• Journal of People, Plants, and Environment
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• v.21 no.6
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• pp.557-564
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• 2018

Along with the aging society, the prevalence of dementia is also increasing. Dementia causes short-term memory loss as well as difficulties of performing daily activities and gradually causes suffering of the patients and their family. In spite of various programs for prevention of dementia of older people are being implemented, there is a lack of developing natural-based program for physical and mental health promotion. Therefore, it is necessary to develop programs for the elderly living alone who are more vulnerable to dementia because of their social and economic isolation. The purpose of this study was to develop a natural-based program and investigate the effects of 10 weeks forest therapy program for dementia prevention to improve the psychological and physical health of the elderly living alone. The experimental subjects were 30 elderly (aged 65 or older) and 31 elderly participated in control group. The Stress response, depressive symptoms, weight, body mass index (BMI), fat mass and muscle mass were measured for pre and post test. The results showed that the experimental group showed subjective stress relief (t=5.249, p=.000), improvement in symptoms of depression (t=4.152, p=.000), and decreases in weight (t=2.686, p=.012), BMI (t=2.629, p=.014) and fat mass (t=2.918, p=.007) after the forest therapy program. The experimental group showed lower stress reactions(t=-7.185, p=.000) and less depressive symptoms (t=-5.303, p=.000) than control group after participating the program. These results suggest that periodic forest exposure can help having less stressful and depressive status than non-forest exposure and the forest therapy program can reduce participants' psychological and physical risk factors of dementia.

• Phonetics and Speech Sciences
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• v.11 no.2
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• pp.57-64
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• 2019

In this paper, we construct a Korean text-to-speech system using the Merlin toolkit which is an open source system for speech synthesis. In the text-to-speech system, the HMM-based statistical parametric speech synthesis method is widely used, but it is known that the quality of synthesized speech is degraded due to limitations of the acoustic modeling scheme that includes context factors. In this paper, we propose an acoustic modeling architecture that uses deep neural network technique, which shows excellent performance in various fields. Fully connected deep feedforward neural network (DNN), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional LSTM (BLSTM) are included in the architecture. Experimental results have shown that the performance is improved by including sequence modeling in the architecture, and the architecture with LSTM or BLSTM shows the best performance. It has been also found that inclusion of delta and delta-delta components in the acoustic feature parameters is advantageous for performance improvement.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.165-178
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• 2019

In an environment transformed by smart factories, measuring the current level of the manufacturing system, deriving improvement targets and tasks and increasing the level of manufacturing competitiveness become the basic activities of the company. However, research on the component analysis and maturity assessment to ensure the future competitiveness of the company is in progress and in the early stages. This study analyzed the existing research on various models, development process, and framework for manufacturing system. In addition, we designed a structural model by deriving the components of future manufacturing system through smart factory related maturity assessment studies. We designed a meta-model that includes an assesment model and a transformation model, and derived the framework development process to propose an integrated framework for the maturity assessment of the future manufacturing system. We verified it by applying it into an actual evaluation project of smart factory.

• The Korean Journal of Archival Studies
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• no.30
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• pp.125-174
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• 2011

Railway Station in Korea built for purpose to plunder resource and invade China 19 and 20 century early, It functioned as main transportation and important role in local history. However, Since the late 1990s, Railway Station in Korea is facing major changes due to Railroad Improvement and Restructuring plan. Photography is useful way to represent memory and local history about railway station, but Photography Documentation on railroad station were not discussed until now due to indifference of the KORAIL and peculiarities of national security facilities for a long time. This study suggest what is document activities and facilities of railway station, use value analysis about railway station's value firstly. next, this study set each documentation area with value analysis for a basis and present concrete example. finally, this study adapt documentation area and detail to classification scheme and apply activities and facilities of supplement or revision to classification scheme.

• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.160-167
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• 2019

Depression is a major mood disorder. Abnormal expression of glial glutamate transporter-1 (GLT-1) is associated with depression. Schisantherin B (STB) is one bioactive of lignans isolated from Schisandra chinensis (Turcz.) Baill which has been commonly used as a traditional herbal medicine for thousands of years. This paper was designed to investigate the effects of STB on depressive mice induced by forced swimming test (FST). Additionally, we also assessed the impairment of FST on cognitive function in mice with different ages. FST and open field test (OFT) were used for assessing depressive symptoms, and Y-maze was used for evaluating cognition processes. Our study showed that STB acting as an antidepressant, which increased GLT-1 levels by promoting PI3K/AKT/mTOR pathway. Although the damage is reversible, short-term learning and memory impairment caused by FST test is more serious in the aged mice, and STB also exerts cognition improvement ability in the meanwhile. Our findings suggested that STB might be a promising therapeutic agent of depression by regulating the GLT-1 restoration as well as activating PI3K/AKT/mTOR pathway.

• 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.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.249-258
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• 2021

Bitcoin prices have been soaring recently as investors flock to cryptocurrency exchanges. The purpose of this study is to predict the Bitcoin price using a deep learning model and analyze whether Bitcoin is profitable through investment strategy. LSTM is utilized as Bitcoin prediction model with nonlinearity and long-term memory and the profitability of MA cross-over strategy with predicted prices as input variables is analyzed. Investment performance of Bitcoin strategy using LSTM forecast prices from 2013 to 2021 showed return improvement of 5.5% and 46% more than market price MA cross-over strategy and benchmark Buy & Hold strategy, respectively. The results of this study, which expanded to recent data, supported the inefficiency of the cryptocurrency market, as did previous studies, and showed the feasibility of using the deep learning model for Bitcoin investors. In future research, it is necessary to develop optimal prediction models and improve the profitability of Bitcoin investment strategies through performance comparison of various deep learning models.

• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.216-225
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• 2020

This study was conducted to qualitatively analyze the selected research through a systematic review to find out application method, outcome measures, and intervention effects of dual task. We searched for published studies from January 2010 to December 2019. Electrical database were PubMed and ProQuest. Search terms were 'dual task' OR 'multi modal' AND 'mild cognitive impairment' OR 'dementia' OR 'Alzheimer's disease'AND 'intervention' OR 'rehabilitation. There were 8 studies selected finally. The dual task was applied not as a single intervention but as a combined intervention with other exercises. The contents of dual task were consisted of motor and cognitive tasks to be independent each other. The outcome measures included general cognitive function such as MMSE and CERAD, executive function, and memory. Additionally the dual task cost was also used to identify the direct improvement of the dual task. This study could provide informations of dual task application on elderly with cognitive impairment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.203-210
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• 2020

Memory-based collaborative filtering is one of the representative types of the recommender system, but it suffers from the inherent problem of data sparsity. Although many works have been devoted to solving this problem, there is still a request for more systematic approaches to the problem. This study exploits distribution of user ratings given to items for computing similarity. All user ratings are utilized in the proposed method, compared to previous ones which use ratings for only common items between users. Moreover, for similarity computation, it takes a global view of ratings for items by reflecting other users' ratings for that item. Performance is evaluated through experiments and compared to that of other relevant methods. The results reveal that the proposed demonstrates superior performance in prediction and rank accuracies. This improvement in prediction accuracy is as high as 2.6 times more than that achieved by the state-of-the-art method over the traditional similarity measures.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1243-1252
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• 2012

Due to the widespread use of smart devices, threats of direct observation attacks such as shoulder surfing and recording attacks, by which user secrets can be stolen at user interfaces, are increasing greatly. Although formal security models are necessary to evaluate the possibility of and security against those attacks, such a model does not exist. In this paper, based on the previous work in which a HCI cognitive model was firstly utilized for analyzing security, we propose STM-GOMS model as an improvement of GOMS-based model with regard to memory limitations. We then apply STM-GOMS model for analyzing usability and security of a password entry scheme commonly used in smart devices and show the scheme is vulnerable to the shoulder-surfing attack. We finally conduct user experiments to show the results that support the validity of STM-GOMS modeling and analysis.