• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.18-24
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• 2022

This study attempts to provide basic data for the development of manpower maintenance programs by checking the degree of job stress and emotional burnout for nurses working in a university hospital and identifying factors affecting emotional burnout. Data were obtained through a structured questionnaire survey conducted on 187 nurses. The average score for job stress of nurses was 2.50 (range 1 to 4) and emotional burnout was 3.29 (range 1 to 5). The factors affecting emotional burnout were occupational climate, job demand, job insecurity, and lack of reward, which accounted for 44% of explanatory power. In order to reduce the emotional burnout of nurses, the management of medical institutions needs administrative and financial support. Further, it is necessary to improve the organizational culture regarding job assignment through job analysis, employment security, and a performance-based reward system.

• Journal of Platform Technology
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• v.10 no.3
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• pp.21-29
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• 2022

Recently, the scope of the smart factory has been expanded, and process research to minimize the part that requires manpower in many processes is increasing. In the case of detecting defects in the appearance of small products, precise verification using a vision system is required. Reliability and speed of inspection are inefficient for human inspection. In this paper, we propose an algorithm for inspecting product appearance defects using a machine vision system. In the case of the remote control targeted in this paper, the appearance is different for each product. Due to the characteristics of the remote control product, the data obtained using two cameras is compared with the master data after denoising and stitching steps are completed. When the algorithm presented in this paper is used, it is possible to detect defects in a shorter time and more accurately compared to the existing human inspection.

• Journal of Korean Academy of Rural Health Nursing
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• v.17 no.2
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• pp.43-49
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• 2022

Purpose: This study was conducted to explore the telemedicine cooperation experience of nurses working in remote areas. Methods: A focus group interviews were used to collect data. All interviews were recorded and transcribed. Content analysis was used to analyze the data. Results: The three main categories and seven sub-categories of telemedicine cooperation experience that emerged are 1) requirement of education on remote support service, 2) consideration of the recipients of medical support services and the characteristics of the area, and 3) difficulties in conducting telemedicine cooperation. Conclusion: As a result of the study, legal protection should be given priority, and it is necessary to select an area where remote cooperation is essential, to discover subjects, and to reduce the burden of work and division of manpower and duties.

• 중국학논총
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• no.63
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• pp.205-224
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• 2019

The results of the studies of China and Vietnam are as follows. First of all, in China, the labor market in China has been fully completing laws and regulations since the implementation of the labor contract law in 2008. Specifically, we analyzed the labor market in China for labor contracts, recruitment, and minimum wage. Next, in Vietnam, which the tertiary and quaternary industries are rapidly developing. The labor market is expected to increase because demand for foreign manpower, as the advancement of retail, finance, tourism services, Smart factories in the textile and sewing- do. The limitations of this study, however, are that there is not enough data to utilize official data for labor market analysis in China and Vietnam. If a practical investigation is conducted for analyzing the labor market in Vietnam due to the changes in the labor market

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.31-36
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• 2020

The creation of the Defense Integrated Data Center(DIDC) has resulted in a reduction in manpower, operating costs, efficient and effective management of resources. However, it is difficult to effectively collect and manage the data of a large number of battlefields coming from equipments such as drones, robots, and IoT added to the fourth industrial revolution and the future battlefield. Therefore, we will propose the design of DIDC network system using NFV and SDN, which are emerging as the core technologies of 5G, a mobile communication technology. After analyzing the data sheet of each equipment, it is considered that by integrating the redundant functions, energy efficiency, resource utilization and effective network management will be possible.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.581-592
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• 2017

Geographic Information System (GIS) is a method of expressing objects in a space. Currently, many research and developments are being conducted to implement 3D GIS. In previous studies, 3D GIS applications have been developed using Unity 3D, which is a 3D engine with good development accessibility. However, it requires manual work to enter various formats of GIS data, making it difficult to immediately reflect GIS data that change frequently. To improve this problem, this study developed a method for automatically reading and outputting various GIS data from the existing Unity 3D application. The improved application could read Satellite Images, Aerial Photographs, Digital Elevation Models (DEM) and Shapefiles with no transformation through other commercial programs, and they could be implemented as 3D objects. This study automated the GIS data conversion which had been manually performed and as a result, the manpower, time, and resources required for 3D GIS implementation can be saved.

• Journal of the Korean Society of Safety
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• v.33 no.3
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• pp.65-70
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• 2018

Terrain data for disaster analysis in hazardous or disaster areas is not only important but also needs to be built quickly. In particular, the introduction of drones is in the early stages of research using drones in a variety of fields such as shooting, analyzing and managing hazardous areas. It is expected that drone will be faster, safer and more effective than existing data collection method in case of small scale disaster hazard area and disaster area where equipment or manpower input is difficult. Therefore, in this study, drone shooting was performed for hazardous areas in mountainous roads located in Samcheok city, Gangwon province, and ground reference points were measured by RTK-GPS. The measured data were converted into DSM (Digital Surface Model) data by coordinate correction using Pix4D postprocessing program and then applied to the analysis of the hazard area of mountainous area. As a result, it was shown that it is effective to identify the risk by using the basic terrain data obtained from the drones.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.891-894
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• 2010

Steep slope disasters accompany economic loss along with casualties, so the evaluation and the systematic management on the regions with slope collapse danger are required. A lot of manpower, time, and economic cost are needed to accumulate disaster history of steep slope areas by the national and small-sized region. As the method for this, it construed location data about each area with disaster occurrence by maknd elocation data of collapsed steep areas through high-resolution satellite image and collectnd edata on the regions with disasters through media and literature data such as a disaster annual report and a disaster comprehensive report. The study selected three shortest routes includnd ethe area with disaster in Jeolla province on literature and the collapsed area found by the image data, and constructed the results of the field survey as database.

• Agribusiness and Information Management
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• v.7 no.1
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• pp.1-9
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• 2015

The purpose of this study is to develop a growth prediction model that can predict growth and development information influencing the production of citrus fruits: the growth model algorithm that can predict floral leaf ratio, number of fruit sets, fruit width, and overweight depending on the main period of growth and development with consideration of the applied weather factors. Every year, large scale of manpower was mobilized to investigate the production of outdoor-grown citrus fruits, but it was limited to recycling the data without an observation supporting system to systemize the database. This study intends to create a systematical database based on the basic data obtained through the observation supporting system in application of an algorithm according to the accumulated long term data and prepare a base for its continuous improvement and development. The importance of the observed data is increasingly recognized every year, and the citrus fruit observation supporting system is important for utilizing an effective policy and decision making according to various applications and analysis results through an interconnection and an integration of the investigated statistical data. The citrus fruit is a representative crop having a great ripple effect in Jeju agriculture. An early prediction of the growth and development information influencing the production of citrus fruits may be helpful for decision making in supply and demand control of agricultural products.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.