• Journal of Korean Academy of Nursing
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• v.41 no.2
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• pp.182-190
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• 2011

Purpose: This study was done to identify correlates and variables predicting daily activity among elders with Diabetes Mellitus (DM). Methods: Seventy-six elders registered in the Department of Endocrine Medicine at C university hospital participated in data collection. Data on daily activity and biochemical variables were collected via actigraph accelerator (Actical) and blood tests between September 2009 and July 2010. Data analysis was done using SPSS WIN 15.0 program and included one-way ANOVA, independent t-test, Pearson correlation coefficients, and stepwise multiple regression. Results: This study showed a positive correlation between daily activity and High Density Lipoprotein Cholesterol (HDL-C) and a negative correlation among Total Cholesterol (TC), Triglyceride (TG), and Low Density Lipoprotein Cholesterol (LDL-C). The variables predicting daily activity were frequency of exercise, HDL-C, and TC. These factors accounted for 40.0% of the variance of daily activity in elders with DM. Conclusion: The results indicate that it is necessary to improve daily activity to reduce Fasting Blood Glucose (FBG), TC, and TG in elders with DM.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.737-745
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• 2021

This study aims to determine whether there are differences in the average abnormal return, trading volume activity, and trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of the coronavirus (COVID-19) in Indonesia. The sample was selected using a purposive sampling method and collected as many as nine pharmaceutical companies listed on the Indonesia Stock Exchange during 2019-2020. The data used in this study were secondary data in the form of daily data on stock closing prices, Composite Stock Price Index (IHSG), stock volume trading, number of shares outstanding, and stock trading frequency. This study was an event study with an observation period of 14 days, namely seven days before and seven days after the announcement of the coronavirus's first positive case in Indonesia. Hypothesis testing employed the paired sample t-test method. Based on the results, it was found that there was no difference in the average abnormal return of pharmaceutical stocks before and after the announcement of the first case of COVID-19. However, there was a difference in the average trading volume activity and the average trading frequency activity in pharmaceutical stocks before and after the announcement of the first case of COVID-19.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.185-194
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• 2000

In this work we measured the total pressure of the aqueous solutions and the methanol-water solutions dissolved with inorganic salts, at $25^{\circ}C$. In organic electrolytes used in this work were $K_2SO_4$ and $(NH_4)_2SO_4$. Using the measured vapour pressures the activity coefficient of solvents and the mean ionic activity coefficient were obtained through thermodynamic relations. The activity coefficients of solvent and the mean ionic activity coefficirnt obtained in this work were fitted with Macedo's model and Acard's model. Both two models were good agreeable to the vapor pressure and the mean ionic activity coefficient for the electroyte aqueous solutions. For electrolyte /methanol/water solutions, Macedo's model had much deviation from experimental data, while Acard's model showed a good agreement with experimental data.

• Korean Journal of Adult Nursing
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• v.20 no.4
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• pp.626-640
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• 2008

Purpose: The aim of this study was to construct leisure physical activity model of middle-aged women in urban area. Methods: Data were gathered by self-report questionnaire from 211 women aged between 41 and 59 years in urban community. The data were analyzed using the SPSS/WIN 10.0 program and the model was constructed using the LISREL 8.54 program. Results: Variables that have direct effects on leisure physical activity were health state, past leisure physical activity, social support, self-efficacy, and affect. Perceived leisure state and behavioral leisure attitude also influenced leisure physical activity in an indirect way. Perceived leisure state had a direct effect on self-efficacy. Behavioral leisure attitude, past leisure physical activity, and experience of exercise effect had significantly direct effects on affect Conclusion: It will provide basic information for developing strategies of programs to enhance leisure physical activity of middle-aged women in urban area.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.18 no.1
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• pp.97-106
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• 2011

Purpose: The purpose of this study was to identify social support and health status based on characteristics of leisure activity in middle-aged women. Methods: Participants were 148 middle-aged women living in the capital area. Data were collected through self-report questionnaires which were constructed to include leisure activity characteristics, social support and Brodman's CMI. Data were analyzed using t-test, and ANOVA, with SPSS/WIN 14.0. Results: Social support was different depending on leisure type, leisure partner, length of participation in present activity, regularity, and motivation to start activity. Health status was different depending on the length of participation in present activity, and regularity. Conclusion: Because social support and health status depend on characteristics of leisure activity, further study in nursing one how to resolve the physical, psychological, social and health problems that middle-aged women might experience through various leisure activities.

• The Korean Journal of Pesticide Science
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• v.9 no.2
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• pp.140-145
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• 2005

Quantitative Structure Activity Relationship (QSAR) assumes the relatedness between physical property and biological activity. However, activity data measured at single concentration such as percent activity have not been used extensively for modeling purpose. This probably comes from the fact that these values are qualitative instead of quantitative. To utilize percent activity data for molecular modeling, we classified the whole data into two classes. One class represents the active while the other signifies the inactive. The percent activity data of ${\beta}$-Ketoacetoanilides measured for TLB (Tomato Late Blight) were investigated. CoMFA (Comparative Molecular Field Analysis) was used as a discriminant function. Using CoMFA provides 3D (three dimensional) information, which is crucial for chemical insight. It can also serve as a predictive model. The resultant model classified the given data correctly (98%). When LOO (leave-one-out) crossvalidation procedure was applied, the classification accuracy was 69%. Therefore two class approximation of percent activity data with CoMFA can be utilized to understand the relationship between chemical structure and biological activity and design subsequent chemical analogs.

• The Journal of Bigdata
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• v.1 no.2
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• pp.1-8
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• 2016

User activity log includes lots of hidden information, however it is not structured and too massive to process data, so there are lots of parts uncovered yet. Especially, it includes time series data. We can reveal lots of parts using it. But we cannot use log data directly to analyze users' behaviors. In order to analyze user activity model, it needs transformation process through extra framework. Due to these things, we need to figure out user activity model analysis framework first and access to data. In this paper, we suggest a novel framework model in order to analyze user activity model effectively. This model includes MapReduce process for analyzing massive data quickly in the distributed environment and data architecture design for analyzing user activity model. Also we explained data model in detail based on real online service log design. Through this process, we describe which analysis model is fit for specific data model. It raises understanding of processing massive log and designing analysis model.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.90-96
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• 2023

Securing large amounts of training data in deep learning neural networks, including convolutional neural networks, is of importance for avoiding overfitting phenomenon or for the excellent performance. However, securing labeled training data in deep learning neural networks is very limited in reality. To overcome this, several augmentation methods have been proposed in the literature to generate an additional large amount of training data through transformation or manipulation of the already acquired traing data. However, unlike training data such as images and texts, it is barely to find an augmentation method in the literature that additionally generates bio-signal training data for convolutional neural network based human activity recognition. Thus, this study proposes a simple but effective augmentation method of bio-signal training data for convolutional neural network based human activity recognition. The usefulness of the proposed augmentation method is validated by showing that human activity is recognized with high accuracy by convolutional neural network trained with its augmented bio-signal training data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2336-2344
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• 2008

In this paper, muscular activity and muscle fatigue of FDI(first dorsal interosseous muscle) and thenar muscle of hand was analyzed with surface EMG signal based on four kinds of attitudes(grip, tip, key and palmar) to measure grip strength and pinch strength after hand operation and rehabilitation treatment. The normative data are needed to interpret evaluation data to assess a patient's ability to return to labor environment. The preceding researchers proposed the standard data only by studying on maximum grip strength and the maximum pinch strength followed by each attitude of subjects' hands. But in this study, the muscle activity and muscle fatigue were considered under the various attitude to propose normative data. As a results, the muscle fatigue may be used only for presentation of normative data in labor environment.