• Journal of Wetlands Research
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• v.25 no.2
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• pp.83-90
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• 2023

Due to climate change, the occurrence of abnormal rainfall is increasing, and the intensity and scale of damage caused by heavy rain are increasing every year. In addition, as the frequency of heavy rains becomes more frequent, heavy rains often occur continuously, resulting in large flooding damage that has never been seen before in urban area. When near rivers and coastal areas are impermeable areas, the maximum flow increases rapidly as the rainfall intensity increases, so a comprehensive flood risk evaluation is needed considering the characteristics of the basin. In this study, the flood inundation risk evaluation was analyzed by giving scores on evaluation factors as a measure to prevent inundation in subway stations. Through the flood inundation risk evaluation process considering the comprehensive evaluation index, the flood risk evaluation was conducted on five urban railway stations with a large amount of traffic and floating population that had been inundated in the past. It is judged that by comprehensively analyzing this and establishing a inundation risk grade (grade 1 to 4) to establish a flood measure suitable for the risk grade.

• The Journal of Korean Obstetrics and Gynecology
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• v.22 no.4
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• pp.101-108
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• 2009

Purpose: Integrative studies have been made to review the correlationship of menstrual period with obesity and stress, and the relationship between stress and obesity has been reconfirmed through the study. Methods: Among the first time outpatients who visited the gynecological department of the OO oriental medical center from May 1st to September 1st of the year 2009, total 114 patients were included for the study by excluding the patients who received uterine hysterectomy, patients taking hormonal medications, and the patients who installed intrauterine devices. Survey has been made to investigate patients' age, menstrual period and duration of menstrual period. The investigation for the degree of obesity and stress was conducted as in below. Results: 1. From the menstrual cycle difference reviewed by Gonadosomatic index (GSI), the severe GSI group tended to show longer menstrual cycle than moderative GSI group. 2. From the menstrual cycle difference reviewed by Body Mass Index (BMI), longer menstrual cycle was observed from the abnormal BMI group than the normal BMI group. 3. No correlative probability values of GSI and BMI were observed. 4. Although the linear regression analysis result of BMI and GSI with the menstrual cycle did not show any statistical significance, the study resulted to show a tendency. Conclusion: Although the correlationship of menstrual cycle with obesity and stress did not show any significance, it is considered that the menstrual period could be affected by the combination of the variables rather than by independent variable.

• Journal of Service Research and Studies
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• v.11 no.2
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• pp.118-130
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• 2021

This study designs and validates a greenhouse complex environmental control algorithm with a multi-phase processing scheme that can combine and control actuators according to the degree of change in the greenhouse environment. The composite environmental control system is a system in which the complex environmental controller analyzes the information detected by sensors and operates appropriately actuators to maintain the crop growth environment. A composite environmental controller directs control devices driving actuators through a composite environmental control algorithm, which calculates the values necessary for the operation of the control devices. Most existing algorithms carry out control procedures on a single phase by iteration cycle, which can cause abnormal changes in the greenhouse environment due to errors in output. The proposed algorithm distributes control procedures over multiple phases: environmental control, environmental control, and device operation, and every iteration cycle, detects environmental changes in the environmental control phase first, and then combines control devices that can control the environment in the environmental control phase, and finally, performs the controls to derive the actuators in the device operation phase. The proposed algorithm is designed based on the analysis of the relationship between greenhouse environmental elements and control devices deriving actuators. According to verification analysis, the multi-phase processing scheme provides room to modify or supplement the setting value and enables the control devices to reflect changes in the associated environmental components.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.169-177
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• 2010

Recently due to the typhoon and extreme rainfall induced by abnormal weather and climate change, the probability of severe damage to human life and property is rapidly increasing. Thus it is necessary to create adequate and reliable flood risk map in preparation for those natural disasters. The study area is Seo-gu in Daegu which is located near Geumho river, one of the tributaries of Nakdong river. Inundation depth and velocity at each time were calculated by applying FLUMEN model to the target area of interest, Seo-gu in Daegu. And the research of creating flood risk map was conducted according to the Downstream Hazard Classification Guidelines of USBR. The 2-dimensional inundation analysis for channels and protected lowland with FLUMEN model was carried out with the basic assumption that there's no levee failure against 100 year precipatation and inflow comes only through the overflowing to the protected lowland. The occurrence of overflowing was identified at the levee of Bisan-dong located in Geumho watershed. The level of risk was displayed for house/building residents, drivers and pedestrians using information about depth and velocity of each node computed from the inundation analysis. Once inundation depth map and flood risk map for each region is created with this research method, emergency action guidelines for residents can be systemized and it would be very useful in establishing specified emergency evacuation plans in case of levee failure and overflowing resulting from a flood.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.142-147
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• 2023

Objectives: As medical history before surgery is often based on patient reporting, there is the possibility that patients intentionally hide underlying diseases or that dentists cannot recognize abnormal health states. Therefore, more professional and reliable treatment processes are needed under the Korean dental specialist system. The purpose of this study was to elucidate the necessity of a preoperative blood testing routine prior to office-based surgery under local anesthesia. Patients and Methods: Preoperative blood lab data for 5,022 patients from January 2018 to December 2019 were assembled. Study participants were those who underwent extraction or implant surgery under local anesthesia at Seoul National University Dental Hospital. Preoperative blood tests included complete blood count (CBC), blood chemistry, serum electrolyte, serology, and blood coagulation data. Values outside of the normal range were considered an "abnormality," and the percentage of abnormalities among the total number of patients was calculated. Patients were divided into two groups based on the presence of underlying disease. The rates of abnormalities in the blood tests were compared between groups. Chi-square tests were performed to compare data from the two groups, and P<0.05 was considered statistically significant. Results: The percentages of males and females in the study were 48.0% and 52.0%, respectively. Of all patients, 17.0% (Group B) reported known systemic disease, while 83.0% (Group A) reported no specific medical history. There were significant differences between Groups A and B in CBC, coagulation panel, electrolytes, and chemistry panel (P<0.05). In Group A, the results of blood tests that required a change in procedure were identified even though the proportion was very small. Conclusion: Preoperative blood tests for office-based surgery can detect underlying medical conditions that are difficult to identify from patient history alone and can prevent unexpected sequelae. In addition, such tests can result in a more professional treatment process and build patient confidence in the dentist.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.755-770
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• 2023

Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.191-202
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• 2024

Global warming is causing abnormal climates worldwide due to the increase in greenhouse gas concentrations in the atmosphere, negatively affecting ecosystems and humanity. In response, various countries are attempting to reduce greenhouse gas emissions in numerous ways, and interest in blue carbon, carbon absorbed by coastal ecosystems, is increasing. Known to absorb carbon up to 50 times faster than green carbon, blue carbon plays a vital role in responding to climate change. Particularly, the tidal flats of South Korea, one of the world's five largest tidal flats, are valued for their rich biodiversity and exceptional carbon absorption capabilities. While previous studies on blue carbon have focused on the carbon storage and annual carbon absorption rates of tidal flats, there is a lack of research linking tidal flat area changes detected using satellite data to carbon storage. This study applied the direct difference water index to high-resolution satellite data from PlanetScope and RapidEye to analyze the area and changes of the Nakdong River estuary tidal flats over six periods between 2013 and 2023, estimating the carbon storage for each period. The analysis showed that excluding the period in 2013 with a different tidal condition, the tidal flat area changed by up to approximately 5.4% annually, ranging from about 9.38 km² (in 2022) to about 9.89 km² (in 2021), with carbon storage estimated between approximately 30,230.0 Mg C and 31,893.7 Mg C.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.