• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.7-15
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• 2023

The electric submersible pump (ESP) has been operating in production wells around the world because of its high applicability and operational efficiency among artificial lift techniques. When operating an ESP in a reservoir, variables such as temperature, pressure, gas/oil ratio, and flow rate are factors that affect ESP performance. In particular, free gas in the production fluid is a major factor that reduces the life and operational efficiency of ESP. This study presents the flow loop system which can implement the performance and damage tests of ESP considering field operating conditions to quantitatively analyze the variables that affect ESP performance. The developed apparatus in an integrated system that can diagnose the failure and causes of ESP, and detect leak of tubing by linking ESP and tubing as one system. In this study, the flow conditions for stable operation of ESP were identified through single phase and two phase flow experiments related to evaluation for the performance of ESP. The results provide the basic data to develop the failure prediction and diagnosis program of ESP, and are expected to be used for real-time monitoring for optimal operating conditions and failure diagnosis for ESP operation.

• Journal of the Korean Geotechnical Society
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• v.39 no.1
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• pp.15-25
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• 2023

Recent weather changes have led to an increase in heavy rainfall resulting in frequent large-scale slope failures. To minimize damage to life and property, a measurement system is used in slope failure warning systems. However, understanding the slope failure behavior is difficult as the measurement system only measures a specific point. Therefore, numerical analysis must be p erformed with the measurement system. The soil water characteristic curve (SWCC) drying curve and boundary conditions that consider evapotranspiration and precipitation have been applied to numerical analysis, but the hysteresis of SWCC affects the numerical analysis results. To address this, a new evapotranspiration calculation method is proposed and applied to boundary conditions, and the measurement data are compared with the results of the numerical analysis. This method takes into account the different infiltration behaviors on evapotranspiration according to the drying and wetting curves of the SWCC, and allows for a more rational prediction of water movement on unsaturated slopes.

• 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.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.385-388
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• 2023

Gypsy moth (Lymantria dispar), a polyphagous insect pest belonging to the family Lymantriidae, is widely distributed in Korea, Japan, Siberia, Europe, and North America. They pose a threat to various host plants including pear trees, apple trees, and blueberries. Traditionally considered a forest pest, the increasing incursion of gypsy moths into agricultural land near forested areas has intensified damage to crops lacking effective control methods. This study aimed to investigate the temperature-dependent development of gypsy moths to enhance outbreak prediction and advance technology development. The effects of temperature on development of each life stage were investigated under constant temperature conditions of 18, 21, 24, 27, 30, and 33℃ (14L:10D, RH 60±5%) utilizing egg masses collected in Jeollanam-do Jangheung-gun in 2021. The results revealed that higher temperatures accelerated the development rate of the gypsy moth larvae with optimal development occurring at 30℃. However, the survival rate was lowest at 33℃. At the favorable temperature of 30℃, the total development period was 43.8 days for females and 42.5 days for males. The developmental threshold temperature were 13.1℃ for females and 12.5℃ for males, with effective accumulated temperature of 641.1 DD and 657.8 DD, respectively.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.64-73
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• 2024

As the seriousness of habitat destruction caused by development projects emerges, the importance of environmental impact assessment (EIA) is increasing to preserve biodiversity. In previous studies, research is being conducted to quantitatively evaluate the biodiversity impact of development factors and surrounding environmental factors on the landscape scale, but research on the factors affecting the reduction of biodiversity based on development projects is insufficient. This study examined whether independent variables (size of development project, type of the development, DEM, ecosystem and nature map, distance from the green land, distance from the protected area), which have been proven to effect biodiversity through the previous researches, have a significant effect on the change of richness index (RI) through multi-class logistic regression analysis, T-test, and analysis of the development type. As a result, only the size of development project and the first richness index in EIA showed p-value less than 0.05. And it was confirmed that the reduction in biodiversity was significantly changed in the following construction types: installation of sports facilities, energy development, and development of industrial location and industrial complex. Since the results of this study confirmed that the impact of the variables may be inconsistent depending on the analysis scale, additional study of necessary indicators at the development project is needed to analyze biodiversity changes in EIA accurately.

• Korean Circulation Journal
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• v.53 no.3
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• pp.170-184
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• 2023

Background and Objectives: Several cases involving severe right ventricular (RV) failure in advanced cancer patients have been found to be pulmonary tumor thrombotic microangiopathies (PTTMs). This study aimed to discover the nature of rapid RV failure syndrome with a suspected diagnosis of PTTM for better diagnosis, treatment, and prognosis prediction in clinical practice. Methods: From 2011 to 2021, all patients with clinically suspected PTTM were derived from the one tertiary cancer hospital with more than 2000 in-hospital bed. Results: A total of 28 cases of clinically suspected PTTM with one biopsy confirmed case were included. The most common cancer types were breast (9/28, 32%) and the most common tissue type was adenocarcinoma (22/26, 85%). The time interval from dyspnea New York Heart Association (NYHA) Grade 2, 3, 4 to death, thrombocytopenia to death, desaturation to death, admission to death, RV failure to death, cardiogenic shock to death were 33.5 days, 14.5 days, 7.4 days, 6.4 days, 6.1 days, 6.0 days, 3.8 days and 1.2 days, respectively. The NYHA Grade 4 to death time was 7 days longer in those who received chemotherapy (7.1 days vs. 13.8 days, p value=0.030). However, anticoagulation, vasopressors or intensive care could not change clinical course. Conclusions: Rapid RV failure syndrome with a suspected diagnosis of PTTM showed a rapid progressive course from symptom onset to death. Although chemotherapy was effective, increased life survival was negligible, and treatments other than chemotherapy did not help to improve the patient's prognosis.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1269-1280
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• 2022

Objective: This study aimed to evaluate the usefulness of quantitative indices obtained from deep learning analysis of gadoxetic acid-enhanced hepatobiliary phase (HBP) MRI and their longitudinal changes in predicting decompensation and death in patients with advanced chronic liver disease (ACLD). Materials and Methods: We included patients who underwent baseline and 1-year follow-up MRI from a prospective cohort that underwent gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance between November 2011 and August 2012 at a tertiary medical center. Baseline liver condition was categorized as non-ACLD, compensated ACLD, and decompensated ACLD. The liver-to-spleen signal intensity ratio (LS-SIR) and liver-to-spleen volume ratio (LS-VR) were automatically measured on the HBP images using a deep learning algorithm, and their percentage changes at the 1-year follow-up (ΔLS-SIR and ΔLS-VR) were calculated. The associations of the MRI indices with hepatic decompensation and a composite endpoint of liver-related death or transplantation were evaluated using a competing risk analysis with multivariable Fine and Gray regression models, including baseline parameters alone and both baseline and follow-up parameters. Results: Our study included 280 patients (153 male; mean age ± standard deviation, 57 ± 7.95 years) with non-ACLD, compensated ACLD, and decompensated ACLD in 32, 186, and 62 patients, respectively. Patients were followed for 11-117 months (median, 104 months). In patients with compensated ACLD, baseline LS-SIR (sub-distribution hazard ratio [sHR], 0.81; p = 0.034) and LS-VR (sHR, 0.71; p = 0.01) were independently associated with hepatic decompensation. The ΔLS-VR (sHR, 0.54; p = 0.002) was predictive of hepatic decompensation after adjusting for baseline variables. ΔLS-VR was an independent predictor of liver-related death or transplantation in patients with compensated ACLD (sHR, 0.46; p = 0.026) and decompensated ACLD (sHR, 0.61; p = 0.023). Conclusion: MRI indices automatically derived from the deep learning analysis of gadoxetic acid-enhanced HBP MRI can be used as prognostic markers in patients with ACLD.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.407-417
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• 2024

The objective of this study is to predict and reduce potential damage caused by chlorine gas leaks, a hazardous material, when vehicles transporting it overturn due to accidents or other incidents. The goal is to forecast the anticipated damages caused by chlorine toxicity levels (ppm) and to design effective response strategies for mitigating them. To predict potential damages, we conducted quantitative assessments using the ALOHA program to calculate the toxic effects (ppm) and damage distances resulting from chlorine leaks, taking into account potential negligence of drivers during transportation. The extent of damage from toxic gas leaks is influenced by various factors, including the amount of the leaked hazardous material and the meteorological conditions at the time of the leak. Therefore, a comprehensive analysis of damage distances was conducted by examining various scenarios that involved variations in the amount of leakage and weather conditions. Under intermediate conditions (leakage quantity: 5 tons, wind speed: 3 m/s, atmospheric stability: D), the estimated distance for exceeding the AEGL-2 level of 2 ppm was calculated to be 9 km. This concentration poses a high risk of respiratory disturbance and potential human casualties, comparable to the toxicity of hydrogen chloride. In particular, leaks in urban areas can lead to significant loss of life. In the event of a leakage incident, we proposed a plan to minimize damage by implementing appropriate response strategies based on the location and amount of the leak when an accident occurs.