• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.45-56
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• 2023

In stream ecosystem assessment, RIVPACS, which makes a simple but clear evaluation based on macroinvertebrate community, is widely used. In this study, a preliminary study was conducted to develop a RIVPACS-type model suitable for Korean streams nationwide. Reference streams were classified into two types(upstream and downstream), and a prediction model for macroinvertebrates was developed based on each family. A model for upstream was divided into 7 (train): 3 (test), and that for downstream was made using a leave-one-out method. Variables for the models were selected by non-metric multidimensional scaling, and seven variables were chosen, including elevation, slope, annual average temperature, stream width, forest ratio in land use, riffle ratio in hydrological characteristics, and boulder ratio in substrate composition. Stream order classified 3,224 sites as upstream and downstream, and community compositions of sites were predicted. The prediction was conducted for 30 macroinvertebrate families. Expected (E) and observed fauna (O) were compared using an ASPT biotic index, which is computed by dividing the BMWPK score into the number of families in a community. EQR values (i.e. O/E) for ASPT were used to assess stream condition. Lastly, we compared EQR to BMI, an index that is commonly used in the assessment. In the results, the average observed ASPT was 4.82 (±2.04 SD) and the expected one was 6.30 (±0.79 SD), and the expected ASPT was higher than the observed one. In the comparison between EQR and BMI index, EQR generally showed a higher value than the BMI index.

• The Korean Journal of Nuclear Medicine Technology
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• v.15 no.1
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• pp.51-57
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• 2011

Purpose: The aim of this study is to identify clinical usefulness of Wide Beam Reconstruction (WBR) which is called Xpress.cardiac$^{TM}$ to confirm the agreement between segmental perfusion and regional wall motion in myocardium compared to conventional OSEM method. Materials and Methods: Subjects were separated two groups. First group was composed of 20 normal control group. Second group was composed of 10 patients (abnormal group) who had coronary artery disease. Subjects underwent myocardial perfusion SPECT ($^{201}Tl$ rest and $^{99m}Tc$-MIBI stress). Image acquisition and reconstruction were that rest stage was each step per 30, 15 seconds and stress stage was each step per 25, 13 seconds, OSEM and WBR methods were applied. Segmental perfusion and regional wall motion were applied 20-segment model of QPS, QGS algorithm in AutoQuant. Status of perfusion was composed of 5 point scoring system (0=normal, 1=mild, 2=moderate, 3=severe hypokinesia, 4=dyskinesia). Status of regional wall motion was also composed of 5 point scoring (0=normal, 1=mild, 2=moderate, 3=severe hypokinesia, 4=dyskinesia). We evaluated the agreement between conventional OSEM and WBR through automatic quantification value. Results: The agreement of rest segmental perfusion between conventional OSEM and WBR in normal patients was 99% (396/400, k=0.662, p<0.0001) and one of rest regional wall motion was 83.8% (335/400, k=0.283), the agreement of stress segmental perfusion was 95.8%(383/400, k=0.656), one of stress regional wall motion was 87.3% (349/400, k=0.390). The match rate of rest segmental perfusion in abnormal patients was 83% (166/200, k=0.605, p<0.0001) and one of rest regional wall motion was 55.5% (111/200, k=0.385), the agreement of stress segmental perfusion was 79.5% (159/200, k=0.682), one of stress regional wall motion was 63.5% (127/200, k=0.486). Conclusion: Compared to conventional OSEM, WBR method had a good agreement of segmental perfusion in myocardium in normal and abnormal groups. However regional wall motion showed meaningful low agreement. Although WBR offers high resolution and contrast ratio, it is not useful method for gated myocardial perfusion SPECT.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.