• Journal of Environmental Science International
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• v.16 no.8
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• pp.941-957
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• 2007

This study monitored marine environments and ecological characteristics of phytoplankton in southern coastal waters during June to October in 2004-2006 and provided an information to how Cochlodinium blooms have occurred. A total of sampling sites was 16 (Dukyang bay, Goheung, Yeoja bay, Gamak bay, Gwangyang bay, Yeosu, and Namhae). Temperature ranged from $19^{\circ}C\;to\;29^{\circ}C$, which all of sampling in Yeoja bay was the highest temperature of $27^{\circ}C\;and\;29^{\circ}C$ during summer. On June, July, September, and October did not show a remarkable difference regardless of sampling sites. Yeoja and Gwangyang bays had 25-27 in salinity that was lower approximately 5-6 compared with other sampling sites. Chlorophyll had considerable fluctuations depending on sampling sites on July and October, in particular, Gwangyang bay was the highest value of $15{\mu}gl^{-1}$ that had five times as much as. Unlikely to temperature, salinity, and chlorophyll, transparency ranged from 2 m to 5 m regardless of sampling sites. Gwangyang bay was the highest DIN (Dissolved Inorganic Nitrogen) of $0.53mgl^{-1}$ on July and August that had ten times as much as, whereas DIP (Dissolved Inorganic Phosphorus) did not show a significant difference between sampling sites. On July, the correlation of DIN and chlorophyll was a negative that should extremely require DIN to grow phytoplankton, but was a positive liner on August. Mean cell number of phytoplankton reached to encounter a peak of 500 cells $ml^{-1}$ in July and August, which diatoms were dominant species and attained an abundance of >60% regardless of months. In August, the occurrence of dinoflagellates ranged from 20% to 30%. Skeletonema costatum, one of dominant speicies, was the highest occurrence to throughout sampling sites during 2004 to 2006. On the basis of cluster analysis for phytoplankton, they were distributed in all of sampling sites. Consequently, significant fluctuations of marine environments were shown in summer and S. costatum was regarded as the representive phytoplankton in southern coastal waters. In particular, the outbreaks of Cochlodinium polykrikoides have occurred in Dukyang bay, Gamak bay, Goheung, Yeosu, and Namhae, but Yeoja and Gwangyang bays did not occur. The distinguish characteristics of occurring sampling sites and non-occurring in Cochlodinium blooms based on this study was DIN that was enough to persistently grow and maintain them even a litter dissolved in water. This suggests that Cochlodinium red tide seems to be occur in off waters.

• Journal of dental hygiene science
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• v.13 no.4
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• pp.518-527
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• 2013

In 2011, South Korea's Ministry of Health and Welfare started a national tooth brushing room program without a pilot project. This study aimed to assess the effect of the implementation of this program in Gangneung, Korea. One-year outcomes of oral health behavior and self-reported oral symptoms in the study group after installation of the tooth brushing room were evaluated and compared with those of the control group using chi-square test. The prevalence rate of good self-rated oral health in grade 1-3 students increased from 35.7% immediately after installation to 48.9% after 6 months (p=0.031) compared to 37.3% in the control group (p=0.051). Immediately after installation of the tooth brushing room, 53.5% of grade 1-3 students in the study group brushed their teeth every day, but after 6 months, only 35.5% of students brushed daily (p<0.001) compared to 28.6% in the control group (p=0.007). The prevalence rate of bad breath in grade 1-3 students was 26.2% for the study group immediately after installation compared to 25.5% in the control group (p=0.065), but it declined 16.5% after 6 months (p=0.055). The prevalence rate of bad breath in grade 4~6 students was 14.7% for the study group after 6 months compared to 25.3% in the control group (p=0.016). We recommend the creation of a healthy school environment through a school-based tooth brushing program under the active supervision of classroom teachers and the continuous monitoring of program processes in order to promote children's oral health.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.