• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1009-1014
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• 2023

Comprehensive observations of the Euler method and the Lagrangian method were performed in order to obtain high-resolution observation data in space and time for the complex environment of new city. The two radiosondes, which measure meteorological parameters using Lagrangian methods, produced air pressure, wind speed and wind direction. They were generally consistent with each other even if the observation points or times were different. The temperature measured by the sensor exposed to the air during the day was relatively high as the altitude increased due to the influence of solar radiation. The temporal difference in wind direction and speed was found in the comparison of Euler's wind profiler data with radiosonde data. When the wind field is horizontally in homogeneous, this result implies the need to consider the advection component to compare the data of the two observation methods. In this study, a method of using observation data at different times for each altitude section depending on the observation period of the Euler method is proposed to effectively compare the data of the two observation methods.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.

• The Journal of the Acoustical Society of Korea
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• v.28 no.6
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• pp.572-577
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• 2009

Large corpus-based concatenating Text-to-Speech (TTS) systems can generate natural synthetic speech without additional signal processing. To prune the redundant speech segments in a large speech segment DB, we can utilize a decision-tree based triphone clustering algorithm widely used in speech recognition area. But, the conventional methods have problems in representing the acoustic transitional characteristics of the phones and in applying context questions with hierarchic priority. In this paper, we propose a new clustering algorithm to downsize the speech DB. Firstly, three 13th order MFCC vectors from first, medial, and final frame of a phone are combined into a 39 dimensional vector to represent the transitional characteristics of a phone. And then the hierarchically grouped three question sets are used to construct the triphone trees. For the performance test, we used DTW algorithm to calculate the acoustic similarity between the target triphone and the triphone from the tree search result. Experimental results show that the proposed method can reduce the size of speech DB by 23% and select better phones with higher acoustic similarity. Therefore the proposed method can be applied to make a small sized TTS.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.221-228
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• 2008

To estimate array shape with reference sources in SONAR systems, nearfield signal modeling is required for the reference sources near a towed array. Array shape estimation method based on the nearfield signal modeling generally exploits the spatial covariance matrix of the received reference sources. Among those method, nearfield eigenvector method uses the eigenvector corresponding to the maximum eigenvalue as a steering vector of the reference source. In this paper, we propose a simplified subspace fitting method based on the nearfield signal modeling with spherical wave modeling. Furthermore, we analyze performance of the array shape estimation methods based on the nearfield signal modeling for various environments. The results of the numerical experiments indicate that the simplified subspace fitting method and the nearfield eigenvector method with single reference source shows almost similar performance. Furthermore, the simplified subspace fitting method with 2 reference sources consistently estimates the shape of the array regardless of the incident angle of the reference sources, whereas the nearfield eigenvector method cannot apply for the case of 2 reference sources.

• Clinical and Experimental Vaccine Research
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• v.12 no.1
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• pp.25-31
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• 2023

Purpose: Since late 2019, the novel coronavirus disease has been a global concern, and alongside preventive strategies, including social distancing and personal hygiene, vaccination is now the primary hope for controlling the pandemic. Sputnik V is an adenovirus vector vaccine used against coronavirus disease 2019 (COVID-19) among Iranian health care providers, and there is a lack of information regarding the Adverse Events Following Immunisation (AEFI) by Sputnik V among the Iranian population. The present study aimed to evaluate AEFI by Sputnik V vaccine among Iranian population. Materials and Methods: Every member of the Islamic Republic of Iran Medical Council received their first dose of the Sputnik V vaccine in Mashhad (Iran) and was referred to receive their second dose enrolled in the present study and asked to fill an English language checklist asking about development of any AEFI following immunization with the first dose of Sputnik V vaccine. Results: A total number of 1,347 with a mean±standard deviation age of 56.2±9.6 years filled the checklist. Most of the participants were male (838 [62.2%]). The present study demonstrated that immunization with the first dose of Sputnik V results in at least one AEFI in 32.8% of the Iranian medical council members. Most of the AEFI was related to musculoskeletal symptoms, including myalgia. By considering the age of 55 years as a cut-off point, individuals younger than 55 had a higher rate of AEFI (41.3% vs. 22.5%, p=0.0001). Male gender, use of analgesics, beta-blockers, and previous COVID-19 infection have a lower chance of developing AEFI (p<0.05). Conclusion: The present study demonstrated that most of the AEFI was related to musculoskeletal symptoms, including myalgia, and older individuals, male gender and those receiving analgesics and beta-blockers were less likely to develop AEFI following immunization with the first dose of Sputnik V.

• IMMUNE NETWORK
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• v.22 no.2
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• pp.16.1-16.20
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• 2022

The gastrointestinal tract is the first organ directly affected by fasting. However, little is known about how fasting influences the intestinal immune system. Intestinal dendritic cells (DCs) capture antigens, migrate to secondary lymphoid organs, and provoke adaptive immune responses. We evaluated the changes of intestinal DCs in mice with short-term fasting and their effects on protective immunity against Listeria monocytogenes (LM). Fasting induced an increased number of CD103⁺CD11b^- DCs in both small intestinal lamina propria (SILP) and mesenteric lymph nodes (mLN). The SILP CD103⁺CD11b^- DCs showed proliferation and migration, coincident with increased levels of GM-CSF and C-C chemokine receptor type 7, respectively. At 24 h post-infection with LM, there was a significant reduction in the bacterial burden in the spleen, liver, and mLN of the short-term-fasted mice compared to those fed ad libitum. Also, short-term-fasted mice showed increased survival after LM infection compared with ad libitum-fed mice. It could be that significantly high TGF-β2 and Aldh1a2 expression in CD103⁺CD11b^- DCs in mice infected with LM might affect to increase of Foxp3⁺ regulatory T cells. Changes of major subset of DCs from CD103⁺ to CD103^- may induce the increase of IFN-γ-producing cells with forming Th1-biased environment. Therefore, the short-term fasting affects protection against LM infection by changing major subset of intestinal DCs from tolerogenic to Th1 immunogenic.

• IMMUNE NETWORK
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• v.22 no.3
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• pp.24.1-24.12
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• 2022

Coronavirus disease 2019 (COVID-19) vaccination in immunocompromised, especially transplant recipients, may induce a weaker immune response. But there are limited data on the immune response after COVID-19 vaccination in liver transplant (LT) recipients, especially on the comparison of Ab responses after different vaccine platforms between mRNA and adenoviral vector vaccines. Thus, we conducted a prospective study on LT recipients who received two doses of the ChAdOx1 nCoV-19 (ChAdOx1), mRNA-1273, or BNT162b2 vaccines compared with healthy healthcare workers (HCWs). SARS-CoV-2 S1-specific IgG Ab titers were measured using ELISA. Overall, 89 LT recipients (ChAdOx1, n=16 [18%]) or mRNA vaccines (mRNA-1273 vaccine, n=23 [26%]; BNT162b2 vaccine, n=50 [56%]) received 3 different vaccines. Of them, 16 (18%) had a positive Ab response after one dose of COVID-19 vaccine and 62 (73%) after 2 doses. However, the median Ab titer after two doses of mRNA vaccines was significantly higher (44.6 IU/ml) than after two doses of ChAdOx1 (19.2 IU/ml, p=0.04). The longer time interval from transplantation was significantly associated with high Ab titers after two doses of vaccine (p=0.003). However, mycophenolic acid use was not associated with Ab titers (p=0.53). In conclusion, about 3-quarters of LT recipients had a positive Ab response after 2 doses of vaccine, and the mRNA vaccines induced higher Ab responses than the ChAdOx1 vaccine.

• Journal of Plant Biotechnology
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• v.50
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• pp.56-62
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• 2023

Transgenic lilies have been obtained using Agrobacterium tumefaciens (AGL1) with the plant scale explants, followed by DL-phosphinothricin (PPT) selection. In this study, scales of lily plants cv. "red flame" were transformed with the pCAMBIA3301 vector containing the gus gene as a reporter and the blpR gene as a selectable marker, as well as a gene of interest showing herbicide tolerance, both driven by the CaMV 35S promoter. Using a 20-minute infection time and a 5-day cultivation period, factors that optimized and demonstrated a high transformation efficiency were achieved. With these conditions, approximately 22-27% efficiency was observed for Agrobacterium-mediated transformation in lilies. After transformation with Agrobacterium, scales of lilies were transferred to MS medium without selective agents for 2 weeks. They were then placed on selection MS medium containing 5 mg/L PPT for a month of further selection and then cultured for another 4-8 weeks with a 4-week subculture regime on the same selection medium. PPT-resistant scales with shoots were successfully rooted and regenerated into plantlets after transferring into hormone-free MS medium. Also, most survived putatively transformed plantlets indicated the presence of the blpR gene by PCR analysis and showed a blue color indicating expression of the gus gene. In conclusion, when 100 scales of lily cv. "red flame" are transformed with Agrobacterium, approximately 22-27 transgenic plantlets can be produced following an optimized protocol. Therefore, this protocol can contribute to the lily breeding program in the future.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.