• Journal of Korean Library and Information Science Society
• /
• v.23
• /
• pp.1-27
• /
• 1995

The purpose of this study is twofold: to investigate into general characteristics of the networks in Korea as a new information technology and to discuss general directions of development of the use of the Internet. This study is designed to achieve the purpose by gathering and analysing data related to the use of Internet of librarians those who work in public libraries and research and development libraries and university libraries. The major conclusions made in this study is summarized as follows. (1) From this survey, received detailed response from 69 librarians, the majority (42) from research and development libraries. The majority (56) were from Library and Information Science subject area, half of them (37) hold advanced degrees. (2) Majority (40) have accessed Internet for one year or less, 9(17%) respondents for two years, 17(32%) spend every day Internet related activity. (3) 44.9% of the respondents taught themselves. 28.9% learned informally from a colleague. Formal training from a single one-hour class to more structured learning was available to 30.4%. (4) The most common reason respondents use the Internet are to access remote database searching(73.9%), to communicate with colleagues and friends and electronic mail(52.2%), to transfer files and data exchange(36.2%), to know the current research front(23.2%). They search OPACs for a variety of traditional task-related reasons(59.4%) and to see what other libraries are doing with their automated systems(31.9%). (5) Respondents for the most part use the functions : WWW (68. 1%), E-Mail(59.4%), FTP(52.2%), Gopher(34.8%), Wais(7.2%). (6) Respondents mentioned the following advantages : access to remote log-in database, an excellent and swift communications vehicle, reduced telecommunication cost, saving time. (7) Respondents mentioned the following disadvantages : low speed of communication, difficult of access to the relevant information and library materials, and shortage of database be distributed within Korea.

• Restorative Dentistry and Endodontics
• /
• v.37 no.4
• /
• pp.215-219
• /
• 2012

Objectives: The purpose of this study was to investigate the screw-in effect and torque generation depending on the size of glide path during root canal preparation. Materials and Methods: Forty Endo-Training Blocks (REF A 0177, Dentsply Maillefer) were used. They were divided into 4 groups. For groups 1, 2, 3, and 4, the glide path was established with ISO #13 Path File (Dentsply Maillefer), #15 NiTi K-file NITIFLEX (Dentsply Maillefer), modified #16 Path File (equivalent to #18), and #20 NiTi K-file NITIFLEX, respectively. The screw-in force and resultant torque were measured using a custom-made experimental apparatus while canals were instrumented with ProTaper S1 (Dentsply Maillefer) at a constant speed of 300 rpm with an automated pecking motion. A statistical analysis was performed using one-way analysis of variance and the Duncan post hoc comparison test. Results: Group 4 showed lowest screw-in effect ($2.796{\pm}0.134$) among the groups (p < 0.05). Torque was inversely proportional to the glide path of each group. In #20 glide path group, the screw-in effect and torque decreased at the last 1 mm from the apical terminus. However, in the other groups, the decrease of the screw-in effect and torque did not occur in the last 1 mm from the apical terminus. Conclusions: The establishment of a larger glide path before NiTi rotary instrumentation appears to be appropriate for safely shaping the canal. It is recommended to establish #20 glide path with NiTi file when using ProTaper NiTi rotary instruments system safely.

• Radiation Oncology Journal
• /
• v.31 no.1
• /
• pp.41-47
• /
• 2013

Purpose: To describe the early experience of analyzing variations and time trends in bladder volume of the rectal cancer patients who received bladder ultrasound scan. Materials and Methods: We identified 20 consecutive rectal cancer patients who received whole pelvic radiotherapy (RT) and bladder ultrasound scan between February and April 2012. Before simulation and during the entire course of treatment, patients were scanned with portable automated ultrasonic bladder scanner, 5 times consecutively, and the median value was reported. Then a radiation oncologist contoured the bladder inner wall shown on simulation computed tomography (CT) and calculated its volume. Results: Before simulation, the median bladder volume measured using simulation CT and bladder ultrasound scan was 427 mL (range, 74 to 1,172 mL) and 417 mL (range, 147 to 1,245 mL), respectively. There was strong linear correlation (R = 0.93, p < 0.001) between the two results. During the course of treatment, there were wide variations in the bladder volume and every time, measurements were below the baseline with statistical significance (12/16). At 6 weeks after RT, the median volume was reduced by 59.3% to 175 mL. Compared to the baseline, bladder volume was reduced by 38% or 161 mL on average every week for 6 weeks. Conclusion: To our knowledge, this study is the first to prove that there are bladder volume variations and a reduction in bladder volume in rectal cancer patients. Moreover, our results will serve as the basis for implementation of bladder training to patients receiving RT with full bladder.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.48 no.8
• /
• pp.631-640
• /
• 2020

Currently, aircraft simulator has drawn a great attention because it has significant advantages of economic, temporal, and spatial costs compared with pilot training with real aircraft. Among the components of the aircraft simulator, flight dynamic model plays a key role in simulating the flight of an actual aircraft. Hence, it is important to verify the fidelity of flight dynamic model with an automated tool. In this paper, we develop a software to automatically verify the fidelity of the flight mechanics model for the efficient development of the aircraft simulator. After designing the software structure and GUI based on the requirements derived from the fidelity verification process, the software is implemented with C # language in Window-based environment. Experimental results on CTSW models show that the developed software is effective in terms of function, performance and user convenience.

• The Journal of Society for e-Business Studies
• /
• v.22 no.2
• /
• pp.1-18
• /
• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Journal of Korean Medical Science
• /
• v.33 no.43
• /
• pp.239.1-239.12
• /
• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Journal of Digital Convergence
• /
• v.19 no.10
• /
• pp.295-301
• /
• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.37 no.3
• /
• pp.119-127
• /
• 2019

In this study, we investigated the application of deep learning to the manufacturing process of traffic and road signs which are constituting the road layer in map production with 1 / 1,000 digital topographic map. Automated classification of road traffic sign images was carried out through construction of training data for images acquired by using transfer learning which is used in image classification of deep learning. As a result of the analysis, the signs of attention, regulation, direction and assistance were irregular due to various factors such as the quality of the photographed images and sign shape, but in the case of the guide sign, the accuracy was higher than 97%. In the digital mapping, it is expected that the automatic image classification method using transfer learning will increase the utilization in data acquisition and classification of various layers including traffic safety signs.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.979-987
• /
• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.151-158
• /
• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.