• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of the Korean Society of Child Welfare
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• no.58
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• pp.205-229
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• 2017

The purpose of this study was to examine gender differences in the impact of exposure to peer delinquency among elementary school-age students and the mediating effects of aggression. The study utilized 458 cases (220 male students, 238 female students) of data from the 2015 Korea Welfare Panel Study (KoWePS) conducted by the Korea Institute for Health and Social Affairs (KIHASA). The theoretical frameworks used in this study included Bandura's social learning theory, Akers' social learning theory, and Sutherland's differential association theory. The findings were as follows. First, there was no statistically significant effect on peer group's delinquency experience overall, aggression, and delinquency experience by gender. Second, male students' delinquency experience of their peer group had a statistically significant effect on their delinquency, however, this was not true for female students. Third, in the case of male students, aggression was found to mediate the relationship between peer group delinquency experience and their own delinquency, but not for female students. From these findings, we suggest a practical and policy-driven intervention plan, focusing on reducing the contact frequency of delinquency experience and aggression, The purpose of this study was to examine gender differences in the impact of exposure to peer delinquency among elementary school-age students and the mediating effects of aggression. The study utilized 458 cases (220 male students, 238 female students) of data from the 2015 Korea Welfare Panel Study (KoWePS) conducted by the Korea Institute for Health and Social Affairs (KIHASA). The theoretical frameworks used in this study included Bandura's social learning theory, Akers' social learning theory, and Sutherland's differential association theory. The findings were as follows. First, there was no statistically significant effect on peer group's delinquency experience overall, aggression, and delinquency experience by gender. Second, male students'delinquency experience of their peer group had a statistically significant effect on their delinquency, however, this was not true for female students. Third, in the case of male students, aggression was found to mediate the relationship between peer group delinquency experience and their own delinquency, but not for female students. From these findings, we suggest a practical and policy-driven intervention plan, focusing on reducing the contact frequency of delinquency experience and aggression, which was found to adversely affect elementary school students' delinquency.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.

• Journal of Preventive Medicine and Public Health
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• v.51 no.5
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• pp.257-262
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• 2018

Objectives: Smoking cessation decreases morbidity and mortality due to chronic obstructive pulmonary disease (COPD). Pharmacotherapy for smoking cessation is highly effective. However, the optimal prescription rate of smoking cessation medications among smokers with COPD has not been systemically studied. The purpose of this study was to estimate the national prescription rates of smoking cessation medications among smokers with COPD and to examine any disparities therein. Methods: We conducted a retrospective study using National Ambulatory Medical Care Survey data from 2007 to 2012. We estimated the national prescription rate for any smoking cessation medication (varenicline, bupropion, and nicotine replacement therapy) each year. Multiple survey logistic regression was performed to characterize the effects of demographic variables and comorbidities on prescriptions. Results: The average prescription rate of any smoking cessation medication over 5 years was 3.64%. The prescription rate declined each year, except for a slight increase in 2012: 9.91% in 2007, 4.47% in 2008, 2.42% in 2009, 1.88% in 2010, 1.46% in 2011, and 3.67% in 2012. Hispanic race and depression were associated with higher prescription rates (odds ratio [OR], 5.15; 95% confidence interval [CI], 1.59 to 16.67 and OR, 2.64; 95% CI, 1.26 to 5.51, respectively). There were no significant differences according to insurance, location of the physician, or other comorbidities. The high OR among Hispanic population and those with depression was driven by the high prescription rate of bupropion. Conclusions: The prescription rate of smoking cessation medications among smokers with COPD remained low throughout the study period. Further studies are necessary to identify barriers and to develop strategies to overcome them.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.118.1-118.1
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• 2012

In situ observations from the Wind spacecraft that statistically analyzed the solar wind proton at 1 AU has indicated that the measured proton temperature anisotropies seems to be regulated by the oblique instabilities (the mirror and oblique firehose). This result is in contradiction with the prediction of linear kinetic theory that the ion-cyclotron (for ${\beta}_{\parallel}$ < 2) and parallel firehose (for ${\beta}_{\parallel}$ <10) would dominate over the oblique instabilities. Various kinds of physical mechanisms have been suggested to explain this disagreement between the observations and linear theory. All of the suggestions consider the solar wind as a unoform magnetized plasma. However the real space environment is replete with the intermediate spatio-temporal scale variations associated with various physical quantities, such as the magnetic field intensity and the solar wind density. In this paper we present that the pervasive intermediate-scale temporal variation of the local magnetic field intensity can lead to the modification of the proton temperature anisotropy versus beta inverse correlation for temperature-anisotropy-driven instabilities. By means of quasilinear kinetic theory involving such temporal variation, we construct the simulated solar wind proton data distribution associated the magnetic fluctuations in (${\beta}_{\parallel}$, $T_{\perp}/T_{\parallel}$) space. It is shown that the theoretically simulated proton distribution and a general trend of the enhanced fluctuations bounded by the oblique instabilities are consistent with in situ observations. Furthermore, the measure magnetic compressibility can be accounted for by the magnetic spectral signatures of the unstable modes.

• Biomolecules & Therapeutics
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• v.30 no.4
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• pp.368-379
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• 2022

Hyaluronic acid (HA), a ligand of CD44, accumulates in some types of tumors and is responsible for tumor progression. The nuclear factor erythroid 2-like 2 (NRF2) regulates cytoprotective genes and drug transporters, which promotes therapy resistance in tumors. Previously, we showed that high levels of CD44 are associated with NRF2 activation in cancer stem like-cells. Herein, we demonstrate that HA production was increased in doxorubicin-resistant breast cancer MCF7 cells (MCF7-DR) via the upregulation of HA synthase-2 (HAS2). HA incubation increased NRF2, aldo-keto reductase 1C1 (AKR1C1), and multidrug resistance gene 1 (MDR1) levels. Silencing of HAS2 or CD44 suppressed NRF2 signaling in MCF7-DR, which was accompanied by increased doxorubicin sensitivity. The treatment with a HAS2 inhibitor, 4-methylumbelliferone (4-MU), decreased NRF2, AKR1C1, and MDR1 levels in MCF7-DR. Subsequently, 4-MU treatment inhibited sphere formation and doxorubicin resistance in MCF7-DR. The Cancer Genome Atlas (TCGA) data analysis across 32 types of tumors indicates the amplification of HAS2 gene is a common genetic alteration and is negatively correlated with the overall survival rate. In addition, high HAS2 mRNA levels are associated with increased NRF2 signaling and poor clinical outcome in breast cancer patients. Collectively, these indicate that HAS2 elevation contributes to chemoresistance and sphere formation capacity of drug-resistant MCF7 cells by activating CD44/NRF2 signaling, suggesting a potential benefit of HAS2 inhibition.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.170-178
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• 2022

A high-performing Earned Value Management System (EVMS) can influence project success and help stakeholders meet project objectives. Although EVMS processes are well-supported by technical guidelines and standards, project managers often face challenges related to the project culture, team, resources, and business practices that make up the project environment within which an EVMS is being used. A comprehensive literature review revealed a lack of a data-driven and consistent assessment frameworks that can gauge the environment surrounding EVMS implementation. This paper will discuss the EVMS environment of construction and environmental projects, and examine its impact on cost performance. The authors used a multi-method approach to identify 27 environment factors that make up the EVMS environment, assessing them on 18 construction and environmental projects worth over $2 billion of total cost. Research methods employed include: (1) a literature review of more than 300 references; (2) a survey of 294 respondents; and (3) remote research charrettes with more than 60 participating expert practitioners. Culture (one of the identified environment categories) was found to be relatively more important in terms of its impact on the EVMS environment, followed by people, practices, and resources. These exploratory results show statistically significant differences in cost performance between completed projects with either a good or poor environment, for the sample projects. Key environment factors are outlined, and guidance is provided to practitioners around how to set up an effective EVMS environment in a construction or environmental project to inform decision-making and support achieving the project cost objectives successfully.

• Journal of The Korean Association For Science Education
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• v.39 no.4
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• pp.511-523
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• 2019

In this age of the $4^{th}$ industrial revolution, we, science educators, are giving more light on students' agentic behavior in the process of educating future scientist. This study, with the analytic lens of epistemic agency, explores the key stages of a student-driven R&E program rather than the scientist-led R&E program. It also examines to understand the emergence of students' epistemic agency in each stage of R&E. Data from participant observation for 18 months and in-depth interviews were collected and analyzed with the constant comparative method of grounded theory. This study identifies and describes five key stages of student-driven R&E: The stage of exploring research theme, designing research, performing lab activity, interpreting results, and communicating research. It also finds that (a) students' epistemic agency emerged with the constant interactions with the R&E structure; (b) students' epistemic agency has deep relations with the epistemic beliefs of the students; (c) students positioned themselves as decision-makers in the R&E practice; (d) the redistributed power and authority of the R&E contributed to the emergence of students' epistemic agency.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.265-277
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• 2013

The study aimed to figure factors affecting employment needs of people with mental disabilities as the employment needs may differ according to income and occupational ability among people with mental disabilities who are classified economically inactive population. Study subjects included 298 economically inactive populations among registered people with mental disability. And the data from 2008-2010 Panel Data of Employment of Person with Disability were analyzed with logistic regression analysis. The result of the study is as follows. It has been found that interested in vocational education of mentally disabled when the graduated from junior high school, the types of intellectual disability, hope education participation rate was high. And then, Mental disabilities with employment needs, male, head of household, when the graduated from junior high school, when ability to increase physical activity and mental disabilities type of autism spectrum disorder when employment desires were. Based on these results, to increase the economic participation of people with mental disabilities, provide vocational rehabilitation services that reflect the needs of people with mental disabilities and employer-driven professional activity is proposed.