• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• East Asian mathematical journal
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• v.29 no.2
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• pp.137-168
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• 2013

In this paper we study activity theory and Davydov's learning activity theory. We analyze brief history of activity theory in Russia, structure of human activity, and Davydov's studies in activity theory. Especially we analyze Davydov's 1st grade mathematics textbook, and try to investigate embodiment of Davydov's learning activity theory in his mathematics textbook.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.169-178
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• 2008

In this paper, we propose a video summarization system which is based on activity in video acquired by multiple non-overlapping cameras for wide-area surveillance. The proposed system separates persons by time-independent background removal and detects activities of the segmented persons by their motions. In this paper, we extract eleven activities based on whose direction the persons move to and consider a key-frame as a frame which contains a meaningful activity. The proposed system summarizes based on activity-based key-frames and controls an amount of summarization according to an amount of activities. Thus the system can summarize videos by camera, time, and activity.

• Journal of Pharmaceutical Investigation
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• v.38 no.5
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• pp.289-293
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• 2008

To develop a poloxamer-based solid suppository with poloxamer and polyethylene glycol mixtures, the melting point of various formulations composed of P 188 and propylene glycol were investigated. The dissolution and antitumor activity of clotrimazole delivered by the poloxamer-based suppository were performed. The poloxamer mixtures composed of P 188 and propylene glycol were homogeneous phases. P 188 greatly affected the melting point of poloxamer mixtures. In particular, the poloxamer mixture [P 188/propylene glycol(70/30%)] with the melting point of about $32^{\circ}C$ was a solid form at room temperature and instantly melted at physiological temperature. Furthermore, the ratio of P 188/propylene glycol greatly affected the dissolution rates of clotrimazole from poloxamer-based suppository. It gave the more effective anti-tumor activity than conventional PEG-based suppository due to fast dissolution. Thus, the clotrimazole-Ioaded poloxamer-based solid suppository was an effective rectal dosage form with anti-tumor activity.

• The Journal of the Acoustical Society of Korea
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• v.30 no.6
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• pp.324-329
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• 2011

In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the conditional maximum a posteriori (CMAP) with deviation. In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the speech activity decisions and spectral deviation in the pervious frame. Experimental results show that the proposed approach yields better results compared to the CMAP-based VAD using the LR test.

• Korean Journal of Pharmacognosy
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• v.43 no.4
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• pp.345-351
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• 2012

In this study, 49 Korean herbal medicines have been investigated with an in vitro evaluation system using glycation end products (AGEs) formation inhibitory activity. Of these, 18 herbal medicines ($IC_{50}$ < $50{\mu}g/ml$) were found to have significant AGEs formation inhibitory activity. Of these, five herbal medicines ($IC_{50}$ < $50{\mu}g/ml$) were found to have significant AGEs formation inhibitory activity. Particularly, Mallotus japonicus (twigs and leaves), Rhus javanica (twigs and leaves), Boehmeria nivea (whole plants), Quercus acuta (stems), and Eurya japonica (stems) showed more potent inhibitory activity (approximately 9-37 fold) than the positive control aminoguanidine ($IC_{50}=76.47{\mu}g/ml$).

• Knowledge Management Research
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• v.15 no.2
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• pp.23-41
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• 2014

Firms increasingly utilize Social Networking Service(SNS) to lead user's voluntary behavior. In the web-based environment, users show coexist loyal behavior which is represented by 'web-based pro-organization citizenship behavior' and 'anti-citizenship behavior'. To measure genuine performance of web-activity, we separated degree of compliance based on credibility, 'deep-level' and 'surface-level' to comprehend different behavior after compliance. The analysis result shows that contents credibility is important to enhance deep-level of compliance which has significant influence on web-based pro-organization citizenship behavior. Contrastively, surface-level of compliance has influence on anti-citizenship behavior. Based on the results of these analyses, the directions of web-based activities for the common good and self-interests of the stakeholders of the web-based activities will be proposed.

• Journal of Technology Innovation
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• v.8 no.1
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• pp.173-195
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• 2000

Activity-based costing(ABC) was developed in manufacturing companies. Recently, ABC has been also applied to cost analysis in service industries and government. In this paper, ABC is applied to research institutes, especially to cost management of government-supported research institutes. ABC is an effective tool in reengineering by removing non-value-added activities, costing R&D projects, managing indirect costs and evaluating performance in research institutes. A case of activity-based costing in a government-supported institutes is provided.

• Journal of Korean language education
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• v.25 no.1
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• pp.29-52
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• 2014

The purpose of this paper is to suggest a discourse analysis activity that can be used in the stage after performing tasks in task-based Korean speaking class and show its pedagogical advantages. A discourse analysis activity is an metadiscourse activity in which learners speak what they have spoken. By analyzing discourse and performing tasks again, learners can enhance their fluency and accuracy, make their knowledges in target language more stable and extend them, and develop problem solving skills. Consequently, this facilitates learners' acquisition of Korean language. This paper reviewed theoretical background of proposing discourse analysis activity, suggested the pedagogical advantages of the analysis, and examined discourse analysis activity in Korean speaking class. And it included the discourse sample of learners in actual class.