT control strategy is implemented in Matlab/Simulink software and verified by experimental results.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.2
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• pp.149-162
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• 2007

In the era of the internet and ubiquitous computing, IS users are still facing a variety of threats. Therefore, a need of more tightened information security service increases unprecedentedly. In this sense, this study is aimed at proposing a new research model in which IT assets (i.e., network, system, and information influence) Security and Information Security Service (i.e., confidentiality, integrity, nonrepudiation, authentication) affect information security quality positively, leading to users' satisfaction eventually. To prove the validity of the proposed research model, PLS analysis is applied with valid 177 questionnaires. Results reveal that both IT assets Security and Information Security Service influence informations security qualify positively, and user satisfaction as well. From the results, it can be concluded that Korean government's recent orchestrated efforts to boost the IT assets Security and Information Security Service helped great improve the information security quality and user satisfaction.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1320-1327
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• 2010

This study is designed to provide a facilitation program of the railroad tourist business that the bicycle is connected with the railroad. The sustainable growth will be achieved thorough the green growth being able to coexist with environment. To deal with the transportation problems -the environmental pollution caused by a large number of cars and the energy depletion, the traffic congestion- in our society is the prerequisites for the green growth. There is the need focusing on the expansion of the green network and the policy implementation of utilizing the bicycle, the formulation of the social consensus for environmental preservation. This provides the opportunity that we create the customer values and reinforce the firm's competitiveness. This study is to propose the plan for accelerating the tourism business connected to the bicycle, and to contribute to the government policy, to boost the green image of the railroad. Approaching in the strategic way to increase the customer's value and environmental preservation, this study will contribute to providing high quality customer service for both of the business performance and the customer satisfaction.

• Journal of Power Electronics
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• v.14 no.3
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• pp.458-467
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• 2014

In this paper, a single-phase current-source bidirectional converter topology for V2G applications is proposed. The proposed converter consists of a single-phase current-source rectifier (SCSR) and an auxiliary switching network (ASN). It offers bidirectional power flow between the battery and the grid in the buck or boost mode and expands the output voltage range, so that it can be compatible with different voltage levels. The topology structure and operating principles of the proposed converter are analyzed in detail. An indirect control algorithm is used to realize the charging and discharging of the battery. Finally, the semiconductor losses and system efficiency are analyzed. Simulation and experimental results demonstrate the validity and effectiveness of the proposed topology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.676-691
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• 2022

An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.494-503
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• 2011

In this paper, we propose a recognition application of facial expression for laughter theraphy on smartphone. It detects face region by using AdaBoost face detection algorithm from the front camera image of a smartphone. After detecting the face image, it detects the lip region from the detected face image. From the next frame, it doesn't detect the face image but tracks the lip region which were detected in the previous frame by using the three step block matching algorithm. The size of the detected lip image varies according to the distance between camera and user. So, it scales the detected lip image with a fixed size. After that, it minimizes the effect of illumination variation by applying the bilateral symmetry and histogram matching illumination normalization. After that, it computes lip eigen vector by using PCA(Principal Component Analysis) and recognizes laughter expression by using a multilayer perceptron artificial network. The experiment results show that the proposed method could deal with 16.7 frame/s and the proposed illumination normalization method could reduce the variations of illumination better than the existing methods for better recognition performance.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.443-454
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• 2017

A positive destination image has an impact on the tourist arrivals and economic growth of the tourist destination. Recently, the content generated by sharing tourist experiences and destination information on the internet has been increasing. The online content has the potential to become a major tourist decision source and provide more in-depth materials and richer content to extract destination image, insight and tourist's perceptions of the destination. This study was designed to explore the destination image of Daegu online and draw lessons for successful image management in an era of big data. Text mining approach and social network analysis were conducted to extract destination image determining elements and assess the influence of the elements. The result showed that destination image elements related to tourist infra-structures and culture, history and art affected the overall destination image of Daegu. Destination marketers should make an effort to grasp these precise destination image and seek ways to boost competitiveness as a tourist destination.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.