• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Journal of the Korean Applied Science and Technology
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• v.36 no.3
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• pp.1028-1040
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• 2019

The current study was performed to investigate the magnitude of exercise-induced muscle damage (EIMD) after downhill running (DR) of different intensities and to examine the availability of muscle echo intensity as biomarkers to detect regional damage within quadriceps muscle group (QG) following DR. Healthy college-age men (n=11) were experienced twice DR sessions [$50%HR_{max}$ DR, LDR; $70%HR_{max}$ DR, HDR] separated by a 2-week wash-out period with the random order. After DR, severity of EIMD according to exercise intensity were determined by serum creatine kinase (CK) activity, muscle tenderness, and neuromuscular function indicators such as a maximal voluntary isometric contraction (MVIC) and range of motion (ROM). Transvaginal B-mode imaging had been employed to evaluate regional muscle echo intensity within QG [rectus femoris, RF; vastus lateralis, VL; vastus medialis, VM; vastus intermedius, VI]. After both DR sessions, changes in serum CK activity and muscle tenderness have tended to more increase in HDR compared to those of LDR. There was a significant interaction effect between exercise intensity during DR and the time course of serum CK activity(p<.05). However, there were no statistical differences between sessions in muscle tenderness. The time course of changes in the neuromuscular functions after DR were similar to those of regional muscle echo intensity regardless exercise intensity. Although neuromuscular function showed to decline in HDR more than those of LDR after DR, no statistical differences between sessions. In contrast, there were significant interaction effects between sessions and time course of changes in RF and VL muscle echo intensity(p<.01), but not shown in those of VI and VM. These results indicated that each muscles within the QG show different response profiles for EIMD during DR, exercise intensity influences on these responses as well. In particular, current findings suggested that muscle echo intensity derived from ultrasound imaging is capable of detecting regional muscle damage in QG following DR.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Life Science
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• v.32 no.12
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• pp.947-955
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• 2022

Silkworms, which have recently shown promise as functional health foods, show functional differences between varieties; therefore, the need for variety identification is emerging. In this study, we analyzed the whole silkworm genome to identify 10 unique silkworm varieties (Baekhwang, Baekok, Daebaek, Daebak, Daehwang, Goldensilk, Hansaeng, Joohwang, Kumkang, and Kumok) using single nucleotide polymorphisms (SNP) present in the genome as biomarkers. In addition, nine SNPs were selected to discriminate between varieties by selecting SNPs specific to each variety. We subsequently created a decision tree capable of cross-verifying each variety and classifying the varieties through sequential analysis. Restriction fragment length polymorphism (RFLP) was used for SNP867 and SNP9183 to differentiate between the varieties of Daehwang and Goldensilk and between Kumkang and Daebak, respectively. A tetra-primer amplification refractory (T-ARMS) mutation was used to analyze the remaining SNPs. As a result, we could isolate the same group or select an individual variety using the nine unique SNPs from SNP780 to SNP9183. Furthermore, nucleotide sequence analysis for the region confirmed that the alleles were identical. In conclusion, our results show that combining SNP analysis of the whole silkworm genome with the decision tree is of high value as a discriminative marker for classifying silkworm varieties.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of the Korean Applied Science and Technology
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• v.40 no.4
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• pp.674-684
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• 2023

The purpose of this study was to investigate the effect of acute listening to music on the cardiovascular reactivity to sympathoexcitation. In this crossover design study, 15 healthy adults(23.1±1.94(yrs) were randomized to either (1)acute listen to the subject's preferred music for 30 minutes and (2)sat as a time control by an experiment coordinator. After completing each trial, the cold pressor test(CPT) was conducted. Heart rate(HR) and blood pressure(BP) were measured for 4 times at baseline, during and after the CPT. Heart rate variability(HRV) were measured for 3 times at baseline, prior and after the CPT. HR and BP increased during the CPT in both trial and returned to baseline after CPT(time effect, p < .001). After CPT, brachial systolic BP reactivity to the CPT was attenuated in listening to music trial compared to control trial(p = . 008). As a result of heart rate variability(HRV), the difference values between the baseline and prior to the CPT showed a significant increase in standard deviation of the NN intervals(SDNN), total power(TP) and high frequency(HF) only in the music trial (p = .001, p = .002, p = .011). The difference value between prior to and after the CPT did not show significance. But compared with the control trial, the music trial was confirmed that SDNN, TP and HF were more activated. Therefore, listening to music alleviated anxiety and tension before the CPT, and it is estimated that it had a favorable effect on stability after the CPT. This findings showed that listening to music may have a positive effect on brachial systolic BP and HRV to sympathoexcitation.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.2
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• pp.293-300
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• 2012

This study was to investigate the unsuitable rate of the storage facilities, the changes in corrosion process over time after installation according to the status, the time to install the facilities, years elapsed after facilities installation, inspection of methods and motivation, and so on, based on the results of the inspection at the petroleum storage facilities conducted by domestic soil-relate specialized agency to derive optimal management plans which meet the status of soil contamination facilities. The results showed that the facilities more than 5 years after the initial leak test at the time of the installation need to be inspected periodically by considering costs of leak test and remediation of polluted soil. The inspection period can be decided by cost and leak test methods showing discrepancies for the results obtained from individual test whether it was direct or indirect. To compensate these matters, we suggested that the direct inspection method on regular schedule is recommended. On the other hand, the inspection can be voluntarily completed to ease burden of the results by inspection or equivalent level to this inspection method. Also, it may need improved construction supervision and performance test system to minimize the occurrence of the nature defects in installing the facilities as well as the upgrade program for the facilities during intervals of inspection period.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.464-477
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• 2013

There is a great need to develop a training program proven to change behavior and improve knowledge. The purpose of this study was to evaluate employee hygiene knowledge, hygiene practice, and cleanliness, before and after HACCP system implementation at one small-size restaurant. The efficiency of the system was analyzed using time-temperature control after implementation of u-HACCP$^{(R)}$. The employee hygiene knowledge and practices showed a significant improvement (p<0.05) after HACCP system implementation. In non-heating processes, such as seasoned lettuce, controlling the sanitation of the cooking facility and the chlorination of raw ingredients were identified as the significant CCP. Sanitizing was an important CCP because total bacteria were reduced 2~4 log CFU/g after implementation of HACCP. In bean sprouts, microbial levels decreased from 4.20 logCFU/g to 3.26 logCFU/g. There were significant correlations between hygiene knowledge, practice, and microbiological contamination. First, personnel hygiene had a significant correlation with 'total food hygiene knowledge' scores (p<0.05). Second, total food hygiene practice scores had a significant correlation (p<0.05) with improved microbiological qualities of lettuce salad. Third, concerning the assessment of microbiological quality after 1 month, there were significant (p<0.05) improvements in times of heating, and the washing and division process. On the other hand, after 2 months, microbiological was maintained, although only two categories (division process and kitchen floor) were improved. This study also investigated time-temperature control by using ubiquitous sensor networks (USN) consisting of an ubi reader (CCP thermometer), an ubi manager (tablet PC), and application software (HACCP monitoring system). The result of the temperature control before and after USN showed better thermal management (accuracy, efficiency, consistency of time control). Based on the results, strict time-temperature control could be an effective method to prevent foodborne illness.

• Journal of Chest Surgery
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• v.42 no.4
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• pp.480-486
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• 2009

Background: The Fogarty thromboembolectomy catheter technique was devised to extract distal arterial emboli and it represents a milestone for the treatment of patients with acute arterial occlusion since the 1960s. The major causes of arterial occlusion have changed from emboli of a heart origin to atherosclerosis over the past 30 years. Accordingly, questions have been raised about the effectiveness of simple Fogarty thromboembolectomy. Material and Method: During the period from March 1990 through August 2008, 156 patients who requiring Fogarty thromboembolectomy were analyzed. The patients were divided into two groups: those with simple Fogarty thromboembolectomy (Group 1, 79 patients) and those with additional vascular bypass graft surgery (Group 2, 77 patients). The duration of symptoms, the cause of thrombi, admission via the emergency room, a history of acupuncture or misdiagnosis, combined diseases, the anatomic occlusion site and the cause of death were analyzed using T-tests, cross tab tests, Chi square tests and Kaplan-Meier tests, respectively. Result: The mean age was 64$\pm$10 years in the 2 groups. The duration of symptoms (pain) in Group 1 vs Group 2 was 12$\pm$4 days vs 71$\pm$14 days (p=0.001). 50 (63%) patients in Group 1 were admitted via the emergency room vs 18 (23%) patients in Group 2 (p=0.005). Misdiagnosis and the treatment for herniated intervertebral disc or acupuncture were given to, 20 (25%) patients in Group 1 vs 30 (39%) patients in Group 2. Anticoagulation treatment before admission was performed in 22 (28%) patients in Group 1 vs 11 (14%) patients in Group 2. The causes of thrombi were heart disease in, 24 (30%) patients in Group 1 vs 6 (8%) patients in Group 2 (p=0.001), atherosclerosis in 46 (58%) patients in Group 1 vs 67 (87%) patients in Group 2 (p=0.001) and trauma in 9 (11%) patients in Group 1 vs 6 (8%) patients in Group 2. The combined diseases were cerebrovascular accident, hypertension and diabetes mellitus in 22 $\sim$ 37% of the total patients. The occlusion sites were mainly in the iliac and femoral arteries. Endarterectomy was performed in 7 (9%) patients in Group 1 vs 18 (23%) patients in Group 2 (p=0.012). Treatment was successful in 27 (34%) patients in Group 1 and in 40 (52%) patients in Group 2 (p=0.019). Reocclusion occurred in 37(47%) patients in Group 1 vs 20 (26%) patients in Group 2 (p=0.000), Amputation was done in 4 (5%) patients in Group 1 vs 12 (16%) patients in Group 2 (p=0.012) and death occurred in 10 (13%) patients (Group 1) vs 3(4%) patients (Group 2) (p=0.044). Conclusion: The recent past has shown a decline in the effectiveness of simple Fogarty thromboembolectomy with a changing pattern of acute arterial occlusion from a rheumatic heart origin to atherosclerosis. Additional bypass procedures play a role for the treatment of arterial occlusion instead of always performing simple Fogarty thromboembolectomy.