• ETRI Journal
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• 제43권1호
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3897-3908
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• 2022

High-fidelity nuclear data libraries and neutronics simulation tools are essential for the development of fast reactors. The IAEA coordinated research project on "Neutronics Benchmark of CEFR Start-Up Tests" offers valuable data for the qualification of nuclear data libraries and neutronics codes. This paper focuses on the verification and validation of the CEFR start-up modelling using OpenMC Monte-Carlo code against the experimental measurements. The OpenMC simulation results agree well with the measurements in criticality, control rod worth, sodium void reactivity, temperature reactivity, subassembly swap reactivity, and reaction distribution. In feedback coefficient evaluations, an additional state method shows high consistency with lower uncertainty. Among 122 relative errors in the benchmark of the distribution of nuclear reaction, 104 errors are less than 10% and 84 errors are less than 5%. The results demonstrate the high reliability of OpenMC for its application in fast reactor simulations. In the companion paper, the influence of cross-section libraries is investigated using neutronics modelling in this paper.

• Nuclear Engineering and Technology
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• 제53권8호
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• pp.2445-2453
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• 2021

Since the nuclear data forms a vital component in reactor physics computations, the nuclear community needs processing codes as tools for translating the Evaluated Nuclear Data Files (ENDF) to simulate nuclear-related problems such as an ACE format that is used for MCNP. Errors, inaccuracies or discrepancies in library processing may lead to a calculation that disagrees with the experimentally measured benchmark. This paper provides an overview of the processing and preparation of ENDF/B-VIII.0 incident neutron data with NECP-Atlas and NJOY codes for implementation in the MCNP code. The resulting libraries are statistically inter-compared and tested by conducting benchmark calculations, as the mutualcomparison is a source of strong feedback for further improvements in processing procedures. The database of the benchmark experiments is based on a selection taken from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP handbook) and those proposed by Russell D. Mosteller. In general, there is quite good agreement between the NECP-Atlas1.2 and NJOY21^(1.0.0.json) results with no substantial differences, if the correct input parameters are used.

• 유통과학연구
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• 제21권3호
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• pp.61-69
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• 2023

Purpose: This study's purpose is to conduct empirical research on online comments affect Vietnamese consumers' impulsive buying in e-distribution. This study also considers affecting of browsing toward the urge to buy, and the urge to buy toward impulse buying in e-distribution. Research design, data and methodology: This study used the non-probability method to assemble data from 273 customers' online buying experiences via a Google Forms online survey. By using SmartPLS, the data were examined for reliability, convergent validity, discriminant validity of the variables, and proposed hypothesis testing. Results: The empirical study discovered that internet comments with utilitarian and hedonistic values had a positive effect on browsing, the urge to buy, and impulse purchases in e-distribution. Additionally, the result revealed that browsing had a positive influence on the urge to purchase. Likewise, the findings also disclosed that the urge to buy had a favorable effect on impulse buying. Conclusions: This study offered a thorough conceptual model of internet feedback influencing browsing, urge to buy, and impulsive purchases in e-distribution. Also, to increase impulsive buying, this study will assist e-distribution managers in concentrating on developing innovative marketing strategies and action plans that take into consideration consumers' internet reviews, browsing, and urge to buy.

• International journal of advanced smart convergence
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• 제12권2호
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• pp.159-166
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• 2023

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in labor-intensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

• 천문학회보
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• 제45권1호
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• pp.63.2-63.2
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• 2020

FLASH is a blind neutral hydrogen (HI) absorption line survey, eventually targeting about 100,000 background radio continuum sources in the entire southern sky using the full 36-antenna of the Australian Square Kilometre Array Pathfinder (ASKAP). Our primary goal is to search for associated and intervening HI absorption lines in the intermediate redshift range 0.4 < z < 1.0. The survey aims to understand the evolution of HI gas in galaxies as well as various physical mechanisms in active galactic nuclei, such as accretion and feedback processes. In this poster, we give an overview of the FLASH survey and present the preliminary results from our first 100-hrs of pilot observations. The latest survey data covers 1,000 square degrees and is ideal for validating observation and data processing in the continuous 300MHz-width low frequency ASKAP band (700-1000MHz). One of the crucial objectives of the pilot survey is to establish the analysis methodology that will be applied to upcoming large absorption surveys in the future. We discuss our data quality validation and present some detections of associated/intervening HI absorption lines. These absorption lines allow us to trace the cold gas properties of active and normal galaxies at higher redshifts where the HI emission line is too weak to be detectable.

• Structural Engineering and Mechanics
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• 제90권2호
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• 융합경영연구
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• 제12권3호
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• pp.35-42
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• 2024

Purpose This study aims to investigate the correlation of social skills and metacognition among university students participating in service-learning programs. Also by evaluating the satisfaction of college students participating in service learning, this research seeks to understand the impact of this program on learning experiences. Research design, data and methodology: The research period spans two semesters, each comprising 15 weeks, from March 2, 2023, to December 20, 2023. Detailed procedures, including planning, preparation, data collection, analysis, and organization, cover activities conducted over the course of 30 weeks. These activities encompass various stages, from initial classroom planning with designated English storybooks to reflection and feedback sessions aimed at continuous development. Data collection methods include surveys, interviews, and observations, allowing for a comprehensive examination of social skills and metacognition among participating students. Results: The results show significant correlations between social skills and metacognition, such as the correlation between knowledge and statistics (r = 0.759, p < .01), the moderate correlation between cooperation and knowledge (r = 0.532, p < .01), the moderate correlation between statistics and cooperation (r = 0.539, p < .01), and the correlation between self-regulation and assertion (r = 0.278, p < .001). The average score of the satisfaction of college students participating in service learning was 4.8 out of 5. Conclusions: This study highlights the significant role of service-learning in boosting social skills and metacognition among university students. This study enhances the academic understanding of the relationships between social skills, metacognition, and service-learning programs, contributing to the expansion of both theoretical and practical knowledge in the field.

• 공학교육연구
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• 제27권4호
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• pp.3-11
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• 2024

This study classified domestic and international systems by type, presenting their key features and examples, with the aim of outlining future directions for system development and research. AI-based learning assistance systems can be categorized into instructional-learning evaluation types and academic recommendation types, depending on their purpose. Instructional-learning evaluation types measure learners' levels through initial diagnostic assessments, provide customized learning, and offer adaptive feedback visualized based on learners' misconceptions identified through learning data. Academic recommendation types provide personalized academic pathways and a variety of information and functions to assist with overall school life, based on the big data held by schools. Based on these characteristics, future system development should clearly define the development purpose from the planning stage, considering data ethics and stability, and should not only approach from a technological perspective but also sufficiently reflect educational contexts.

• Ocean and Polar Research
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• 제34권1호
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• pp.73-83
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• 2012

Antarctica is very sensitive to climate change but the number of stations is not sufficient to accurately analyze climate change in this regoin. Model reanalysis data supplements the lack of observation and can be used as long term data to verify climate change. In this study, the 20CR (Twentieth Century Reanalysis) Project data from NCEP/NCAR and monthly mean data (temperature, solar radiation and longwave radiation) from 1871 to 2008, was used to analyze the temperature trend and change in radiation. The 20CR data was used to validate the observation data from Antarctica since 1950 and the correlation coefficients between these data were determined to be over 0.95 at all stations. The temperature increased by approximately $0.23^{\circ}C$/decade during the study period and over $0.20^{\circ}C$/decade over all of the months. This increasing trend was observed throughout the Antarctica and a slight increase was observed in the Antarctic Peninsula. In addition, solar radiation (surface) and longwave radiation (surface and top of atmosphere) trends correlated with the increase in temperature. As a result, outgoing longwave radiation at the surface is attenuated by atmospheric water vapor or clouds and radiation at the top of the atmosphere was reduced. In addition, the absorbed energy in the atmosphere increases the temperature of the atmosphere and surface, and then the heated surface emits more longwave radiation. Eventually these processes are repeated in a positive feedback loop, which results in a continuous rise in temperature.