• Smart Structures and Systems
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• v.21 no.3
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• pp.257-262
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• 2018

Vibrational problems in the domestic Small Horizontal Axis Wind Turbines (SHAWT) are due to flap wise vibrations caused by varying wind velocities acting perpendicular to its blade surface. It has been reported that monitoring the structural health of the turbine blades requires special attention as they are key elements of a wind power generation, and account for 15-20% of the total turbine cost. If this vibration problem is taken care, the SHAWT can be made as commercial success. In this work, Shape Memory Alloy (SMA) wires made of Nitinol (Ni-Ti) alloys are embedded into the Glass Fibre Reinforced Polymer (GFRP) wind turbine blade in order to reduce the flapwise vibrations. Experimental study of Nitinol (Ni-Ti) wire characteristics has been done and relationship between different parameters like current, displacement, time and temperature has been established. When the wind turbine blades are subjected to varying wind velocity, flapwise vibration occurs which has to be controlled continuously, otherwise the blade will be damaged due to the resonance. Therefore, in order to control these flapwise vibrations actively, a non-linear current controller unit was developed and fabricated, which provides actuation force required for active vibration control in smart blade. Experimental analysis was performed on conventional GFRP and smart blade, depicted a 20% increase in natural frequency and 20% reduction in amplitude of vibration. With addition of active vibration control unit, the smart blade showed 61% reduction in amplitude of vibration.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.133-144
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• 2020

The number of elderly people with dementia is increasing as fast as the proportion of older people due to aging, which creates a social and economic burden. In particular, dementia care costs, including indirect costs such as increased care costs due to lost caregiver hours and caregivers, have grown exponentially over the years. In order to reduce these costs, it is urgent to introduce a management system to care for dementia patients. Therefore, this study proposes a sensor-based abnormal behavior detection system to manage dementia patients who live alone or in an environment where they cannot always take care of dementia patients. Existing studies were merely evaluating behavior or evaluating normal behavior, and there were studies that perceived behavior by processing images, not data from sensors. In this study, we recognized the limitation of real data collection and used both the auto-encoder, the unsupervised learning model, and the LSTM, the supervised learning model. Autoencoder, an unsupervised learning model, trained normal behavioral data to learn patterns for normal behavior, and LSTM further refined classification by learning behaviors that could be perceived by sensors. The test results show that each model has about 96% and 98% accuracy and is designed to pass the LSTM model when the autoencoder outlier has more than 3%. The system is expected to effectively manage the elderly and dementia patients who live alone and reduce the cost of caring.

• Journal of Korean Academy of Nursing
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• v.47 no.1
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• pp.1-13
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• 2017

Purpose: The purpose of this study was to examine the effects of a cognitive training program on neurocognitive task performance and activities of daily living (ADL) in patients who had a stroke. Methods: The research design for this study was a nonequivalent control group non-synchronized design. Patients were assigned to the experimental (n=21) or control group (n=21). The experimental group received a 4-week cognitive training program and usual care (i.e., rehabilitation service), while the control was received usual care only. Cognitive function was measured with a standardized neurocognitive test battery and ADL was assessed at baseline and one and two months after completion of the intervention. Repeated measures ANOVA was used to determine changes in cognitive function and ADL over 2 months. Results: The interaction of group and time was significant indicating that the experimental group showed improvement in attention, visuospatial function, verbal memory, and executive function compared to the control group which had a sustained or gradual decrease in test performance. A significant group by time interaction in instrumental ADL was also found between the experimental group with gradual improvement and the control group showing no noticeable change. Conclusion: Findings show that the cognitive training program developed in this study is beneficial in restoring cognitive function and improving ADL in patients following a stroke. Further study is needed to investigate the long-term relationship between cognitive training participation and cognitive improvement and effective functioning in daily living.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.19-28
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• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.

• Journal of Home Health Care Nursing
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• v.30 no.3
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• pp.252-263
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• 2023

Purpose: To assess the feasibility, safety, and preliminary estimates of effectiveness of Tai Chi on the functional outcomes of older adults in the community. Methods: This was a mixed-method study that employed a single-group repeated measure design and in-depth interviews. Nine older adults were recruited from the community were recruited to participate in a Tai Chi program, conducted twice weekly for 6 months. Research outcomes included physical function, cognitive function, and quality of life, measured at intervals of 3 and 6 months. Findings: Tai Chi exercises were gradually conducted based on the health status of the older adults. All participants actively participated in the program with an average attendance of 90%. Consequently, the participants showed significant improvements in mobility and their memory recall ability at both 3 and 6 months. Additionally, the results of the Stroop test exhibited improvement 3 months after the commencement of the study program. Quality of life of the participants improved according to the mild cognitive impairment questionnaire, but it did not show significant improvement in health-related quality of life. Conclusion: The Tai Chi exercise program was a safe and, feasible program to improve the physical function, cognitive function, quality of life among the older adults in the community.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.6
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• pp.597-611
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• 2009

With the advent of ubiquitous computing society, many advanced technologies have enabled wireless sensor networks which consist of small sensor nodes. However, the sensor nodes have limited computing resources such as small size memory, low battery life, short transmission range, and low computational capabilities. Thus, decreasing energy consumption is one of the most significant issues in wireless sensor networks. In addition, numerous applications for wireless sensor networks are recently spreading to various fields (health-care, surveillance, location tracking, unmanned monitoring, nuclear reactor control, crop harvesting control, u-city, building automation etc.). For many of them, supporting security functionalities is an indispensable feature. Especially in case wireless sensor networks should provide a sufficient variety of security functions, sensor nodes are required to have more powerful performance and more energy demanding features. In other words, simultaneously providing security features and saving energy faces a trade-off problem. This paper presents a novel energy-efficient security architecture in an IEEE 802.15.4-based wireless sensor network called the Hybrid Adaptive Security (HAS) framework in order to resolve the trade off issue between security and energy. Moreover, we present a performance analysis based on the experimental results and a real implementation model in order to verify the proposed approach.

• Bulletin of the Korean Chemical Society
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• v.27 no.9
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• pp.1393-1396
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• 2006

The 1+1 two-photon Resonant Enhanced Multiphoton Ionization (REMPI) spectra of 1,4-dichlorobenzene was obtained from 240 nm through to 250 nm on a laser mass spectrometer. Special care was taken to build up a heatable sample inlet system suitable for detecting a trace semi-volatile organic compound and reducing the memory effort on the inner wall of the inlet system. The detection limits of 1,4-dichlorobenzene in ppbV/V concentration range at certain wavelengths are presented.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.129-138
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• 2016

Recently, the number of children who are required to take special care are increasing because of the fast-changing society and the environment factor. Among them, most children in poor family are leading to Children with borderline intelligence, so urgent action are needed to prevent this situation. This study is conducted to establish the reason why children with borderline intelligence are taking place. In addition, this study embodies serious game as a solution which is able to prevent and cure this children with borderline intelligence problem. This game was made to improve weak memory, concentration and judgment of children with borderline intelligence and was verified in effectiveness by thirty people and experts.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.