• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.1
• /
• pp.7-13
• /
• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• Journal of the Korean Society of Radiology
• /
• v.18 no.3
• /
• pp.187-201
• /
• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.15 no.4
• /
• pp.81-95
• /
• 2012

This study was intended for Fagus engleriana stand in Is. Ulleung where the disturbance of vegetation has been caused by the exploitation and the increase of tourists. For the effective conservation and management on this issue, this study was conducted provide basic data. The sixteen study sites ($20{\times}20m$) were installed in the dominant Fagus engleriana stand and the base environment and vegetation were investigated. The Fagus engleriana stand was classified into two groups, The Fagus engleriana stand was classified into two groups, community A is Fagus engleriana-Sorbus amurensis and community B is Fagus engleriana-Acer pictum subsp. Mono by cluster analysis and community A were nothing signigicant by indicator species analysis. Community B were Eight species (Tsuga sieboldii, Camellia japonica, Dystaenia takesimana ect.) significant by indicator species analysis. The diameter class of 16cm to 25cm was 53.7% in population structure of Fagus engleriana, which was the highest and showed inverse J-distribution. Species diversity index (H') of investigated woody layer group ranged from 0.99 to 2.05 and that of under layer group ranged from 1.75~2.59. According to Non-metric Multidimensional Scaling (NMS) analysis, the woody layer was divided into community A developed in the region having relatively high sand content at high altitudes and community B formed at the place having relatively high clay content at low altitudes. Then this classification was significant through Multi-Response Permutation Procedures (MRPP) analysis. The distribution of understory vegetation through Detrended Correspondence Analysis (DCA) was induced by the silt content and cover degree of vegetation layer.

• The Journal of the Korea Contents Association
• /
• v.18 no.5
• /
• pp.191-214
• /
• 2018

This research empirically analyzed the selection factors and the locational selection factors of the medical service facilities according to the gradual increase of the importance of the selection factors and the locational selection factors regarding the establishments of the small- and medium-sized hospitals according to the rapid changes of the socio-economic conditions. By analyzing the priority order according to the levels of the importance of each evaluation item factor through a research related to the selection factors and the locational selection factors of the small- and medium-sized hospitals and by drawing what the important factors that have the influences on the competitiveness of the pre-existent small- and medium-sized hospitals are through the classification of the real estate locational factors and the non-locational factors, the purpose lies in utilizing them as the basic data and materials for the opening strategies of the small- and medium-sized hospitals considering the special, locational characteristics according to the important factors of the selection factors of the small- and medium-sized hospitals, regarding the medical suppliers that have been preparing, for opening the new, small- and medium-sized hospitals. Based on the results of the preceding researches and the researches on the case examples, 28 evaluation factors were arrived at in terms of the level of the medical treatment, the medical services, the accessibilities of the hospitals, the conveniences of the hospitals, and the physical environment. And, regarding the 28 detailed evaluation factors that had been collected, through the interviews with the related experts, the 5 factors of the medical level, the medical service, the expertise of the hospital, the convenience of the hospital, and the physical environment were selected as the upper class evaluation factors. And, according to each upper class, a total of 28 low-part evaluation factors were selected. Regarding the optimal evaluation factors that were selected, the optimal locational factors were selected by carrying out an AHP questionnaire survey investigation with 200 medical experts as the subjects. Regarding the AHP analysis results, similarly with the case examples of the precedent researches, the levels of the importance appeared in the order of the medical level, the medical services, the accessibility of the hospital, the physical environment, and the convenience. And the factors that were related to the facilities of a hospital appeared low. The results of this research can be applied in providing the basis for the decision-makings regarding the selections of the locations of the small- and medium-sized hospitals in the future.