• Convergence Security Journal
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• v.20 no.4
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• pp.27-34
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• 2020

Recently, the fourth industrial revolution technology has not only changed everyday life greatly through technological development, but has also become a major keyword in the establishment of defense policy. In particular, Internet of Things, cloud, big data, mobile and cybersecurity technologies, called ICBMS, were selected as core leading technologies in defense information policy along with artificial intelligence. Amid the growing importance of the fourth industrial revolution technology, research is being carried out to develop the C4I system, which is currently operated separately by the Joint Chiefs of Staff and each military, including the KJCCS, ATCIS, KNCCS and AFCCS, into an integrated system in preparation for future warfare. This is to solve the problem of reduced interoperability for joint operations, such as information exchange, by operating the C4I system for each domain. In addition, systems such as the establishment of an integrated C4I system and the U.S. military's Risk Management Framework (RMF) are essential for efficient control and safe operation of weapons systems as they are being developed into super-connected and super-intelligent systems. Therefore, in this paper, the intelligent cyber threat detection, management of users' access to information, and intelligent management and visualization of cyber threat are presented in the future C4I system based on big data/cloud.

• Korean Journal of Clinical Laboratory Science
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• v.53 no.1
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• pp.122-130
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• 2021

The types of artifacts that are observed in intraoperative neurophysiological monitoring (INM) is truly diverse. The removal of artifacts that interfere with the examination is essential. In addition, improving the quality of the examination by removing artifacts is a reflection of the competency of the examiner and is also the best way to ensure patient safety. However, if knowledge of the equipment or anesthesia in the operating room is insufficient due to lack of experience, artifacts cannot be removed even with a method appropriate to the situation. If artifacts are not separated and removed, the reading of the examination results in confusion in the operation process. This can be a fatal problem in neurosurgery that requires rapid and sophisticated procedures. In this paper, the causes of artifacts that occur during surgery are classified into electrical factors, non-electrical factors, and other factors, and a method and examination method for removing artifacts according to the specific situation is mentioned. Although the operating room environment is a very critical place to simultaneously consider various scenarios, we hope that a stable and optimal INM will play a role by knowing the types and causes of various artifacts and how to tackle them.

• Journal of dental hygiene science
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• v.21 no.4
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• pp.233-242
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• 2021

Background: The importance of infection with COVID-19 is being emphasized in dentistry with high risks such as aerosols. The purpose of this study is to investigate the knowledge and practice of infection control, stress and coping, and turnover of dental hygienists. Methods: Questionnaire was conducted knowledge and practice of infection control, occupational stress and coping, turnover. Survey data was investigated about 149 dental hygienists from February to March 2021 Data were analyzed t-test, ANOVA, Pearson's correlation using statistical programs of PASW Statistics ver. 21.0. Results: Regarding occupational stress, relationship conflict was higher in the group with less than 2 years of experience (p<0.05). Job anxiety, organizational system, inadequate compensation, and workplace culture were highly surveyed in the 3 to 5 year of experience. The group with more than 6 years of experience had the highest perception of lack of job autonomy (p<0.05). The group with higher knowledge of infection control had lower mean inappropriate rewards and stress (p<0.05). The group with high infection control performance had a lower average in items such as job instability, organizational system, inadequate compensation, workplace culture, and stress. And problem-focused coping ability was found to be high (p<0.05). Infection control knowledge and performance were positively correlated (r=0.251, p<0.01), infection control practice and stress were negatively correlated (r=-0.264, p<0.01), and stress and emotional coping were positively correlated (r=0.367, p<0.01). Stress was positively correlated with turnover rate (r=0.549, p<0.01). Conclusion: Infection control training was required to reduce occupational stress. Occupational stress was highly correlated with turnover, a holistic and systemic organizational operation and improvement of the quality of medical care were required to reduce stress.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.697-705
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• 2021

Currently, roadway operation agencies provide interior zones of tunnels with emergency information including crash, fire, and vehicles' stop, through state-of-the-art technologies such as variable message signs and radio-based broadcast systems. However, when coping with an emergency in tunnel interior zones, such information could be too late for drivers to access. A radio-based emergency alert system at the access zones of freeway tunnels, on the other hand,could be a good alternative for solving this problem. Therefore, the objective of this study is to assess user acceptability of such an alternative system. To carry out this study, an online survey was conducted on 762 drivers, and the survey results were analyzed using a structural equation modeling to identify factors affecting acceptability of the proposed system. As a result, driver characteristics such as age group, driving frequency, and driving career, utilization of conventional traffic information, and usefulness of conventional traffic information have a positive impact on acceptability. It is expected that the findings of the study will be a basis to effectively address and deploy a new emergency alert system at the access zones of freeway tunnels.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.255-276
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• 2021

The purpose of this study is to analyze the perceptions, demands, operation problems of teacher librarians for the implementation of the high school credit system. 153 teacher librarians working in high schools participated in the online survey, and statistical analysis was conducted Teacher librarians had a high understanding of the high school credit system, and the demand for elective courses management was high. In particular, teacher librarians working at research schools had a higher understanding of the high school credit system and the operating foundation than those in general schools. Lack of awareness of the educational role of students and teachers toward teacher librarians was recognized as the biggest problem in the elective course management and there was no difference according to the background variables of the teacher librarian as gender, region, experience. On the basis of this result, it is suggested and discussed that developing high school credit system training program for teacher librarians, opening of elective courses related to reading, media, and information literacy skills and developing textbook, and instituting mandatory completion courses related to reading, media, and information literacy skills in pre-teacher training course.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.125-131
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• 2022

Recently, the prevalence of remotely managed patient care systems in medical institutions is increasing due to COVID-19. In particular, in the case of fluid monitoring, hospitals are considering introducing it as a system that can reduce patient safety and nurses' work. There are two products under development: a load cell method that measures weight and a method that detects drops of sap by infrared sensing. Although each product has differences in operation principle, sensor type, size, usage, and price, medical institutions are highly interested in the accuracy of the data obtained.In this study, two prototypes with different sensor methods were manufactured and the total amount of infusion per hour was measured to test the accuracy, which is the core of the infusion monitoring device. In addition, when there was an external movement, the change in the measured value of the sap was tested to evaluate the accuracy according to the measurement method. As a result of the experiment, there was a difference of less than 5% in the measurement value error of the two devices, and the load cell method showed a difference in the low-capacity measurement value and the infrared method in the high-capacity measurement value. As a result of this experiment, there was little difference in accuracy according to the sensor method of the infusion monitoring device, and it is considered that there is no problem in accuracy when used in a medical institution.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.145-158
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• 2022

Although the rapid development of technology has led to a swift increase in the number of companies using social networking services (SNS), it will not be accurate to say that they have fully "utilized" the functionality of SNS simply by "using" these services. Therefore, this study aims to increase the convenience of using digital technology and help SNS users in extending the functionality of these services beyond their regular use and thus, revitalize the field by increasing the service providers' efficiency. In this study, the Facebook usage status of a graduate school newspaper from an undisclosed university in Seoul was analyzed from February to December, 2021 using the participant observation method. The results of the study revealed the following: First, it is necessary to diversify the subject and type of content to ensure a continuous supply of quality content; Second, there is a need to examine the user categories and characteristics by utilizing SNS functionalities such as, the target reports and insights, and based on this, supply content that meets the needs of the users; Third, to resolve the problem of low levels of user participation and an inactive Facebook account, it is necessary to mobilize new marketing tools like online events. The significance of this study is that it confronts the real problems faced by some companies that cannot keep pace with market changes in a digital environment, identifies failure factors, and proposes solutions to them.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.47-56
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• 2022

The safe driving reward system aims to reduce the loss of life and property by reducing the occurrence of accidents by motivating safe driving and encouraging active participation by providing direct reward to vehicle drivers who have performed safe driving. In the case of the existing digital tachograph, the goal is to limit dangerous driving by recording the driving status of the vehicle whereas the safe driving reward system is a support measure to increase the effect of accident prevention and induces safe driving with financial reward when safe driving is performed. In other words, in an area where accidents due to speeding are high, direct reward is provided to motivate safe driving to prevent traffic accidents when safe driving instructions such as speed compliance, maintaining distance between vehicles, and driving in designated lanes are performed. Since these safe operation data and reward histories must be managed transparently and safely, the reward evidences and histories were constructed using the closed blockchain Hyperledger Fabric. However, while transparency and safety are guaranteed in the blockchain system, low data processing speed is a problem. In this study, the sequential block generation speed was as low as 10 TPS(transaction per second), and as a result of applying the acceleration function a high-performance network of 1,000 TPS or more was implemented.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.