• Journal of Life Science
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• v.33 no.8
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• pp.663-679
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• 2023

This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.601-610
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• 2023

The comprehension of water-level behavior in rivers is essential for effective flood and river environmental management. The objective of this study is to propose a methodology that can be used by field engineers engaged in actual practice, to readily identify the characteristics of water-level behavior during flood events. To this end, a total of 45 historical water-level records from 2010 to 2022 year, which provide flood information for the flood vulnerable districts of the Han River, were obtained. A Water-level Rise Behavior Curve (WRBC) was developed and suggested to quantify the amount of water-level rise per unit time during flood. As a result, the water-level rises by more than 80% of the total rise within the first 6.2 hours, followed by a gradual rise. The time required to achieve a particular equilibrium varied depending on the area and runoff characteristics of the upstream. Furthermore, the study revealed that the WRBC provides a statistical representation of the water-level rise trend during floods, and can be effectively utilized for flood mitigation measures in waterfront spaces and irrigation facilities.

• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.385-400
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• 2019

We reviewed existing studies and proposed future research on the sustainability of short-term effects, and of the Yongin Emotion Management Training program (Y-EMT). In this study, we conducted Y-EMT to verify its effectiveness in relation to affective disorders associated with chronic schizophrenia. Thirty-five patients were assigned to the experimental group, and 35 to the control group. First, results of repeated-measures analysis supported the effectiveness of Y-EMT. Comparisons of the results on the pre-test with those for the 5-month and 1-year post-training tests showed significant differences in mean scores between the experimental and control groups. Furthermore, these differences persisted at the 2-year follow-up. Second, in the experimental group, interactions between emotional behavior and expression, self-efficacy, and changes in interpersonal relationships showed significant effects at 5 months after the program ended, and these effects continued to increase up to 1 year post-training. Taken together, the findings indicate that, to maintain the effects of therapy in patients with chronic schizophrenia, patients should undergo Y-EMT along with cognitive rehabilitation training and social skills training. Human relations training such as Y-EMT is most helpful for rehabilitation when it is paired with high levels of medication.

• Korean Journal of Social Welfare Studies
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• v.41 no.2
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• pp.5-33
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• 2010

The purpose of this study is to understand the open adoption experience of adoptive families. For this purpose, the data were collected through diverse data collection methods including in-depth interviews with adoptive families who had experiences of open adoption and analyzed using a qualitative case study approach. Data collected from six adoptive families were employed for within-case analysis and cross-case analysis. Each case was carefully examined and summarized using story-telling style in the within-case analysis and major issues for open adoption appeared in each case were described and compared one by one. In the cross-case analysis, all the cases were re-examined keeping the issues appeared in the within-case analysis in mind and eight integrated themes were emerged from it. The eight integrated themes are 'the crucial meeting', 'a clear arrangement', 'an uneasy parallel', 'the other mom', 'who are the real parents?', 'the words never to say', 'the hidden characters', and 'the center of relations or the outsider'. Based on the results of the study, the policies and practical guidelines related to open adoption were suggested. Also, the suggestions for the further studies were made to obtain more abundant information beyond the limitations of the study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.1
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• pp.23-30
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• 1993

This study was conducted to obtain the fundamental information on antioxidant component as affected by process of grain filling in different plant types of sesame. Sesamin and sesamolin as antioxidant components, oil content and seed weight were investigated for two plant types with different by branching habit. The sesamin and sesamolin contents in grains followed a pattern of increase immediately after flowering in branch type and monocapsule habit than non-branch type and tricapsule habit. But they started to decrease around 45 days after flowering, which oil content and seed weight continued to increase until maturity. The sesamin content increased quickly up from 10th to 40th day after flowering and showed almost maximum at 43th day after flowering. The sesamolin content increased quickly up from 20th to 30th day after flowering and showed almost maximum at 45th day after flowering. The oil content increased quickly up from 20th to 30th day after flowering and showed almost maximum at 47th day after flowering. The seed weight increased quickly up from 20th to 40th day after flowering and showed almost maximum at 48th day. The sesamin content, sesamolin content, oil content and seed weight showed almost maximum at from 43th to 48th day after flowering. So that this period was considered to be of physiological maturity.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.102-111
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• 2023

Automatic Music Transcription (AMT) is a task that detects and recognizes musical note events from a given audio recording. In this paper, we focus on reducing the latency of real-time AMT systems on piano music. Although neural AMT models have been adapted for real-time piano transcription, they suffer from high latency, which hinders their usefulness in interactive scenarios. To tackle this issue, we explore several techniques for reducing the intrinsic latency of a neural network for piano transcription, including reducing window and hop sizes of Fast Fourier Transformation (FFT), modifying convolutional layer's kernel size, and shifting the label in the time-axis to train the model to predict onset earlier. Our experiments demonstrate that combining these approaches can lower latency while maintaining high transcription accuracy. Specifically, our modified model achieved note F1 scores of 92.67 % and 90.51 % with latencies of 96 ms and 64 ms, respectively, compared to the baseline model's note F1 score of 93.43 % with a latency of 160 ms. This methodology has potential for training AMT models for various interactive scenarios, including providing real-time feedback for piano education.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.5-12
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• 2008

Biot proposed the frequency dependent formulation for the propagation of elastic waves in saturated media based on the coupled theory mixtures. Based on Biot theory, a special frequency called 'the characteristic frequency' contains unique information of the permeability of soils. The characteristic frequency is measured from I/Q (inverse quality factor) versus frequency curve by an acoustic sweep test, and the permeability of soils is computed from Biot equation. In this paper, laboratory tests are performed at The University of Mississippi using a large test box. The measured characteristic frequency is consistently obtained at 3500 Hz for mortar sands. The computed permeability of mortar sands based on Biot equation turned out 2.01 $10^{-4}m/sec$, while the permeability from the laboratory constant head test turned out 1.49 $10^{-4}m/sec$. This paper addresses the theoretical background and experimental procedure of this technique.

• Knowledge Management Research
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• v.23 no.4
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• pp.275-294
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• 2022

In recent years, with the continuous integrative development of e-commerce and social media, social commerce, as a trust-centered social transaction mode, has become an important performance form of e-commerce. The good experience of online community and abundant user-generated content (UGC) attract more and more users and businesses to participate in the community contribution. In this context, the cost of accessing information is continuously decreasing, which not only makes the purchase process more concise and efficient, but also greatly increases the possibility of consumers' impulsive consumption. However, there are very few empirical studies on the internal influencing mechanism of consumers' impulsive consumption based on the characteristics of UGC for social commerce. In view of this, based on S-O-R model, this study constructs a model of consumers' impulsive consumption in the context of social commerce from the characteristics of UGC, with perceived risk as the mediating variable and tie strength as the moderating variable. The results show that content authenticity, content usefulness, and content valence of UGC have significant negative impacts on consumers' risk perception in the process of purchase decision-making, and consumers' perceived risk has a significant negative impact on consumers' impulsive consumption. Meanwhile, the tie strength between UGC producer and UGC receiver plays a moderating role between content usefulness and perceived risk, as well as between perceived risk and impulsive consumption. Finally, combined with the above findings, this study provides effective suggestions for relevant participants in social commerce in terms of business management.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.455-477
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• 2022

The purpose of this study is to examine the perception of RDA by university library librarians and the level of perception of emotions, familiarity, understanding, introduction, obstacles, and education, and to secure basic data to improve awareness. The data needed for the study were obtained through a survey of 76 librarians in 4-year university libraries. As a result of the survey, 67.1% of librarians had listened to RDA, and school classes were the highest at 51.0% of cognitive means. Second, librarians perceived that they were more positive emotions than negative emotions about RDA, and that they were not familiar with RDA-related principles or concepts. In particular, it was recognized that the RDA itself and FRAD were not more familiar. The librarian recognized that they understood RDA-related principles or concepts at a normal level, and in particular, recognized that they did not further understand the authority and encoding. Third, librarians recognized the introduction of RDA at a slightly higher level than usual, but among the questions, 'RDA replaces AACR2' was recognized at a normal level. The librarian recognized the obstacles to the introduction of RDA at a higher level than usual, and in particular, the lack of education related to RDA was highly recognized. Fourth, librarians recognized the need for RDA-related education at a high level, especially the use of RDA Toolkit, changes in RDA records, differences between RDA and AACR2, and the need for RDA and MARC21-related education. In this study, based on the research results, a plan to improve librarian awareness of RDA was proposed.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.