• Korean Journal of Occupational Health Nursing
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• v.32 no.2
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• pp.39-48
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• 2023

Purpose: This study aimed to understand how changes in income due to the COVID-19 pandemic have affected the health behavior and mental health status of self-employed individuals. Methods: We compared the health behavior and mental health status of regular wage workers and self-employed individuals with no change in income, with that of self-employed individuals with reduced income due to the spread of COVID-19. Results: Smoking status, average amount of smoking per day, changes in the amount of smoking and drinking due to COVID-19, drinking frequency per year, monthly binge drinking experiences, subjective stress, and suicidal thoughts experienced by self-employed individuals with decreased income were not only higher than those of wage workers and self-employed individuals with maintained income, but their happiness index was also lower than the latter group. Conclusion: This study suggests that the change in total household income due to COVID-19 adversely affects the health behavior and mental health status of self-employed individuals. However, COVID-19-related policies focus only on economic loss compensation, and the health behavior and mental health management for self-employed individuals is insufficient. Therefore, it is necessary to establish policies for health behavior and mental health management of self-employed individuals.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.250-256
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• 2009

This paper presents a robotic tower-crane system using encoder and gyroscope sensors as path tracking devices. Tower crane work is often associated with falling accidents and industrial disasters. Such problems often incur a loss of time and money for the contractor. For this reason, many studies have been done on an automatic tower crane. As a part of 5-year 23-million-dollar research project in Korea, we are developing a robotic tower crane which aims to improve the safety level and productivity. We selected a luffing tower crane, which is commonly used in urban construction projects today, as a platform for the robotic tower crane system. This system comprises two modules: the automated path planning module and the path tracking module. The automated path planning system uses the 3D Cartesian coordinates. When the robotic tower crane lifts construction material, the algorithm creates a line, which represents a lifting path, in virtual space. This algorithm seeks and generates the best route to lift construction material while avoiding known obstacles from real construction site. The path tracking system detects the location of a lifted material in terms of the 3D coordinate values using various types of sensors including adopts encoder and gyroscope sensors. We are testing various sensors as a candidate for the path tracking device. This specific study focuses on how to employ encoder and gyroscope sensors in the robotic crane These sensors measure a movement and rotary motion of the robotic tower crane. Finally, the movement of the robotic tower crane is displayed in a virtual space that synthesizes the data from two modules: the automatically planned path and the tracked paths. We are currently field-testing the feasibility of the proposed system using an actual tower crane. In the next step, the robotic tower crane will be applied to actual construction sites with a following analysis of the crane's productivity in order to ascertain its economic efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.

• Research in Plant Disease
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• v.28 no.4
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• pp.195-203
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• 2022

Zea mays, known as maize or corn, is a major staple crop and an important source of energy for humans and animals, thus ensuring global food security. Approximately 9.4% of the loss of total annual corn production is caused by pathogens including fungi, bacteria, and viruses, resulting in economic losses. Although the use of fungicides is one of the most common strategies to control corn diseases, the frequent use of fungicides causes various health problems in humans and animals. In order to overcome this problem, an eco-friendly control strategy has recently emerged as an alternative way. One such eco-friendly control strategy is the use of beneficial microorganisms in the control of plant pathogens. The beneficial microorganisms can control the plant pathogens in various ways, such as spatial competition with plant pathogens, inhibition of fungal or bacterial growth via the production of secondary metabolites or antibiotics, and direct attack to plant pathogens via enzyme activity. Here, we reviewed microorganisms as biocontrol agents against corn diseases.

• Journal of Forest and Environmental Science
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• v.39 no.2
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• pp.105-117
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• 2023

Climate change and invasive alien plant species (IAPs) are having a significant impact on mountain ecosystems. The combination of climate change and socio-economic development is exacerbating the invasion of IAPs, which are a major threat to biodiversity loss and ecosystem functioning. Species distribution modelling has become an important tool in predicting the invasion or suitability probability under climate change based on occurrence data and environmental variables. MaxEnt modelling was applied to predict the current suitable distribution of most noxious weed A. adenophora (Spreng) R. King and H. Robinson and analysed the changes in distribution with the use of current (year 2000) environmental variables and future (year 2050) climatic scenarios consisting of 3 representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5) in Bhutan. Species occurrence data was collected from the region of interest along the road side using GPS handset. The model performance of both current and future climatic scenario was moderate in performance with mean temperature of wettest quarter being the most important variable that contributed in model fit. The study shows that current climatic condition favours the A. adenophora for its invasion and RCP 2.6 climatic scenario would promote aggression of invasion as compared to RCP 4.5 and RCP 8.5 climatic scenarios. This can lead to characterization of the species as preferring moderate change in climatic conditions to be invasive, while extreme conditions can inhibit its invasiveness. This study can serve as reference point for the conservation and management strategies in control of this species and further research.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.279-290
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• 2022

A superconducting fault current limiter(SFCL) is a power device that exploits superconducting transition to control currents and enhances the flexibility, stability and reliability of the power system within a few milliseconds. With a high phase transition speed, high critical current densities and little AC loss, high-temperature superconducting (HTS) wires are suitable for a resistive-type SFCL. However, HTS wires due to the lack of optimization research are rather inefficient to directly apply to a fault current limiter in terms of the design and capacity, for the existing method relied the characteristics. Therefore, in order to develop a suitable wire for an SFCL, it is necessary to enhance critical current uniformity, select optimal stabilizer materials and conducted research on the development of uniform stabilizer layering technology. The high temperature superconducting wires manufactured by this study get an average critical current of 804 A/12mm-width at the length of 710m; therefore, conducted research was able to secure economic performance by improving efficiency, reducing costs, and reducing size.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.3
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• pp.155-164
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• 2023

About 75% of vessel collision accidents are caused by human error, which causes enormous economic loss, environmental pollution, and human casualties, thus research on automatic collision avoidance of vessels is being actively conducted. In addition, vessels must comply with the COLREGs rules stipulated by IMO when performing collision avoidance with other vessels in motion. In this study, the collision risk was calculated by estimating the position and velocity of other vessels through the Probabilistic Data Association Filter (PDAF) algorithm based on RADAR sensor data. When a collision risk is detected, we propose an event-triggered Nonlinear Model Predict Control (NMPC) algorithm that geometrically creates waypoints that satisfy COLREGs and follows them. To verify the proposed algorithm, simulations through MATLAB are performed.

• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.12.1-12.7
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• 2023

Bovine viral diarrhea virus (BVDV) is an RNA virus belonging to Pestivirus in the family Flaviviridae. BVDV has economic significance for the livestock industry because of its association with acute disease, fetal loss, and birth of persistently infected (PI) animals. This study aimed to investigate the BVDV infection rates in Korean native cattle farms in Jeju for further planning of a BVDV control program in the Jeju Province. BVDV antibodies and antigens were tested in 15,842 sera collected from 302 Korean native cattle herds between January 2014 and June 2017 using enzyme-linked immunosorbent assay (ELISA). Viral antigen was detected by reverse transcription-polymerase chain reaction from 60 sera that were antigen ELISA-positive. BVDV antibodies were found in 90.7% (274/302) herds and 61.1% (9,678/15,842) cows. BVDV antigens were found in 13.2% (40/302) herds and 0.4% (61/15,842) cows. The oldest animal group (> 8 years) exhibited the highest sero-positive rates (91%), while the youngest animal group (< 1 years) had the highest antigen positivity rates (0.52%). Of the 60 antigen-positive sera, BVDV types 1 and 2 were found in 36 and 12 sera, respectively. Additionally, six animals were considered to be PI as BVDV was continually detected in annual examination.

• The Journal of Bigdata
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• v.8 no.1
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• pp.73-82
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• 2023

Marine debris not only affects the survival of marine life, water pollution, and scenery but also has secondary effects on economic loss and human health. While research on underwater and surface debris is actively ongoing, solutions to marine debris in hard-to-reach blind spots are being developed slowly. To address this problem, we utilize drones to detect and track marine debris in blind spots such as tetrapods. The detected debris is then visualized by calculating its location coordinates using the drone's GPS, altitude, and heading values. The proposed method of using drones for detecting marine debris and matching it with longitude and latitude coordinates provides an effective solution to the problem of marine debris in blind spots.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.347-356
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• 2023

In Water Distribution Systems (WDSs), can abnormal hydraulic and water quality conditions such as red-water phenomenon and leakage occur. To restore them, data is generated through various meters data to predict and detect. However, in the case of leakage if difficult to detect unless direct exploration is performed. Among them, unreported leakage, are not seen visually and account for the most considerable volumes of leakage, which leads to economic loss. Bur direct exploration is limited through on site conditions such as securing professional manpower. In this paper, leakage volumes and location were randomly generated for the WDS, which was assumed to be calibrated, and it was detected through a deep learning model. For abnormal data generation, the leakage was simulated using the emitter coefficient, and leakage detection was successfully performed through the generated abnormal data and normal data.