• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of Korea Multimedia Society
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• 제21권5호
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• pp.626-637
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• 2018

Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

• Archives of design research
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• 제11권1호
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• pp.99-108
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• 1998

The purpose of this study was to exanune the effects of learning training program on university students' creativity. In order to accomplish this purpJse of the study effectively, the following research hypotheses were posited: (Hypothesis 1) The treJltment group trained with the learning program will perform better in technic.al class. (Hypothesis 2) The treatment group trained with the learning program will show the higher degree of progress in diagram creativity than the pre-treatment group. The result of the study were summarized as follows: First, regarding the effects of training, there appeared a significant difference between the groups of post-treatment class and pre-treatment class in the use of learning strategies. Second. there appeared a significant difference between the group of post-treatment class and pre-treatment class in diagranl creativity.

• IEMEK Journal of Embedded Systems and Applications
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• 제16권5호
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• pp.163-169
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• 2021

In this paper, we implemented a deep learning-based automatic object tracking and handy motion control drone system and analyzed the performance of the proposed system. The drone system automatically detects and tracks targets by analyzing images obtained from the drone's camera using deep learning algorithms, consisting of the YOLO, the MobileNet, and the deepSORT. Such deep learning-based detection and tracking algorithms have both higher target detection accuracy and processing speed than the conventional color-based algorithm, the CAMShift. In addition, in order to facilitate the drone control by hand from the ground control station, we classified handy motions and generated flight control commands through motion recognition using the YOLO algorithm. It was confirmed that such a deep learning-based target tracking and drone handy motion control system stably track the target and can easily control the drone.

• KIPS Transactions on Software and Data Engineering
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• 제12권6호
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• pp.251-258
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• 2023

It is necessary to predict peak load accurately in order to supply electric power and operate the power system stably. Especially, it is more important to predict peak load accurately in winter and summer because peak load is higher than other seasons. If peak load is predicted to be higher than actual peak load, the start-up costs of power plants would increase. It causes economic loss to the company. On the other hand, if the peak load is predicted to be lower than the actual peak load, blackout may occur due to a lack of power plants capable of generating electricity. Economic losses and blackouts can be prevented by minimizing the prediction error of the peak load. In this paper, the latest deep learning model such as TCN is used to minimize the prediction error of peak load. Even if the same deep learning model is used, there is a difference in performance depending on the hyper-parameters. So, I propose methods for optimizing hyper-parameters of TCN for predicting the peak load. Data from 2006 to 2021 were input into the model and trained, and prediction error was tested using data in 2022. It was confirmed that the performance of the deep learning model optimized by the methods proposed in this study is superior to other deep learning models.

• Journal of Internet Computing and Services
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• 제10권5호
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• pp.135-143
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• 2009

The purpose of this study is to examine the variables related to the students' e-learning participation that have an effect on learning achievement in e-learning environment of cyber universities. Based on the related research, the followings are derive. First, students' attendance and participation in discussion showed higher correlation with the learning achievement than other participation variables. However, the total studying time in online classes showed lower correlation with the learning achievement. Second, the variables that have an effect on the learning achievement were in the order of students' attendance, participation in discussion, access frequency to online classes, learning progress and number of data uploads. Third, by the learners' background, the difference among the variables that have an effect on learning achievement were found. Based on the results above, this study suggests considerations about participation variables to enhance the learning achievement in cyber universities.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제22권6호
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• pp.1231-1243
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• 1997

Digital satellite communication channels have nonlinearities with memory due to saturation characteristics of traveling wave tube amplifier in the satellite and transmitter/receiver linear filters. In this paper, we propose a network structure and a learning algorithm for complex pi-sigma network (CPSK) and exploit CPSN in the problem of equalization of nonlinear satellite channels. The proposed CPSN is a complex-valued extension of real-valued pi-sigma network that is a higher-order feedforward network with fast learning while greatly reducing network complexity by utilizing efficient form of polynomials for many input variables. The performance of the proposed CPSN is demonstrated by computer simulations on the equalization of complex-valued QPSK input symbols distorted by a nonlinear channel modeled as a Volterra series and additive noise. The results indicate that the CPSN shows good equalization performance, fast convergence, and less computations as compared to conventional higher-order models such as Volterra filters.

• Journal of The Korean Association For Science Education
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• 제19권3호
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• pp.367-376
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• 1999

When 37 7th-graders learned science in cooperative learning environments, their small-group processes were audio/video taped. The verbal behaviors that appeared in cooperative learning processes were categorized, and the relationships between verbal behaviors and academic achievement were investigated. Students' verbal behaviors were classified into learning behaviors and management behaviors. Learning behaviors were further coded into giving help. reading problem, and asking help. Giving help was the most frequent behavior among the categories. In studying zero-order correlation between verbal behaviors and academic achievement, giving help and reading problem were found to have positive relationships with academic achievement. Giving specific content, which is a subcategory of giving help. showed the closest correlation with academic achievement. In studying partial correlation between verbal behaviors and the improvement of academic achievement, only application subtest score. which demands higher-order thinking, was positively related with some verbal behaviors including giving specific content.

• Computers and Concrete
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• 제19권3호
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.35-42
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• 2022

The article identifies the problem of monitoring the quality of higher education in three main areas, which are comparative pedagogical systems of education. The first direction is determined by dissertation works, the second - monographs and textbooks, and the third reveals scientific periodicals. According to its internal structure, monitoring the quality of education combines important management components identified in the article (analysis, evaluation and forecasting of processes in education; a set of methods for tracking processes in education; collecting and processing information to prepare recommendations for research processes and make necessary adjustments). Depending on the objectives, three areas of monitoring are identified: informational (involves the accumulation, structuring and dissemination of information), basic (aimed at identifying new problems and threats before they are realized at the management level), problematic (clarification of patterns, processes, hazards, those problems that are known and significant from the point of view of management). According to its internal structure, monitoring the quality of education combines the following important management components: analysis, evaluation and forecasting of processes in education; a set of techniques for tracking processes in education; collection and processing of information in order to prepare recommendations for the development of the studied processes and make the necessary adjustments. One of the priorities of the higher education modernization program during the COVID-19 pandemic is distance learning, which is possible due to the existence of information and educational technologies and communication systems, especially for effective education and its monitoring in higher education. The conditions under which the effectiveness of pedagogical support of monitoring activities in the process of distance learning is achieved are highlighted. According to the results of the survey, the problems faced by higher education seekers are revealed. A survey of students was conducted, which had a certain level of subjectivity in personal assessments, but the sample was quite representative.