• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.86-94
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• 1997

This paper proposes the robot inverse calibration method using a neural networks. A high-order networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the diff- erence of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from .+-. 5 .deg. to .+-. 0.1 .deg. .

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.1-12
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• 2023

With the rapid increase of user generated data on digital platforms, the task of categorizing and classifying theses huge data has become difficult. Topic modeling is an unsupervised machine learning technique that can be used to get a summary from a large collection of documents. Topic modeling has been widely used in English content, yet the application of topic modeling in Arabic language is limited. Therefore, the aim of this paper is to provide a systematic review of the application of topic modeling algorithms in Arabic content. Using a well-known and trusted databases including ScienceDirect, IEEE Xplore, Springer Link, and Google Scholar. Considering the publication date from 2012 to 2022, we got 60 papers. After refining the papers based on predefined criteria, we resulted in 32 papers. Our result show that unfortunately the application of topic modeling techniques in Arabic content is limited.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.521-543
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• 2023

Reynolds-averaged Navier-Stokes (RANS) models are extensively employed in industrial settings for the purpose of simulating intricate fluid flows. However, these models are subject to certain limitations. Notably, disparities persist in the Reynolds stresses when comparing the RANS model with high-fidelity data obtained from Direct Numerical Simulation (DNS) or experimental measurements. In this work we propose an approach to mitigate these discrepancies while retaining the favorable attributes of the Menter Shear Stress Transport (SST) model, such as its significantly lower computational expense compared to DNS simulations. This strategy entails incorporating an explicit algebraic model and employing a neural network to correct the turbulent characteristic time. The imposition of realizability constraints is investigated through the introduction of penalization terms. The assimilated Reynolds stress model demonstrates good predictive performance in both in-sample and out-of-sample flow configurations. This suggests that the model can effectively capture the turbulent characteristics of the flow and produce physically realistic predictions.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.737-747
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• 2022

The purpose of this study is to analyze farmers' perceptions of Greenhouse Gas Reduction Projects and identify factors influencing participation in the projects. To achieve the research objective, a survey was conducted and a probit model was adopted for the empirical analysis. The results showed that farmers do not participate in the projects due to a lack of education and promotion and due to economic loss. It also showed that the frequency of training and technical guidance learning, degree of recognition of the need for efforts to reduce greenhouse gases, and the level of recognition of the projects concerned have a positive impact on the willingness to participate in the projects. Meanwhile, participation in agricultural environment conservation programs has a negative impact on it. Enhancement of education and promotion as well as economic support (direct payment, R&D) would be useful to raise the willingness to participate in the projects.

• Journal of The Korean Association For Science Education
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• v.23 no.5
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• pp.517-530
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• 2003

The purpose of this study is to find out a model to explain the process of students' science-related career choice by identifying the causal relationships between science career choice and related factors. Important factors of science-related career choice were identified through factor analysis. 'Perception about career related to science', 'preference for science learning' and 'participation in science related activity' were three main factors of science-related career choice. A questionnaire was developed to know the factors of students' science-related career choice, and so as to make it possible to be analysed by structural equation modeling. The subject were 947 grade 6, 9, and 11 students in Seoul. Numbers of boys and girls in each grade was almost same. According to the structural equation modeling, 4 corrected models were obtained. In all 4 corrected models, 'perception about career related to science' had direct influence, and 'preference for science learning' and 'participation in science related activity' had indirect influence on science-related career choice. In the most fitting model. 'perception about career related to science' had an effect on science-related career choice with standardized total effect coefficient 1.03(direct effect 0.82, indirect effect 0.21). 'Preference for science learning', which influence 'participation in science related activity', had an effect on science-related career choice with standardized indirect effect coefficient 0.65. 'Participation in science related activity', which influence 'perception about career related to science'. had an effect on science-related career choice with standardized indirect effect coefficient 0.79. The implication to school science education is that it is most effective to raise the 'perception about career related to science' in order to make more students choose science related career. It is also effective to have more students participate in science related activity and enhance the preference for science learning. To explain the process of science related career choice more fully, it is necessary to build a more comprehensive model containing more factors influencing science-related career choice. It is necessary to test and complement the structural equation model by enlarging the subject to science high school students and science related college students.

• Journal of The Korean Association For Science Education
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• v.42 no.3
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• pp.371-381
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• 2022

This study causally investigates whether high school student with high science learning motivation becomes to achieve more or vice versa, and also how those two factors affect STEM career motivation. Research participants were 1st year students in a high school at Seoul. We surveyed their science learning motivation three times in the same time interval in the fall semester of 2021, and once a STEM career motivation in the third period. We collected data from 171 students with their mid-term and final exam scores, with which, we constructed and fitted an autoregressive cross-lagged model. The research model shows high measurement stability and fit indices. All the autoregressive and cross-lagged paths were statistically significant. However, standardized regression coefficients were larger in path from motivation to achievement compared to the opposite. Only science learning motivation shows significant direct effect on STEM career motivation, rather than achievement. For indirect effects, the first science learning motivation affected the final exam score and STEM career motivation, and the final exam score affected STEM career motivation. However, the final exam score did not have a total effect toward STEM career motivation. The result of this study shows reciprocal and cyclic causality between science learning motivation and achievement - in comparison, the effect of motivation for the opposite is larger than that of achievement. Also the result of this study strongly reaffirms the importance of science learning motivation. Instructional implications for strengthening science learning motivation throughout a semester was discussed, and a study for the longitudinal effect of science learning motivation and achievement in high school student toward future STEM vocational life was suggested.

• Education of Primary School Mathematics
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• v.26 no.3
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• pp.163-180
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• 2023

This study aimed to investigate the factors influencing the intentions of elementary school teachers to use artificial intelligence (AI) in mathematics lessons and to identify the essential prerequisites for the effective implementation of AI in mathematics education. To achieve this purpose, we examined the structural relationship between elementary school teachers' TPACK(Technological Pedagogical Content Knowledge) and the TAM(Technology Acceptance Model) using structural equation model. The findings of the study indicated that elementary school teachers' TPACK regarding the use of AI in mathematics instruction had a direct and significant impact on their perceived ease of use and perceived usefulness of AI. In other words, when teachers possessed a higher level of TPACK competency in utilizing AI in mathematics classes, they found it easier to incorporate AI technology and recognized it as a valuable tool to enhance students' mathematics learning experience. In addition, perceived ease of use and perceived usefulness directly influenced the attitudes of elementary school teachers towards the integration of AI in mathematics education. When teachers perceived AI as easy to use in their mathematics lessons, they were more likely to recognize its usefulness and develop a positive attitude towards its application in the classroom. Perceived ease of use, perceived usefulness, and attitude towards AI integration in mathematics classes had a direct impact on the intentions of elementary school teachers to use AI in their mathematics instruction. As teachers perceived AI as easy to use, valuable, and developed a positive attitude towards its incorporation, their intention to utilize AI in mathematics education increased. In conclusion, this study shed light on the factors influencing elementary school teachers' intentions to use AI in mathematics classes. It revealed that teachers' TPACK plays a crucial role in facilitating the integration of AI in mathematics education. Additionally, the study emphasized the significance of enhancing teachers' awareness of the advantages and convenience of using AI in mathematics instruction to foster positive attitudes and intentions towards its implementation. By understanding these factors, educational stakeholders can develop strategies to effectively promote the utilization of AI in mathematics education, ultimately enhancing students' learning outcomes.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.113-119
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• 2018

The purpose of this study is to verify the effect of participating in teaching material development program using 3D printer on the recognition of universal design for learning of early childhood special education pre-service teachers. This study was conducted on 30 early childhood special education pre-service teachers who participated in teaching material development program using 3D printer. The results of this study showed that the early childhood special education pre-service teachers use of the 3D printer-based teaching material development program resulted in statistically significant differences in the 4 low rank factors of the universal design for learning. This research shows that direct experience in developing teaching material using 3D printers is very effective to improve universal design for learning awareness. In addition, the experience of developing teaching material for pre-service early childhood teachers is important for improving the instructional design ability, and they need the practice of the development of the teaching material using the latest technology for teaching students with various needs.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.185-192
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• 2012

Call Center requires an ability of agents a lot more than face-to-face contact due to being achieved communication by non face-to face channel for contact with customers. In order to improve the ability of agents, Call Center carries out various educational training according to their work experience and function and with the accomplishment of educational training, Call Center is going to fulfill to develop its quality of counseling and productivity. On the other hand, due to investment of a lot of time and budget to educational training, it is needed to grasp and manage about its effectiveness that how helpful the training is for performance of work-site operations through evaluation of educational training. Having Seen researches about evaluation of educational training until these days, most researches have mainstream to measure satisfaction and a level of learning or degree that how the learning transfers to actions. It is found that a research about an entire evaluation model should be required. This study aims to investigate effectiveness of Call Center educational training from the level of recognition by reflecting Kirkpatrick's the four levels of learning evaluation. By the four levels, reaction, learning, behavior and results, the study found out a connection with standards of evaluation about each levels. In addition, by using structural equation modeling, it was examined goodness of fit about the entire model. Furthermore, by an alternative model, considering a direct relation between a factor of reaction and behavior, it was compared and examined goodness of fit of overall model of the study model and the alternative one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.527-533
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• 2018

Recently, the device-to-device (D2D) communication has been conceived as the key technology for the next-generation mobile communication systems. The neighbor discovery in which the nearby users are found, is essential for the proper operation of the D2D communication. In this paper, we propose new neighbor discovery scheme based on deep learning technology which has gained a lot of attention recently. In the proposed scheme, the neighboring users can be found using the uplink pilot transmission of users only, unlike conventional neighbor discovery schemes in which direct pilot communication among users is required, such that the signaling overhead can be greatly reduced in our proposed scheme. Moreover, the neighbors with different proximity can also be classified accordingly which enables more accurate neighbor discovery compared to the conventional schemes. The performance of our proposed scheme is verified through the tensorflow-based computer simulations.