• Korean Journal of Adult Nursing
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• v.27 no.6
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• pp.707-717
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• 2015

Purpose: This study was to develop and verify the effects of the exercise-cognitive combined dual-task training program on cognitive function and depression of the elderly with mild cognitive impairment (MCI). Methods: A non-equivalent control group pretest-posttest design was used. The participants were assigned into two groups: an experimental group receiving an exercise-cognitive combined dual-task (n=20) and a control group receiving a simple-task (n=18). After 8 weeks of intervention (2 days per week), the change in depression and cognitive functions were compared between the groups. Results: General cognitive function (t=-2.81, p=.011), frontal cognitive function (Z=-3.50, p<.001), attention/working memory function (U=-2.91, p=.004), depression (t=4.96, p<.001) of the experimental group were significantly increased than those of the control group. Conclusion: The findings of the study showed that an exercise-cognitive combined dual-task program for MCI was effective in improving general cognitive function, frontal and executive function, attention/working memory function, and reducing depression.

• Korean Journal of Materials Research
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• v.22 no.8
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• pp.385-389
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• 2012

Significant improvements in the switching voltage distribution are required for the development of unipolar resistive memory devices using $MnO_x$ thin films. The $V_{set}$ of the as-grown $MnO_x$ film ranged from 1 to 6.2 V, whereas the $V_{set}$ of the oxygen-annealed film ranged from 2.3 to 3 V. An excess of oxygen in an $MnO_x$ film leads to an increase in $Mn^{4+}$ content at the $MnO_x$ film surface with a subsequent change in the $Mn^{4+}/Mn^{3+}$ ratio at the surface. This was attributed to the change in $Mn^{4+}/Mn^{3+}$ ratios at the $MnO_x$ surface and to grain growth. Oxygen annealing is a possible solution for improving the switching voltage distribution of $MnO_x$ thin films. In addition, crystalline $MnO_x$ can help stabilize the $V_{set}$ and $V_{reset}$ distribution in memory switching in a Ti/$MnO_x$/Pt structure. The improved uniformity was attributed not only to the change of the crystallinity but also to the redox reaction at the interface between Ti and $MnO_x$.

• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.1-17
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• 2014

In this study, various characteristics of R&D related economic variables were studied to analyze complexity of science and technology activities in Korea, as reliance of R&D activities of the private sector is growing by the day. In comparison to other countries, this means that it is likely to be fluctuated by economic conditions. This complexity characteristic signifies that the result of science and technology activities can be greatly different from the anticipated results - depending on the influences from economic conditions and the results of science and technology activities which may be unpredictable. After reviewing the results of 17 variables related to science and technology characteristics of complex systems intended for time-series data - in the total R&D expenditure, and private R&D expenditure, numbers of SCI papers, the existence of chaotic characteristics were. using Lyapunov Exponent, Hurst Exponent, BDS test. This result reveals science and technology activity of the three most important components in Korea which are; heavy dependence on initial condition, the long term memory of time series, and non-linear structure. As stable R&D investment and result are needed in order to maintain steady development of Korea economy, the R&D structure should be less influenced by business cycles and more effective technology development policy for improving human resource development must be set in motion. And to minimize the risk of new technology, the construction of sophisticated technology forecasting system should take into account, for development of R&D system.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.989-997
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• 2018

Massive event stream brings us great challenges in its volume, velocity, variety, value and veracity. Picking up some valuable information from it often faces with long detection time, high memory consumption and low detection efficiency. Aiming to solve the problems above, an efficient complex event detection method based on NFA_HTS (Nondeterministic Finite Automaton_Hash Table Structure) is proposed in this paper. The achievement of this paper lies that we successfully use NFA_HTS to realize the detection of complex event from massive RFID event stream. Specially, in our scheme, after using NFA to capture the related RFID primitive events, we use HTS to store and process the large matched results, as a result, our scheme can effectively solve the problems above existed in current methods by reducing lots of search, storage and computation operations on the basis of taking advantage of the quick classification and storage technologies of hash table structure. The simulation results show that our proposed NFA_HTS scheme in this paper outperforms some general processing methods in reducing detection time, lowering memory consumption and improving event throughput.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.30-32
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• 2014

최근 낸드 플래시 메모리는 임베디드 저장 장치로 많이 사용되고 있다. 비휘발성인 플래시 메모리는 기존의 하드디스크와 달리 저 전력, 좋은 내충격성 및 집적도 등 많은 장점이 있지만 데이터 업데이트 시 덮어쓰기가 안 되어 쓰기 연산 전 해당 블록을 지우는 작업이 선 진행되어야 하며 이로 인해 부분 페이지 업데이트가 자주 일어난다. 이런 플래시메모리와 더불어 최근 차세대 메모리연구가 많이 진행 중인데, 이 중에서 PCM 이라는 메모리는 비휘발성으로 정전 시 데이터가 날라 가버리는 DRAM에 반해 전원이 공급 안 되더라도 데이터가 보존되는 특성이 있다. 하지만 PCM 역시 플래시 메모리와 마찬가지로 블록 당 쓰기연산 작업이 제한되어 있어서 근래에 DRAM과 같이 사용하는 하이브리드 구조를 채택하여 많은 연구가 진행되고 있다. 따라서 본 논문에서는 플래시 메모리의 문제점을 해결함으로서 수명을 연장시키고 정 전시 데이터가 보존되지 않는 DRAM의 단점을 하이브리드 메모리를 기반으로하여 데이터의 신뢰성을 높이는 FTL알고리즘을 제안한다.

• Electronics and Telecommunications Trends
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• v.34 no.4
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• pp.43-54
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• 2019

Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.297-309
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• 2021

In this study, to capture the skewness and kurtosis detected in both conditional and unconditional return distributions of the stock markets of Kazakhstan and Russia, two versions of normal mixture GARCH models are employed. The data set consists of daily observations of the Kazakhstan and Russia stock prices, and world crude oil price, covering the period from 1 June 2006 through 1 March 2021. From the empirical results, incorporating the long memory effect on the returns not only provides better descriptions of dynamic behaviors of the stock market prices but also plays a significant role in improving a better understanding of the return dynamics. In addition, normal mixture models for time-varying volatility provide a better fit to the conditional densities than the usual GARCH specifications and has an important advantage that the conditional higher moments are time-varying. This implies that the volatility skews implied by normal mixture models are more likely to exhibit the features of risk and the direction of the information flow is regime-dependent. The findings of this study contain useful information for diverse purposes of cross-border stock market players such as asset allocation, portfolio management, risk management, and market regulations.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Journal of agricultural medicine and community health
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• v.45 no.3
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• pp.121-129
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• 2020

Purpose: As dementia progresses, cognitive function decreasing leads to memory loss, speech degradation, time and space degradation and judgment degradation, which causes difficulties in carrying out tasks related to daily life. It was said that community-based non-drug intervention therapy for early dementia patients was important to participate in entertainment treatment, including activities such as awareness and exercise therapy, exercise rehabilitation, aerobic exercise, and art. Methods: This study conducted 15 experimental and 15 control groups(experimental group : Fumanet exercise, control group : general occupational therapy) for eight weeks at the Daycare Center in Gyeonggi-do to find out the impact of the Fumanet exercise program on cognitive and depression functions of the elderly. The pre-post evaluation used KGDS, MMSE. Results: There were significant differences between the two groups in the function of menopause, memory recall, attention concentration and calculation, and depression, and no significant results were obtatined in memory registration, language function, understanding and fracture. The Fumanet movement was judged to be effective in improving cognitive function and reducing depression for the elderly with dementia. Conclisions: The Fumanet movement was judged to be effective in improving cognitive function and reducing depression for the elderly with dementia.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.789-799
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• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.