• Wind and Structures
• /
• v.36 no.6
• /
• pp.393-404
• /
• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• 한국정보교육학회:학술대회논문집
• /
• 2021.08a
• /
• pp.295-301
• /
• 2021

This study tried to examine the necessity of AI literacy education to increase the effectiveness of artificial intelligence education ahead of the revision of the next revised curriculum. To this end, AI modeling classes were conducted for high school students and the necessity, content, and training period of AI literacy perceived by students in AI education were investigated through a questionnaire. The results showed that they generally agreed on the need for data utilization and data preprocessing in the AI class, and in the course of the AI class, there were many cases of difficulties due to lack of basic competencies for database use. In particular, it was observed that the understanding of the file structure for data analysis was insufficient and the understanding of the data storage format for data analysis was low. In order to overcome this part, the necessity of prior education for data processing was recognized, and there were many opinions that it is generally appropriate to go to high school at that time. As for the content elements of AI literacy, it was found that there were high demands on the content of data visualization along with data transformation, including data creation and deletion.

• Computers and Concrete
• /
• v.33 no.1
• /
• pp.55-75
• /
• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.41-58
• /
• 2020

This study addresses the issue of which factors make domestic works have turnover intention. To pursue this research issue, we utilized a public database "2017 Occupational Migration Path Survey", administerd by Korea Employment Information Service (KEIS). Decision tree method was applied to extract crucial factors influencing workers' turnover intention. They include 'the degree of matching the level of education with the level of work', 'the possibility of individual development', 'the job-related education and training', 'the promotion system', 'wage and income', 'social reputation for work' and 'the stability of employment'.

• Journal of Aerospace System Engineering
• /
• v.18 no.4
• /
• pp.18-26
• /
• 2024

This paper describes the results of a study on Generic Quad Tilt Rotor UAM aircraft, focusing on nonlinear mathematical modeling and the development of real-time simulation software. In this research, we designed a configuration for a Generic Quad Tilt Rotor eVTOL UAM aircraft based on NASA's UAM mission requirements. We modeled the aerodynamics using a database, the prop-rotor dynamics with a thrust database, and included a ground reaction and atmospheric model in the flight model. We defined the control concept for various modes(helicopter mode, transition mode, and airplane mode), derived tilt angle corridors, and formulated flight control requirements. The resultant real-time flight simulation software not only performs trim analysis for Tilt Rotor UAM aircraft but also predicts handling qualities, optimizes tilt angle scheduling based on dynamic characteristics, designs and validates flight control laws for helicopter, transition, and airplane modes, and facilitates flight training through simulator integration.

• Journal of Arbitration Studies
• /
• v.30 no.3
• /
• pp.47-66
• /
• 2020

Due to the division of China's departmental laws, the disconnect between theory and practice, and the influence of traditional academic thinking on the understanding of the knowledge structure of arbitration legal talents in practice, the construction of law school colleges, teaching teams, and research centers mostly revolves around departmental laws, tearing the connection of the arbitration legal system. The student-centered, process-guaranteed, and result-oriented arbitration master of law training model is "virtualized," the shaping of arbitration professionalism is ignored, the coverage of practical teaching is narrowed, and the arbitration legal profession is mostly formalized. The prevalence of specialized curriculum systems shortage, single faculty, formalized practical teaching, outdated curriculum settings, unsuitable curriculum system design for development, and inaccurate professional curriculum standards and positioning renders it difficult to integrate the "Belt and Road." The cutting-edge, the latest research results, and practical experience cannot reflect the connotation, goals, and requirements of "Entrepreneurship" education, as well as arbitral issues such as the ineffective monitoring of practical education and the inconsistent evaluation of standards and scales. Under the background of the "Belt and Road," based on system theory and practice and through training goals that innovate and initiate organizational form, activity content, management characteristics, assessment and support conditions, etc., the arbitration law teaching curriculum system is gradually improved and integrated. Through the establishment of a "Belt and Road" arbitration case file database and other measures, a complete arbitration law theory and practice teaching guarantee system has been established. Third parties are introduced, arbitration law experimental modules are developed, students are guided how to discover new knowledge, new contents are mastered, solidarity, cooperation, and problem-solving capabilities are cultivated in the practice of the "Belt and Road," and quality education, vocational education, and innovation education are organically integrated. In order to implement the requirements of arbitration law education, innovation development and collaborative management of arbitration law teaching practice base should be cultivated, thus giving full play to the effect of collaborative education between universities and arbitration institutions.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.135-135
• /
• 2017

Greenhouse have been developed to provide the plants with good environmental conditions for cultivation crop, two major factors of which are the inside air temperature and humidity. The inside temperature are influenced by the heating systems, ventilators and for systems among others, which in turn are geverned by some type of controller. Likewise, humidity environment is the result of complex mass exchanges between the inside air and the several elements of the greenhouse and the outside boundaries. Most of the existing models are based on the energy balance method and heat balance equation for modelling the heat and mass fluxes and generating dynamic elements. However, greenhouse are classified as complex system, and need to make a sophisticated modeling. Furthermore, there is a difficulty in using classical control methods for complex process system due to the process are non linear and multi-output(MIMO) systems. In order to predict the time evolution of conditions in certain greenhouse as a function, we present here to use of recurrent neural networks(RNN) which has been used to implement the direct dynamics of the inside temperature and inside humidity of greenhouse. For the training, we used algorithm of a backpropagation Through Time (BPTT). Because the environmental parameters are shared by all time steps in the network, the gradient at each output depends not only on the calculations of the current time step, but also the previous time steps. The training data was emulated to 13 input variables during March 1 to 7, and the model was tested with database file of March 8. The RMSE of results of the temperature modeling was $0.976^{\circ}C$, and the RMSE of humidity simulation was 4.11%, which will be given to prove the performance of RNN in prediction of the greenhouse environment.

• Therapeutic Science for Rehabilitation
• /
• v.7 no.2
• /
• pp.9-16
• /
• 2018

Objective : The aim of this research was to provide EEG (electroencephalogram) basic data in clinical areas through identifying measurement tools, measurement methods, and evaluation and analysis method of the EEG which is a neurological change measurement of patients with brain injury. Methods : Previous studies were found in an electronic database (e.g., PubMed, Science Direct). The keyword search terms were 'Electroencephalography', 'stroke', 'intervention OR training'. Results : Utilitizing brain-computer interface, the EEG, which is a tool for measuring the effects of rehabilitation through changes of brain activation state. Also, it could identify functional brain reorganization mechanism. Whenever a research utilized the EEG, which is composed of various channels, different types of electrode, and varied electrode locations. Conclusions : Through this review, we found that Electroencephalography is possible to neurologically verify the effectiveness of intervention and formulate an intervention strategy for efficient occupational therapy.

• Journal of the Korean Society for information Management
• /
• v.28 no.2
• /
• pp.53-63
• /
• 2011

This paper aims to find out the kinds of sub-topics that were researched in relation to Information Literacy (IL). The text mining method was applied to the articles with information literacy' in the fields of the descriptor, title and in the LISA Database. Also, out of 214 journals that published these articles, those with the top ten highest frequencies were listed and analyzed. Research results show that 908 articles on information literacy were published in 214 journals and User training' and Students' were major descriptors in the sub-topic area of information literacy. Also, Reference Services Review and The Journal of Academic Librarianship are two key journals in IL research as they have the highest frequency of related articles and have shown increasing trends.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.23 no.3
• /
• pp.545-559
• /
• 2013

There is a limitation on counteracting recent cyber-attacks with only technical security measures because they become more intelligent and large-scale to aim at employees instead of systems directly or to be conducted with unspecified multiple PCs. Thus, comprehensive measures revolved around related manpower are necessary to deal with them. However, domestic information security education system which is the base of professional manpower training lacks medium-and long-term plans for information security education, verification of education programs, and information sharing among educational institutions. This paper suggests information security education development plans for resolving problems on domestic education systems and improving cyber information security environment such as a national information security education master plan, certification system introduction of education programs, and professional manpower database management.