• Quality Improvement in Health Care
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• v.26 no.2
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• pp.86-94
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• 2020

Purpose: This study aimed to identify current quality control (QC) practices of primary care clinics participating in the National Cancer Screening Program (NCSP) in Korea. Methods: A nationwide survey using a structured questionnaire was conducted among the primary care clinics participating in the NCSP, which were selected by a proportionate stratified sampling. The questionnaire consisted of general information about the responding clinics and the scope of QC activities undertaken. A total of 360 clinics responded and the set of data was then analyzed with Chi-square test and multivariable logistic regression analysis. Results: Among 360 respondents, 332 (92%) reported that they were involved in the QC activities. Most frequently performed QC activities were 'maintenance of facility and instruments' (89%) and 'staff training' (85%). The analysis revealed, with statistical significance (p<.05), that there was an association between certain characteristics of the clinics and the scope of QC activities. These findings also indicated that the diversity of QC practices varies according to the size of the clinics. The clinics screening more types of cancer, those with more screenees, and those with more employees were more likely to implement various QC activities including 'maintenance of facility and instruments', 'external quality control', and 'management of screening data'. Conclusion: To our knowledge, this is the first study to investigate the current status of QC activities conducted among primary care clinics participating in the NCSP. The results of this survey can be used as a basis for further development of policies on quality management of small- and medium-sized primary care clinics in Korea. However, further studies encompassing various aspects of QC activities and management of primary care clinics are needed to assess the current situation in a concise manner.

• (The)Study of the Eastern Classic
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• no.32
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• pp.279-310
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• 2008

In this research, the writer investigated the thought in realism and view on education appeared in the text, GUANZI("管子") roughly. The thoughts and contemplation in GUANZI mostly contain practical issues of politics, law and economy, as well as military policy and also get involved in the education pursuing an organic relationship therewith. In GUANZI, the rule of law and morality were applied to the politics in a harmonious fashion. Although the text upheld agrarian-oriented policy in connection with nation's economy, it, however, succeeded to secure the national wealth by having implemented the polices concerned with industry and commerce in an appropriate manner. In addition, he established strong military organization through political stability and by securing economic strength. In short, this is the policy in order to pursue 'a rich nation with a strong military.' Under such situation, education made a positive contribution to meet the realistic needs in order for reinforcement of politics, economy, and military. In the level of moral education that will set right the decorum and proprieties of the people, and their loyalty and integrity, vocational training that enabled the four divisions of society, that is, the official class, farmers, artisans and merchants, to carry out their given jobs successfully was highly valued in GUANZI. These are substantial efforts in order to establish the order of community by means of putting emphasis on people's morality and loyalty, and also to create the public weal through reinforcement of producing activities of each class of society. After all, the realistic thought and view on education appeared in GUANZI can be understood as an expression of strong will to accomplish national prosperity and military strength in order to overcome disturbing situations in the society in those days.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.32 no.1
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• pp.171-197
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• 2021

The purpose of this study is to suggest the ways of improving systematic records management based on the results of analyzing the present records management situation of college and department in an university which is operating archives. To achieve the purpose, survey and interview of the persons in charge of producing and managing the administrative documents of college and department were conducted. Furthermore, actual condition investigation of records management of college and department and university archives were analyzed. According to the results of analysis, the following problems were found: 1) lack of sufficient records management by college and department, 2) lack of understanding of tasks by processing staffs, 3) operational problems of university archives. Based on the results, recommendations were made as follows; First, it is necessary to improve an awareness of records management by operating staffs. Second, it is necessary to educate staffs through the development of training programs reflecting the characteristics of the university and set up guidelines for records management by college and department. Third, it is necessary to execute reorganization and supplement the personnel of university archives.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.787-797
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• 2020

The marine officer plays a pivotal role in the shipping organization as a professional who performs a complex and diverse function. On the sea, unlike land duty, the possibility of turnover increases due to characteristics such as living in isolated spaces, continuous shift work during a set sailing period, high intensity work tension, stress, and social isolation. In this study, the impact of the organization's civic actions on the intention of turnover as a mediator of organizational immersion was divided into three groups of large companies, small and medium-sized enterprises, and public enterprises to check the differences between each category in a structural manner. Analysis showed that there were statistically significant differences between the groups in loyalty and turnover intention when the sub-factors of organizational commitment and organizational citizen behavior of the marine officer, and the size of turnover intention were included. Organization citizen behavior did not directly affect turnover intention, but when indirect effects were included, there was an effect through loyalty, and relationship-oriented organizational citizen behavior negatively affected turnover intention through loyalty. Excluding public enterprises, the non-standardization path coefficients were -0.229±0.117 and -0.319±0.068, respectively, showing a statistically significant effect in large companies and SMEs. These results indicate that in order to lower the employee turnover intention in large corporations and small and medium-sized shipping companies, it is necessary to consider not only organizational citizen behavior but also measures to increase organizational commitment.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.1
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• pp.139-146
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• 2021

This study studied the network between positive psychology capital, intention of start-up, and start-up behavior for learners who are willing to start a business. The research targets were intended to study the impact of the connection relationship between each variable, targeting nail art certification learners who are willing to start their own businesses. For this study, the measurement variables of positive psychology capital, intention of start-up, and start-up behavior were set, and the collected data were analyzed for connection-centeredness and eigenvector after data collection. The findings are as follows. First, some variables affecting the intention of start-up showed optimism, resilience and hope of positive psychology capital. Second, the intention to start a business was shown to have a significant impact on the behavior of start-ups, which, unlike the preceding study, appeared to be almost outside the network structure, showing that the behavior of start-ups was not significantly affected by other variables. Third, it is important to increase self-efficacy in positive psychological capital in order to increase the behavior of start-ups. Fourth, the analysis of the eigenvactor among positive psychology capital, intention of start-up, and start-up behavior showed optimism as some of the most central variables. In other words, prospective start-ups were found to be aware of the hardships and expected positive results in the future. The implications of this study, along with the intention and behavior of prospective entrepreneurs, are important factors in positive psychology capital, and suggest the importance of various educational programs that can be enhanced by positive psychology capital in start-up education or training programs and what should be taught. In addition, this study analyzed the network by approaching it from the perspective of positive psychology capital of prospective entrepreneurs in order to enhance the effectiveness of support programs for start-ups by the government, public institutions or universities in the future.

• The korean journal of orthodontics
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• v.52 no.1
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• pp.3-19
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• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.56-61
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• 2022

In this paper, we classify and present AI algorithms and natural language processing methods used in chatbots. A framework that can be used to implement a chatbot is also described. A chatbot is a system with a structure that interprets the input string by constructing the user interface in a conversational manner and selects an appropriate answer to the input string from the learned data and outputs it. However, training is required to generate an appropriate set of answers to a question and hardware with considerable computational power is required. Therefore, there is a limit to the practice of not only developing companies but also students learning AI development. Currently, chatbots are replacing the existing traditional tasks, and a practice course to understand and implement the system is required. RNN and Char-CNN are used to increase the accuracy of answering questions by learning unstructured data by applying technologies such as deep learning beyond the level of responding only to standardized data. In order to implement a chatbot, it is necessary to understand such a theory. In addition, the students presented examples of implementation of the entire system by utilizing the methods that can be used for coding education and the platform where existing developers and students can implement chatbots.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.4
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• pp.307-326
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• 2021

Real-time sea level observations from tide gauges include missing and erroneous values. Classification as abnormal values can be done for the latter by the quality control procedure. Although the 3𝜎 (three standard deviations) rule has been applied in general to eliminate them, it is difficult to apply it to the sea-level data where extreme values can exist due to weather events, etc., or where erroneous values can exist even within the 3𝜎 range. An artificial intelligence model set designed in this study consists of non-annotated recurrent neural networks and ensemble techniques that do not require pre-labeling of the abnormal values. The developed model can identify an erroneous value less than 20 minutes of tide gauge recording an abnormal sea level. The validated model well separates normal and abnormal values during normal times and weather events. It was also confirmed that abnormal values can be detected even in the period of years when the sea level data have not been used for training. The artificial neural network algorithm utilized in this study is not limited to the coastal sea level, and hence it can be extended to the detection model of erroneous values in various oceanic and atmospheric data.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.1-12
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• 2022

Traditional methods of measuring corneal ulcers were difficult to present objective basis for diagnosis because of the subjective judgment of the medical staff through photographs taken with special equipment. In this paper, we propose a method to detect the ulcer area on a pixel basis in corneal ulcer images using a semantic segmentation model. In order to solve this problem, we performed the experiment to detect the ulcer area based on the DeepLab model which has the highest performance in semantic segmentation model. For the experiment, the training and test data were selected and the backbone network of DeepLab model which set as Xception and ResNet, respectively were evaluated and compared the performances. We used Dice similarity coefficient and IoU value as an indicator to evaluate the performances. Experimental results show that when 'crop & resized' images are added to the dataset, it segment the ulcer area with an average accuracy about 93% of Dice similarity coefficient on the DeepLab model with ResNet101 as the backbone network. This study shows that the semantic segmentation model used for object detection also has an ability to make significant results when classifying objects with irregular shapes such as corneal ulcers. Ultimately, we will perform the extension of datasets and experiment with adaptive learning methods through future studies so that they can be implemented in real medical diagnosis environment.