• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.295-304
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• 2021

Learning assistance system that continuously measures user's concentration will be helpful to grasp the concentration pattern and adjust the learning method accordingly to improve the learning efficiency. Although a lot of various learning aids have been proposed, there have been few studies on the concentration monitoring system in real time. Therefore, in this study, we developed an Android-based mobile application that can measure concentration during study by using functional near-infrared spectroscopy, which is used to measure brain activity. First, the task accuracy was predicted at a maximum level of 93.75% from the prefrontal oxygenation characteristics measured while performing the visual Q&A task on 11 college students, and a concentration calculation formula based on a linear regression model was derived. Then, a survey on the usability of the mobile application was conducted, overall high satisfaction and positive opinions were obtained. From these findings, this application can be used as a customized learning aid application for users, and further, it can help educators improve the quality of classes based on the level of concentration of learners.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.1043-1052
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• 2013

This study was conducted to investigate the correlations between the $PM_{10}$ concentration trend and meteorological elements in the Gimhae region and analyze the transportation routes of air pollutants through back-trajectory analysis. Among the air quality measuring stations in the Gimhae regions, the $PM_{10}$ concentration of the Sambangdong station was higher than that of the Dongsangdong station. Also, an examination of the relationships between $PM_{10}$ concentration and meteorological elements showed that the greater the number of yellow dust occurrence days was and the lower the temperature and precipitation were, the higher the $PM_{10}$ concentration appeared. Furthermore, a cluster analysis through the HYSPLIT model showed that there were 4 clusters of trajectories that flowed into the Gimhae region and most of them originated in China. The meteorological characteristics of the four clusters were analyzed and they were similar to those of the air masses that influence South Korea. These analyses found that meteorological conditions affect the $PM_{10}$ concentration.

• Journal of Environmental Science International
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• v.26 no.1
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• pp.55-65
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• 2017

Average concentration of PM in Seoul metropolitan area satisfied the Korean air quality standard in 2010. Furthermore, concentration of PM in all boroughs across Seoul met the air environment standard in 2012. $PM_{10}$ concentration was relatively higher in center of Seoul in comparison to the rest, while $PM_{2.5}$ concentration showed exactly the contrary result. We analyzed the effect that PM emissions from vehicles would have on PM concentrations across Seoul. The results showed that average annual PM concentration recently decreased in Seoul although the number of vehicles registered annually continued its upward trend. By contrast, average fine dust concentrations in Seoul showed a decline which suggested that correlation between annual average PM concentrations and number of registered vehicles remained low. However, year-on-year vehicle registration rate recently showed a declining tendency in the same way as the trend of changes in average PM concentrations. Particularly, the upward trend in annual average PM concentrations in 2002 and 2007 was consistent with the increase in vehicle registration rate, suggesting that vehicle registration rate was closely associated with changes in PM concentrations.

• Speech Sciences
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• v.12 no.4
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• pp.7-16
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• 2005

We investigate the effect of membership concentration on the performance of the speaker-independent recognition system by FVQ/HMM. For the membership function, we adopt the result obtained from the objective function approach by Bezdek. Membership concentration is done by varying the exponent in the membership function. The number of selected clusters is constrained to two for the sake of cheap computational cost. Experimental results showed that the recognition rate has its maximum value when the membership function was taken to be inversely proportional to the distance of the input vector from the cluster centroid. When the membership concentration was two weak or too strong, the performance was found to be relatively poor as expected. Except these extreme cases, the membership concentration was not shown to affect the recognition rate significantly. This is in accordance with the general observation that the fuzzy system is not much sensitive. to the detailed shape of the membership function as long as it is overlapped over multiple classes.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.189-202
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• 2019

During thermal power coal-fired boiler operation, it is very important to detect the pulverized coal concentration in the air pipeline for the boiler combustion stability and economic security. Because the current measurement methods used by power plants are often involved with large measurement errors and unable to monitor the pulverized coal concentration in real-time, a new method is needed. In this paper, a new method based on microwave circular waveguide is presented. High Frequency Electromagnetic Simulation (HFSS) software was used to construct a simulation model for measuring pulverized coal concentration in power plant pipeline. Theoretical analysis and simulation experiments were done to find the effective microwave emission frequency, installation angle, the type of antenna probe, antenna installation distance and other important parameters. Finally, field experiment in Jilin Thermal Power Plant proved that with selected parameters, the measuring device accurately reflected the changes in the concentration of pulverized coal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of Environmental Science International
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• v.22 no.8
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• pp.1029-1041
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• 2013

In this study, eight episode days of high-concentration $PM_{10}$ occurrences in the Gimhae region between 2006 and 2011 were analyzed. Most of them appeared in winter and the highest concentration was observed around 12 LST. Furthermore, the wind direction, wind velocity, and temperature elements were compared with observed values to verify the WRF numerical simulation results used in this study, and they simulated well in accordance with the trend of the observed values. The wind was generally weak in the high-concentration episode days that were chosen through surface weather chart and the numerical simulation results for wind field, and the air pollutants were congested due to the effects of the resulting local winds, thereby causing a high concentration of air pollutants. Furthermore, the HYSPLIT model was performed with the WRF numerical simulation results as input data. As a result, they originated from China and flowed into Gimhae in all eight days, and the lowest concentration appeared on the days when recirculation occurred.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.59-77
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• 2009

Microblog services are such new communication channels with some considerable potential to improve information sharing. The idea of sharing short messages using multiple access points seems to be appealing to people worldwide. Through the lens of flow and social identity, we explored factors that influence information sharing behaviors in microblog services. With an empirical study, we examined enjoyment and concentration(flow) and self-presentation(social identity) in microblog services like twitter can contribute to the user behaviors. Our aim was to gain insight into ways of creating an environment that facilitating voluntary sharing of information. Our findings suggested that enjoyment, concentration, and selfpresentation were crucial determinants of information sharing behaviors in microblog services. This study has important implications for academic researchers and practitioners who seek to understand why microblog service users share their information with other members in microblog services.

• Journal of Convergence for Information Technology
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• v.10 no.11
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• pp.195-201
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• 2020

There have been many developments and innovations in the educational environments in line with the rapidly evolving information age. E-Learning is a representative example of this rapid evolution. However, E-Learning is challenging to maintain students' concentration because of the low engagement level and limited interactions between instructors and students. Additionally, instructors have limitations in identifying learners' concentration. This paper proposes a system that can measure E-learning users' concentration levels by detecting the users' eyelid movement and the top of the head. The system recognizes the eyelid and the top of the head and measures the learners' concentration level. Detection of the eyelid and the top of the head triggers an event to assess the learners' concentration level based on the users' response. After this process, the system provides a normalized concentration score to the instructor. Experiments with experimental groups and control groups were conducted to verify and validate the system, and the concentration score showed more than 90% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.129-135
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• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.