• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Biomolecules & Therapeutics
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• v.27 no.3
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• pp.283-289
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• 2019

Brain aging induces neuropsychological changes, such as decreased memory capacity, language ability, and attention; and is also associated with neurodegenerative diseases. However, most of the studies on brain aging are focused on neurons, while senescence in astrocytes has received less attention. Astrocytes constitute the majority of cell types in the brain and perform various functions in the brain such as supporting brain structures, regulating blood-brain barrier permeability, transmitter uptake and regulation, and immunity modulation. Recent studies have shown that SIRT1 and SIRT2 play certain roles in cellular senescence in peripheral systems. Both SIRT1 and SIRT2 inhibitors delay tumor growth in vivo without significant general toxicity. In this study, we investigated the role of tenovin-1, an inhibitor of SIRT1 and SIRT2, on rat primary astrocytes where we observed senescence and other functional changes. Cellular senescence usually is characterized by irreversible cell cycle arrest and induces senescence- associated ${\beta}$-galactosidase (SA-${\beta}$-gal) activity. Tenovin-1-treated astrocytes showed increased SA-${\beta}$-gal-positive cell number, senescence-associated secretory phenotypes, including IL-6 and IL-$1{\beta}$, and cell cycle-related proteins like phospho-histone H3 and CDK2. Along with the molecular changes, tenovin-1 impaired the wound-healing activity of cultured primary astrocytes. These data suggest that tenovin-1 can induce cellular senescence in astrocytes possibly by inhibiting SIRT1 and SIRT2, which may play particular roles in brain aging and neurodegenerative conditions.

• 한국노년학
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• v.30 no.1
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• pp.81-91
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• 2010

The purpose of this study was to test a validity and reliability of Cognitive Performance Scale(CPS), a cognitive measure generated from 5 items(comatose status, decision making, short-term memory, making self understood, and eating). Method: 393 patients in 2 hospitals for the elderly with dementia were measured with CPS by two nurses independently. The inter-rater agreement was tested by comparing two scores. The CPS score was compared with GDS, which was measured by doctors and nurses, and MMSE score which was drawn from the claim data of Health Insurance Review & Assessment Service. Result: The correlation coefficient between CPS and GDS was 0.742(p<0.0001), CPS and MMSE was -0.794(p<0.0001). The Cronbach's coefficient alpha of CPS was 0.742, Kappa value was 0.772~1.000. The CPS showed high validity and reliability in long term care hospitals of Korea.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.492-500
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• 2018

This study examined the effects of a dual-task virtual reality program on the cognitive function and EEG for patients with mild cognitive impairment. A dual-task virtual reality program was performed in the experimental groups while conventional occupational therapy was carried out in the control group for 30 minutes per session, which was done five days per week for 6 weeks. The results were as follows. First, the memory of the cognitive function and balance was improved significantly in the experimental group with the dual-task virtual reality program compared to the control group with the traditional occupational therapy. Second, EEG was also increased significantly in the experimental group compared to the control group. The results of this study suggest that the dual-task virtual reality program was an effective treatment method for the elderly with mild cognitive impairment and would be a cornerstone of basic data that will be helpful to those suffering from a range of diseases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.401-404
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• 2016

This paper proposes entropy coding method of HEVC CABAC Encoder for efficient hardware architecture. The Binary Arithmetic Encoder requires data dependency at each step, which is difficult to be operated in a fast. Proposed Binary Arithmetic Encoder is designed 4 stage pipeline to quickly process the input value bin. According to bin approach, either MPS or LPS is selected and the binary arithmetic encoding is performed. Critical path caused by repeated operation is reduced by using the LUT and designed as a shift operation which decreases hardware size and not using memory. The proposed Binary Arithmetic Encoder of CABAC is designed using Verilog-HDL and it was implemented in 65nm technology. Its gate count is 3.17k and operating speed is 1.53GHz.

• 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 Science Education
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• v.45 no.1
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• pp.129-141
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• 2021

With the change of the undergraduate medical education system, many medical schools have recently run or developed a medical education curriculum. The premedical curriculum should be designed according to the sequencing and level of the medical curriculum, but there were no discussions on the standards or evidence for the basic science-related subjects. Therefore, this study examines Physics I, Physics II, Life sciences I, Life sciences II, Chemistry I, and Chemistry II, which are the subjects of need assessment exploration. The need assessment used mean, mean difference, and Borich demand, The locus for focus of memory degree and importance, and the result was converted into 76 keywords. The results of this study are expected to be used as basic data for the development of subjects related to basic science in premedical curriculum.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.35-44
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• 2011

The PP-1/64-128 block cipher support variety data block and secret key size. Also, it is suitable for hardware implementation and can much easier to apply Concurrent Error Detection(CED) for cryptographic chips compared to other block ciphers, because it has same encryption and decryption process. In this paper, we proposed truncated differential cryptanalysis of PP-1/64-128. the attack on PP-1/64-128 block cipher requires $2^{50.16}$ chosen plaintexts, $2^{46.16}$ bytes memory spaces and $2^{50.45}$ PP-1/64-128 encryption to retrieve secret key. This is the best result of currently known PP-1/64-128 differential cryptanalysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1159-1168
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• 2020

In the past, control and monitor of a large number of instruments is a specialized area, which requires an expensive dedicated module to implement. However, with the recent development of embedded technology, various products capable of performing M&C (Monitor and Control) have been released, and the scope of application is expanding. Accordingly, it is possible to more easily build a small M&C environment than before. In this paper, we discussed a method to replace the M&C of the VLBI system, which had to be implemented through a specialized hardware product, with an inexpensive general imbeded technology. Memory based data transmission, reception and storage is a technology that is already generalized not only in VLBI but also in the network field, and more effective M&C can be implemented when some items of Ethernet are optimized for the VLBI (Very Long Baseline Interferometer) system environment. In this paper, we discuss in depth the design and implementation for the multidrop based IoT architecture.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.115-123
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• 2021

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.