• Journal of Wetlands Research
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• v.23 no.1
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• pp.27-34
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• 2021

In this study, the main influencing factors of the occurrence of cyanobacteria at each of the eight Multifunctional weirs were derived using a random forest, and a categorical prediction model based on a Algal bloom warning system was developed. As a result of examining the importance of variables in the random forest, it was found that the upstream points were directly affected by weir operation during the occurrence of cyanobacteria. This means that cyanobacteria can be managed through efficient security management. DO and E.C were indicated as major influencers in midstream. The midstream section is a section where large-scale industrial complexes such as Gumi and Gimcheon are concentrated as well as the emissions of basic environmental facilities have a great influence. During the period of heatwave and drought, E.C increases along with the discharge of environmental facilities discharged from the basin, which promotes the outbreak of cyanobacteria. Those monitoring sites located in the middle and lower streams are areas that are most affected by heat waves and droughts, and therefore require preemptive management in preparation for the outbreak of cyanobacteria caused by drought in summer. Through this study, the characteristics of cyanobacteria at each point were analyzed. It can provide basic data for policy decision-making for customized cyanobacteria management.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.259-267
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• 2021

Recently, the market for personal health care and medical devices based on Bluetooth Low Energy(BLE) has grown rapidly. BLE is being used in various medical data communication devices based on low power consumption and universal compatibility. However, since data errors occurring in the transmission of medical data can lead to medical accidents, it is necessary to analyze the causes of errors and study methods to reduce data error. In this paper, the minimum communication speed to be used in medical devices was set to at least 800 byte/sec based on the wireless electrocardiography regulations of the Ministry of Food and Drug Safety. And the data loss rate was tested when data was transmitted at a speed higher than 800 byte/sec. The factors that cause communication data error were classified, and the relationship between each factor and the data error rate was analyzed through experiments. When there were two or more activated peripherals connected to the central, data error occurred due to channel hopping and bottleneck, and the data error rate increased in proportion to the communication distance and the number of activated peripherals. Through this experiment, when the BLE is used in a medical device that intermittently transmits biosignal data, the risk of a medical accident is predicted to be low if the number of peripherals is 3 or less. But, it was determined that BLE would not be suitable for the development of a biosignal measuring device that must be continuously transmitted in real time, such as an electrocardiogram.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.172-178
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• 2022

In this paper, water meter AMR(automatic meter reading), one of the core technologies of smart water grid, using LoRa IoT network is studied. The main content of the research is to develop the network system and show the test results that one PC server receives the readings of water meters from multiple households through LoRa communication and stores them in the database, and at the same time sends the data to the web server database through internet. The system also allows users to monitor the meter readings using their smartphones. The hardware and firmware of the main board of the digital water meter are developed. For a PC server program, MDMS(meter data management system) is developed using Visual C#. The app program running on the user's smartphone is also developed using Android Studio. By connecting each developed parts, the total network system is mounted on a flow test bench in the laboratory and tested. For the fields test, 5 places around the university are selected and the transmission distances are tested. The test result show that the developed system can be applied into the real field. The developed system can be expanded to various social safety nets such as monitoring the living alone or elderly with dementia.

• Pediatric Infection and Vaccine
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• v.29 no.1
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• pp.37-45
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• 2022

Purpose: During the coronavirus disease 2019 (COVID-19) pandemic, we conducted a Delphi survey that included the experts from the field of COVID-19 immunization in children aged 5-11 years. The aim was to organize collective expert opinions on COVID-19 vaccination in young children in the Republic of Korea, and so thus assist the vaccination policy. Methods: The panels included pediatric infectious disease specialists, preventive medicine experts, infectious disease physicians, and COVID-19 vaccine experts consulting the Ministry of Health and Welfare. The Delphi survey was conducted online using a questionnaire from February 14 to February 27, 2022. Results: The Delphi panels agreed that children were vulnerable to COVID-19, and the severity of illness was modest. Furthermore the panels reported that children with chronic illness were more susceptible to a worsening clinical course. There were generally positive opinions on the effectiveness of COVID-19 vaccination in children aged 5-11 years, and experts gathered a slightly positive opinion that the adverse events of pediatric COVID-19 were not numerous. The benefits of COVID-19 vaccination were evaluated at a level similar to the potential risks in children. Currently, the only approved mRNA platform vaccine in children seemed to be sustainable; however, the recombinant protein platform COVID-19 vaccines were evaluated as better options. Conclusions: Due to the surge of the Omicron variant and an increase in pediatric cases, the COVID-19 vaccination in young children may have to be considered. Panels had neutral opinions regarding the COVID-19 vaccination in children aged 5-11 years. Thus monitoring of the epidemiology and the data about the safety of COVID-19 vaccination should be continued.

• Economic and Environmental Geology
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• v.54 no.2
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• pp.199-212
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• 2021

The Sebyeonggwan Hall (National Treasure No. 305) is located on the Naval Headquarter of Three Provinces in Tongyeong, and it has partly undergone with several rebuilding, remodeling, repairing and restorations since it's the first establishment in Joseon Dynasty (AD 1605) of ancient Korea. This study focuses on 50 foundation stones that comprise the Sebyeonggwan. These stones are made of six rock types and currently have various shapes of the surface damages. As the foundation stones, the dominant rock type was dacitic lapilli tuffs, and provenance-based interpretation was performed to supply alternative stones for conservation. Most of the provenance rocks for foundation stones showed highly homogeneity with their corresponding stones of petrography, mineralogy and magnetic susceptibility. According to surface deterioration assessments, the most serious damages of the stones were blistering and scaling. The deterioration mechanism was identified through the analysis of inorganic contaminants, and the primary reason is considered salt weathering caused by sea breeze and other combined circumstances. Based on the mechanical durability of the stones, there was no foundation stone that required the replacement of its members attributed to the degradation of the rock properties, but conservation treatment is considered necessary to delay superficial damage. The foundation stones are characterized by a combined outcome of multiple petrological factors that caused physical damage to surfaces and internal defects. Therefore, it's required to diagnosis and monitoring the Sebyeonggwan regularly for long-term preservation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.346-352
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• 2022

Mountain meteorology in South Korea that is covered mountains with complex terrain is important for understanding and managing the forest disaster and forest ecosystems. In particular, recent changes in dryness and/or rainfall intensity due to climate change may cause an increase in the possibility of forest disasters. Therefore, accurate monitoring of mountain meteorology is needed for efficient forest management. Korea Forest Service (KFS) is establishing the Automatic Mountain Meteorology Observation Stations (AMOS) in the mountain regions since 2012. 464 AMOSs are observing various meteorological variables such as air temperature, relative humidity, wind speed and direction, precipitation, soil temperature, and air pressure for every minute, which is conducted the quality control (QC) to retain data reliability. QC process includes the physical limit test, step test, internal consistency test, persistence test, climate range test, and median filter test. All of AMOS observations are open to use, which can be found from the Korean Mountain Meteorology Information System (KoMIS, http://mtweather.nifos.go.kr/) of the National Institute of Forest Science and the Public Data Portal (https://public.go.kr/). AMOS observations with guaranteed quality can be used in various forest fields including the public safety, forest recreation, forest leisure activities, etc., and can contribute to the advancement of forest science and technology. In this paper, a series of processes are introduced to collect and use the AMOS dataset in the mountain region in South Korea.

• Korean Journal of Poultry Science
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• v.49 no.4
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• pp.281-286
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• 2022

This study was conducted to investigate the detection rate and serotypes of Salmonella spp. in conventional and welfare poultry farms. Ten welfare (five layer and five broiler) and 15 conventional farms (five layer and ten broiler farms) were visited to collect environmental samples for identification and serotyping of Salmonella spp. The detection rate of Salmonella spp. was higher in the welfare farms than in conventional farms in both layer and broiler farms. In layer farms, Salmonella spp. was detected in 0.76% (1 out of 130) of samples from one of five welfare layer farms, but was not detected in the five in conventional layer farms. No significan ifference (P>0.05) was observed between the welfare and conventional layer farms. In broiler farms, Salmonella spp. was detected in 10.5% (21 out of 200) of samples from four of five welfare broiler farms and 3.5% (7 out of 200) of samples from five of ten conventional broiler farms, and a significant difference (p <0.05) was observed between the welfare and conventional broiler farms. Among 29 Salmonella spp. isolates, five isolates were serotyped to Salmonella enterica subsp. Enteritidis (n=2), Salmonella enterica subsp. Grampian (n=1), Salmonella enterica subsp. Virchow (n=1), and Salmonella enterica subsp. Senftenberg (n=1). These results suggest that microbial risks could be higher in welfare farms than in conventional farms due to easy access to open-air areas, environmental enrichment, and reduced use of antibiotics. Therefore, continuous monitoring and surveillance for Salmonella spp. is necessary to improve the microbiological safety of poultry meat.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Journal of Environmental Health Sciences
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• v.49 no.4
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• pp.183-189
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• 2023

Background: The indoor air quality of classrooms, in which the capacity per unit area is high and students spend time together, must be managed for safety and comfort. It is necessary to develop an eco-friendly indoor air quality reduction method rather than biased management that relies solely on air purifiers. Objectives: In this study, plants and air purifiers were installed in middle school classrooms to evaluate the indoor PM_2.5 reduction. Methods: Four middle school classrooms were selected as test beds. Air quality was monitored in real-time every one minute using IoT equipment installed in the classrooms, corridors, and rooftops. After measuring the background concentration, plants and air purifiers were installed in the classroom and the PM_2.5 reduction effect was analyzed through continuous monitoring. Results: After installing the plants and air purifiers, the average PM_2.5 concentration was 33.7 ㎍/m³ in the classrooms without plants and air purifiers, 25.6 ㎍/m³ in classrooms with plants only, and 21.7 ㎍/m³ in classrooms with air purifiers only. In the classroom where plants and air purifiers were installed together, it was 20.0 ㎍/m³. The reduction rates before and after installation were 4.5% for classrooms with plants only, 16.5% for classrooms with air purifiers only, and 27.6% for classrooms with both plants and air purifiers. The I/O ratio, which compares the concentration of PM_2.5 in classrooms with corridors and outside air, also showed the lowest in the order of plants and air purifiers, air purifiers, and plant-only classrooms. Conclusions: The PM_2.5 reduction effect of using plants was confirmed, and it is expected to be used as basic data for the development of environmentally-friendly indoor air quality improvement methods.