• Journal of Digital Convergence
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• v.17 no.2
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• pp.389-397
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• 2019

The purpose of this study is to present guidelines for service design for efficient operation of Maker Space due to the proliferation of Maker Movement. First, we investigate the literature survey and prior research on the definition and status of Maker space. Second, we performed video ethnography, participant observation and in-depth interview on maker space service by qualitative survey method. Third, we analyze the surveyed contents and present guidelines such as Persona or Blueprint for maker space. We classify Maker space into general lab and professional lab, derive Persona from each Maker space, and establish Blueprint to provide guidelines for design and operation. It is expected that the Maker space service design presented in this study can be used as a guideline to help the service improvement of the existing Maker space and the planning, design and operation of the new Maker space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.20-25
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• 2019

Most multimedia signals contain image data, so the problem of efficient processing and transmitting the image data is an important task of the information society. This paper proposes a compression algorithm that reduces the color bits according to importance using YUV color space among the various methods of compressing image data. 4: 2: 2 subsampling is the standard in the field of video. Using the color information and the characteristics of the human retina, YUV color data was reduced by 4: 2: 2 subsampling. The YUV images and RGB images can be interconverted using the transformation matrix. The image data was converted into color space by YUV, and the relatively low U and V bits were subjected to a downscaling operation. The data was then compressed through 4: 2: 2 subsampling. The performance of the proposed algorithm was compared and analyzed by a comparison with existing methods. As a result of the analysis, it was possible to compress the image without reducing the information of the low importance color element and without significant deterioration in the quality compared to the original.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.309-316
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• 2018

Objectives: Both the valence and arousal components of affect are important considerations when managing mental healthcare because they are associated with affective and physiological responses. Research on arousal and valence analysis, which uses images, texts, and physiological signals that employ deep learning, is actively underway; research investigating how to improve the recognition rate is needed. The goal of this research was to design a deep learning framework and model to classify arousal and valence, indicating positive and negative degrees of emotion as high or low. Methods: The proposed arousal and valence classification model to analyze the affective state was tested using data from 40 channels provided by a dataset for emotion analysis using electrocardiography (EEG), physiological, and video signals (the DEAP dataset). Experiments were based on 10 selected featured central and peripheral nervous system data points, using long short-term memory (LSTM) as a deep learning method. Results: The arousal and valence were classified and visualized on a two-dimensional coordinate plane. Profiles were designed depending on the number of hidden layers, nodes, and hyperparameters according to the error rate. The experimental results show an arousal and valence classification model accuracy of 74.65 and 78%, respectively. The proposed model performed better than previous other models. Conclusions: The proposed model appears to be effective in analyzing arousal and valence; specifically, it is expected that affective analysis using physiological signals based on LSTM will be possible without manual feature extraction. In a future study, the classification model will be adopted in mental healthcare management systems.

• Journal of Korean Academy of Medicine & Therapy Science
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• v.10 no.2
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• pp.47-57
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• 2018

Objective: The purpose of this study was to compare the effects of exercise program combined with spaced retrieval and exercise program to show the effects on elderly people with dementia by presenting them to clinics and welfare facilities such as long-term care facilities. Method: This study was conducted in 20 elderly patients with dementia and randomly assigned to exercise program combined with spaced retrieval and exercise program. After screening the subjects for compliance with the criteria, Before starting the experiment, activites of daily living, depression, and nitive evaluation were performed. After 8 weeks, 3 times per week, 40 minutes per intervention, and 4 and 8 weeks, respectively K-MBI, GDSSF-K and MMSE-K were used to evaluate the differences between the experimental and control groups. Results: There was no statistically significant difference in the daily activities, depression, and cognitive scores between the groups of exercise program combined with spaced retrieval and exercise program group. However, there was a significant difference between the two groups after training (p<.05). Particularly, there was statistically significant difference in post-training cognitive evaluation (MMSE-K) only in the exercise program combined with spaced retrieval group (p<.05) Conclusion: This study suggests that exercise program combined with spaced retrieval is more effective in improving cognitive ability. This suggests that the exercise program combined with spaced retrieval is more effective.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.1-9
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• 2018

In the fourth industrial revolution, which has become an issue now, we have been able to receive instant analysis results faster than the existing slow speed through various Big Data technologies, and to conduct real-time monitoring on mobile and web. First, various irregular sensor Data is generated using IoT device, Raspberry Pi. Sensor Data is collected in real time, and the collected data is distributed and stored using several nodes. Then, the stored Sensor Data is processed and refined. Visualize and output the analysis result after analysis. By using these methods, we can train the human resources required for Big Data and mobile related fields using IoT, and process data efficiently and quickly. We also provide information that can confirm the reliability of research results through real time monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1104-1109
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• 2019

Business utilization of holograms is widely used as a similar hologram. The use of holograms has been proposed in many cases. In this paper, we present an outline of similar holograms using up to 3 or 4 facets, and express the similar holograms using the results produced by 3D modeling for a building from dealing with the representation of buildings from hololens to pseudo-hologram by using 3D modeling results. In addition, to reflect the real image of the disadvantage of modeling, we propose a method to enhance the 3D expression of the object by reflecting the actual building surface on the 3D model through photographing. Virtual building seen by the human eye can be virtually shown in space through a hologram among various methods shown in a virtual space such as AR / VR / MR. Through this study, it will be possible to express holograms of various materials such as buildings or cultural properties with enhanced realism.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.471-479
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• 2018

This research examines the sustainable mutual growth of academia and industry regarding human resource cultivation and recruitment of local communities. at the beginning of regularized survival competitions and university innovations according to University Basic Competence Evaluations and etc., This research considers the substantive effect of educational resources of universities on recruitment rates in the pursuit of enhancing university-industry cooperation. Therefore, to identify factors of recruitment rates, we employ a university-wise index based on a quantitative index of educational resources of universities. Regarding study methods, set-up and verification of hypothesis, empirical analysis, descriptive statistics analysis, and correlation analysis are used to identify the correlation between dependent variables and independent variables based on the three sub-indexes of open records at Higher Education including educational environments, educational finances, and research achievements. Implications were derived from multiple regression analysis results regarding education conditions and recruitment rates, educational finances and recruitment rates, and research achievement and recruitment rates. This research can be extended to predict regional university recruitment rates with empirical analysis considering regional characteristics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.7-12
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• 2019

Clouds in the atmosphere are important variables that affect the temperature change by reflecting the radiant energy of the earth surface as well as changing the amount of sunshine by reflecting the sun's radiation energy. Especially, the amount of sunshine on the surface is very important It is essential information. Therefore, eye-observations of the sky on the surface of the earth have been enhanced by satellite photographs or relatively narrowed observation equipments. Therefore, cloud automatic observing systems have been developed in order to replace the human observers, but depending on the seasons, the reliability of observations is not high enough to be applied in the field due to pollutants or fog in the atmosphere. Therefore, we have developed a cloud observation algorithm that is robust against smog and fog. It is based on the calculation of the degree of aerosol from the all-sky image, and is added to the developed cloud reader to develop season- and climate-insensitive algorithms to improve reliability. The result compared to existing cloud readers and the result of cloud cover is improved.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.133-145
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• 2018

Medical systems incurred accidents may result in significant damage for human being. Therefore, performing hazard analysis is important for medical system which is to identify hazard for preventing the accidents and minimizing the potential harm. Hazard analysis that is applied medical systems are difficult to apposite selected, because difference of analysis methods and applied development lifecycle is caused by objective of hazard analysis. It is required to select appropriate hazard analysis at concept phase during development lifecycle, owing to basic requirement elicitation to mitigate or prevent hazard based on identified hazard at concept phase. In this paper, hazard analysis methods, PHA and STPA, are compared at concept phase in which both methods have been applied on the medical system. As a result of compared methods, hazard analyst can be selected optimized hazard analysis methods for concept phase of the medical systems.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.71-78
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• 2021

Most neural-network-based speech synthesis models utilize neural vocoders to convert mel-scaled spectrograms into high-quality, human-like voices. However, neural vocoders combined with mel-scaled spectrogram prediction models demand considerable computer memory and time during the training phase and are subject to slow inference speeds in an environment where GPU is not used. This problem does not arise in linear spectrogram prediction models, as they do not use neural vocoders, but these models suffer from low voice quality. As a solution, this paper proposes a Tacotron 2 and Transformer-based linear spectrogram prediction model that produces high-quality speech and does not use neural vocoders. Experiments suggest that this model can serve as the foundation of a high-quality text-to-speech model with fast inference speed.