• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.32 no.2
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• pp.51-62
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• 2021

Objectives: Tic disorder is a neurodevelopmental disorder characterized by multiple involuntary movements of muscles or vocalization. Although tic symptoms subside as the patient ages, some patients suffer from significant functional impairments related to severe tic symptoms. This manuscript aimed to review the latest scientific evidences for the effect of cognitive-behavioral interventions on tic disorder. Methods: The relevant studies were identified by searching medical research databases. We focused our search on studies published between 2000 and 2020 in order to reflect the latest scientific evidence. A total of 821 articles were identified in the initial database search and 27 articles were finally included for the review after the exclusion of duplicated and irrelevant articles. Results: Behavioral therapies including habit reversal training, Comprehensive Behavioral Intervention for Tics, and exposure and response prevention were the most widely studied interventions for tic disorder and are recommended as first-line treatments for tic disorders with high confidence. Cognitive psychophysiologic approaches were also reported to be effective. Conclusion: Further studies are needed to support the future treatment of tics with low-cost and more widely available treatments, in order to ensure better treatment outcomes.

• Convergence Security Journal
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• v.20 no.5
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• pp.41-52
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• 2020

This paper implements a priority algorithm for active response to security event risk, and implements an event scheduler that performs efficient event processing based on this. According to the standards that have global standards such as CVE and CVSS, standards for scoring when security events are executed are prepared and standardized so that priorities can be more objectively set. So, based on this, we build a security event database and use it to perform scheduling. In addition, by developing and applying the security event scheduling priority algorithm according to the situation of security events in Korea, it will contribute to securing the reliability of information protection and industrial development of domestic or ganizations and companies.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Genomics & Informatics
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• v.19 no.4
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• pp.45.1-45.8
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• 2021

Brassica napus is the third most important oilseed crop in the world; however, in Korea, it is greatly affected by cold stress, limiting seed growth and production. Plants have developed specific stress responses that are generally divided into three categories: cold-stress signaling, transcriptional/post-transcriptional regulation, and stress-response mechanisms. Large numbers of functional and regulatory proteins are involved in these processes when triggered by cold stress. Here, our objective was to investigate the different genetic factors involved in the cold-stress responses of B. napus. Consequently, we treated the Korean B. napus cultivar Naehan at the 4-week stage in cold chambers under different conditions, and RNA and cDNA were obtained. An in silico analysis included 80 cold-responsive genes downloaded from the National Center for Biotechnology Information (NCBI) database. Expression levels were assessed by reverse transcription polymerase chain reaction, and 14 cold-triggered genes were identified under cold-stress conditions. The most significant genes encoded zinc-finger proteins (33.7%), followed by MYB transcription factors (7.5%). In the future, we will select genes appropriate for improving the cold tolerance of B. napus.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Earthquakes and Structures
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• v.26 no.2
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• pp.117-130
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• 2024

Probabilistic model of seismic demand is the main tool used for seismic demand estimation, which is a fundamental component of the new performance-based design method. This model seeks to mathematically relate the seismic demand parameter and the ground motion intensity measure. This study is intended to use Bayesian analysis to evaluate the accuracy of the seismic demand estimation of Steel moment resisting frames (SMRFs) through a completely Bayesian method in statistical calculations. In this study, two types of intensity measures (earthquake intensity-related indices such as magnitude and distance and intensity indices related to ground motion and spectral response including peak ground acceleration (PGA) and spectral acceleration (SA)) have been used to form the models. In addition, an extensive database consisting of sixty accelerograms was used for time-series analysis, and the target structures included five SMRFs of three, six, nine, twelve and fifteen stories. The results of this study showed that for low-rise frames, first mode spectral acceleration index is sufficient to accurately estimate demand. However, for high-rise frames, two parameters should be used to increase the accuracy. In addition, adding the product of the square of earthquake magnitude multiplied by distance to the model can significantly increase the accuracy of seismic demand estimation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.5-17
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• 2018

The standardized eddy covariance flux data processing in KoFlux has been updated, and its database has been amended accordingly. KoFlux data users have not been informed properly regarding these changes and the likely impacts on their analyses. In this paper, we have documented how the current structure of data processing in KoFlux has been established through the changes and improvements to ensure transparency, reliability and usability of the KoFlux database. Due to increasing diversity and complexity of flux site instrumentation and organization, we have re-implemented the previously ignored or simplified procedures in data processing (e.g., frequency response correction, stationarity test), and added new methods for $CH_4$ flux gap-filling and $CO_2$ flux correction and partitioning. To evaluate the effects of the changes, we processed the data measured at a flat and homogeneous paddy field (i.e., HPK) and a deciduous forest in complex and heterogeneous topography (i.e., GDK), and quantified the differences. Based on the results from our overall assessment, it is confirmed that (1) the frequency response correction (HPK: 11~18% of biases for annually integrated values, GDK: 6~10%) and the stationarity test (HPK: 4~19% of biases for annually integrated values, GDK: 9~23%) are important for quality control and (2) the minimization of the missing data and the choice of the appropriate driver (rather than the choice of the gap-filling method) are important to reduce the uncertainty in gap-filled fluxes. These results suggest the future directions for the data processing technology development to ensure the continuity of the long-term KoFlux database.

• Journal of the Earthquake Engineering Society of Korea
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• v.9 no.4 s.44
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• pp.29-42
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• 2005

Total 350 borehole profiles were selected from the database of borehole logs in Seoul, for the site-specific seismic evaluation at two 4km${\times}$4km plain areas. Equivalent-linear site response analyses for the selected 350 sites were conducted based on shear wave velocity (Vs) Profiles, which were determined from the N-Vs correlation established using borehole seismic testing results in the inland areas of Korea. Most sites were categorized as site classes C and D based on the mean Vs to 30 m in depth (Vs30) ranging from 250 to 550 m/s. The she periods of the plains in Seoul ranging between 0.1 and 0.4 sec were significantly lower than those of the western US, from which the site coefficients in Korea were derived. For plains in Seoul, the site coefficients, Fa's and Fv's specified in the Korean seismic design guide, underestimate the ground motion in short-period (0.1-0.5 sec) band and overestimate the ground motion in mid-period (0.4-2.0 sec) band, respectively, because ol the differences in the geotechnical conditions between Seoul and the western US, although the Fa's in several sites overestimate the motion due to the base Isolation effect resulted from the soft layer in soil deposit.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.292-299
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• 2018

The vehicle exterior design is the main parameter of aerodynamic wind noise, but the modification of it is nearly impossible at a proto-type stage. Therefore, it is very important to verify exterior design and estimate the correct wind noise level at the early vehicle design stages. The numerical simulations of aerodynamic wind noises around A-pillar and wiper were developed for specific vehicle exterior designs, but could not be directly used for the discussions with designers because these need complex modeling and simulation process. This study proposes new approach to A-pillar and wiper wind noise estimation at design stage using response surface methodology of modeFRONTIER, of which database is composed of PowerFLOW simulation, PowerCLAY modeling, SEA-Baced (Statistical Energy Analysis-Based) interior noise simulation, and turbulent acoustic power simulation. New design parameters are defined and their contributions are analyzed. A state-of-the-art, easy and reliable CAT (Computer Aided Test) tool for A-pillar and wiper wind noise are acquired from this study, which shows high usefulness in car development.

• Journal of the Korean Society for information Management
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• v.32 no.2
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• pp.131-152
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• 2015

This study described the results of converting RDB to RDF ontology by each of R2RML method and Non-R2RML method. This study measured the size of the converted data, the conversion time per each tuple, and the response speed to queries. The STNet, a structured terminology dictionary based on RDB, was served as a test bed for converting to RDF ontology. As a result of the converted data size, Non-R2RML method appeared to be superior to R2RML method on the number of converted triples, including its expressive diversity. For the conversion time per each tuple, Non-R2RML was a little bit more faster than R2RML, but, for the response speed to queries, both methods showed similar response speed and stable performance since more than 300 numbers of queries. On comprehensive examination it is evaluated that Non-R2RML is the more appropriate to convert the dynamic RDB system, such as the STNet in which new data are steadily accumulated, data transformation very often occurred, and relationships between data continuously changed.