• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1173-1178
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• 2023

In modern society, there is a tendency to emphasize efficiency and lead to detailed planning of team projects and individual tasks within organizations. In a 24-hour routine, the ability to use time effectively is considered an indicator of conscientiousness, and people try to imitate planned and organized people and utilize various systems to manage their daily lives. The reason why you want to perform a given task efficiently is because it affects the success or failure of the project by completing the task within a set period of time. Neglecting the project schedule is considered a major risk that threatens a successful outcome. This applies not only to large-scale organizational projects but also to individual life, and utilizes a variety of schedule management tools that emphasize time-based efficient management. In project management, it is necessary to carefully understand the detailed work progress rather than simply based on Today. In this paper, we propose an Android application that can manage schedules by accessing the user's project in units of tasks rather than dates, and introduce its implementation. The application we implemented in this paper can manage the project's goals and schedule by registering the project to which the user belongs. In addition, it provides the ability to approach work efficiently by visualizing the progress of the entire project or individual goals. As a result, users can use the application we propose in this paper to focus on their projects and manage schedules by task, thereby improving the overall efficiency of the project.

• Korean Journal of Environmental Agriculture
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• v.42 no.4
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• pp.259-268
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• 2023

Rhodococcus sp. 3-2 strain has been reported to degrade benzimidazole-based pesticides, such as benomyl and carbendazim. Therefore, this study aimed to optimize culture medium composition and culture conditions to achieve cost-effective and efficient large-scale production of the Rhodococcus sp. 3-2 strain. The study identified that the optimal media composition for mass culture comprised 0.5% glucose, 0.5% yeast extract, 0.15% NaCl, 0.5% K₂HPO₄, 0.5% sodium succinate, and 0.1% MgSO₄. Additionally, a microbial agent was developed using a 1.5-ton fermenter, with skim milk (20%), monosodium glutamate (15%), and vitamin C (2%) as key components. The storage stability of the microbial agent has been confirmed, with advantages of low temperature conservation, which helps to sustain efficacy for at least six months. We also assessed the benomyl degradation activity of the microbial agent within field soil. The results revealed an over 90% degradation rate when the concentration of viable cells exceeded 2.65 × 10⁶ CFU/g after a minimum of five weeks had elapsed. Based on these findings, Rhodococcus sp. 3-2 strain can be considered a cost-effective microbial agent with diverse agricultural applications.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• Korean Journal of Radiology
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• v.2 no.4
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• pp.216-221
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• 2001

Objective: To compare, in terms of their demonstration of tears of the anterior glenoid labrum, oblique axial MR arthrography obtained with the patient's shoulder in the abduction and external rotation (ABER) position, with conventional axial MR arthrography obtained with the patient's arm in the neutral position. Materials and Methods: MR arthrography of the shoulder, including additional oblique axial sequences with the patient in the ABER position, was performed in 30 patients with a clinical history of recurrent anterior shoulder dislocation. The degree of anterior glenoid labral tear or defect was evaluated in both the conventional axial and the ABER position by two radiologists. Decisions were reached by consensus, and a three-point scale was used: grade 1=normal; grade 2=probable tear, diagnosed when subtle increased signal intensity in the labrum was apparent; grade 3=definite tear/defect, when a contrast material-filled gap between the labrum and the glenoid rim or deficient labrum was present. The scores for each imaging sequence were averaged and to compare conventional axial and ABER position scans, Student's t test was performed. Results: In 21 (70%) of 30 patients, the same degree of anterior instability was revealed by both imaging sequences. Eight (27%) had a lower grade in the axial position than in the ABER position, while one (3%) had a higher grade in the axial position. Three whose axial scan was grade 1 showed only equivocal evidence of tearing, but their ABER-position scan, in which a contrast material-filled gap between the labrum and the glenoid rim was present, was grade 3. The average grade was 2.5 (SD=0.73) for axial scans and 2.8 (SD=0.46) for the ABER position. The difference between axial and ABER-position scans was statistically significant (p<0.05). Conclusion: MR arthrography with the patient's shoulder in the ABER position is more efficient than conventional axial scanning in revealing the degree of tear or defect of the anterior glenoid labrum. When equivocal features are seen at conventional axial MR arthrography, oblique axial imaging in the ABER position is helpful.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.531-550
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• 2023

Purpose: The unique nature of life-and-death healthcare services sets them apart from other service industries. While many studies exist on the relationship between healthcare services and customer satisfaction, most of them focus on mildly ill patients, ignoring the differences between critically ill and non-seriously ill patients. This study discusses the actual quality of healthcare services for patients who are facing life-threatening illnesses and are on life support, as well as their right to protection and dignity. Methods: The survey conducted to 149 patients with the four major illnesses: cancer, heart disease, brain disease and rare and incurable disease, those who have experiences with senior general hospitals. Results: The basic statistics of this study are adequate to represent the four major critical illnesses, and the reliability and validity of this study's hypotheses, which were measured by multiple items, were analyzed, and the internal consistency was judged to be high. In addition, it was found that the convergent validity was good and the discriminant validity was also secured. When examining the goodness of fit of the hypotheses, the SRMR, which is the standardized root mean square of residuals that measures the difference between the covariance matrix of the data variables and the theoretical covariance matrix structure of the model, met the optimal criteria. Conclusion: The academic implications of this study are differentiated from other studies by moving away from evaluating the quality of healthcare services for mildly ill patients and focusing on the rights and dignity of patients with life-threatening illnesses in four senior general hospitals. In terms of academic implications, this study enriches the depth of related studies by demonstrating the right to protection and dignity as a factor of patient-centeredness based on physical environment quality, interaction quality, and outcome quality, which are presented as sub-factors of healthcare quality. We found that the three quality factors classified by Brady and Cronin (2001) are optimized for healthcare quality assessment and management, and that the results of patients' interaction quality assessment can be used to provide a comprehensive quality rating for hospitals. Health and human rights are inextricably linked, so assessing the degree to which rights and dignity are protected can be a superior and more comprehensive measurement tool than traditional health level measures for healthcare organizations. Practical implications: Improving the quality of the physical environment and the quality of outcomes is an important challenge for hospital managers who attract patients with life and death conditions, but given the scale and economics of time, money, and human inputs, improving the quality of interactions and defining them as performance indicators in hospital quality management is an efficient way to create maximum value in the short term.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of Platform Technology
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• v.11 no.5
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• pp.84-96
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• 2023

Network slicing refers to a technology that divides network infrastructure into multiple parts. Network slicing enables flexible network configuration while minimizing the physical resources required for network division. For this reason, network slicing technology has recently been developed and introduced in a form suitable for the 5G environment for efficient management of large-scale network environments. However, systematic analysis of network slicing research in the 5G environment has not been conducted, resulting in a lack of systematic analysis of the technology. Accordingly, in this paper, we provide insight into network slicing technology in the 5G network environment by conducting a comparative analysis of the technology. In this study's comparative analysis, 13 literatures on network slicing in the 5G environment was identified and compared and analyzed through a systematic procedure. As a result of the analysis, three network slicing technologies frequently used for 5G networks were identified: RAN (radio access network) slicing, CN (core network) slicing, and E2E (end-to-end) sliding. These technologies are mainly used for network services. It was confirmed that research is being conducted to achieve quality improvement and network isolation. It is believed that the results of this comparative analysis study can contribute to 6G technology research as a future direction and utilization plan for network slicing research.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.299-306
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• 2024

This study sought to suggest improvement measures by analyzing how major satisfaction of college students majoring in tourism in the metropolitan area affects career decision self-efficacy and employment preparation behavior. A survey was conducted on college students majoring in tourism-related fields from April 1 to May 31, 2024, and 217 out of a total of 250 were used for analysis. As a result of the study, hypotheses 1 and 3 established in this study were found to have a significant positive influence, and hypothesis 2 was partially accepted. Therefore, the improvement plan of this study is to make continuous efforts to improve the competitiveness of universities, such as developing educational policies and educational programs to improve the satisfaction of majors, satisfaction with curriculum and classes, and customized teaching methods for each subject, and prior to students' full-scale job preparation activities. There is a need to improve individualized employment strategies for major students and employment counseling methods between professors and students. In particular, it is necessary to establish an efficient employment preparation system through educational facilities and environments, curricula, educational programs, and professor consultations where major students can make their own career decisions.