• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.10
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• pp.103-112
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• 1999

Genetic algorithm, which uses principles of natural selection and population genetics, is an efficient method to find out an optimal solution. In conventional genetic algorithms, however, the size of gene pool needs to be increased to insure a convergency. Therefore, many memory spaces and much computation time were needed. Also, since child chromosomes were generated by chromosome crossover and gene mutation, the algorithms have a complex structure. Thus, in this paper, a compact stereo matching algorithm using a population-based incremental learning based on probability vector is proposed to reduce these problems. The PBIL method is modified for matching environment. Since th proposed algorithm uses a probability vector and eliminates gene pool, chromosome crossover, and gene mutation, the matching algorithm is simple and the computation load is considerably reduced. Even though the characteristics of images are changed, stable outputs are obtained without the modification of the matching algorithm.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.63-69
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• 2021

Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.858-869
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• 2023

As one of the pivotal techniques of image restoration, single-channel blind source separation (SCBSS) is capable of converting a visual-only image into multi-source images. However, image degradation often results from multiple mixing methods. Therefore, this paper introduces an innovative SCBSS algorithm to effectively separate source images from a composite image in various mixed modes. The cornerstone of this approach is a novel triple generative adversarial network (TriGAN), designed based on dual learning principles. The TriGAN redefines the discriminator's function to optimize the separation process. Extensive experiments have demonstrated the algorithm's capability to distinctly separate source images from a composite image in diverse mixed modes and to facilitate effective image restoration. The effectiveness of the proposed method is quantitatively supported by achieving an average peak signal-to-noise ratio exceeding 30 dB, and the average structural similarity index surpassing 0.95 across multiple datasets.

• Journal of Biosystems Engineering
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• v.31 no.3 s.116
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• pp.203-208
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• 2006

This study was conducted to improve the control performance of a current variable-rate controller for granular fertilizers. Simulation model was developed. Optimized proportional, integral and derivative gains were determined by simulation model using 2nd order PID gain learning algorithm, and these control gains were evaluated through the field tests. Important results of this study are as follows; 1. Principles of pre-existing variable-rate application of granular fertilizers were investigated. 2. Simulation model of a PID controller that could simulate the control system was developed by using Matlab/Simulink program. The program was to determine PID control coefficients through the simulation model and 2nd order PID gain learning algorithm. 3. PID control coefficients obtained from the simulation were applied to the developed model. When the step input was given, Maximum overshoot were 1.96%, rise time were 0.05 sec, settling time were 0.06 sec and steady state error were 0.21 % respectively. 4. The simulation model was verified through field tests. The errors of maximum overshoot were 10%, rise time were 0.11 sec, settling time were 0.40 sec and steady state error were 8% because of loads and noises. Rise time was decreased to one third of that of the pre-existing system. 5. If the speed of a fertilizing machine is $0.3{\sim}0.6\;m/s$ and the maximum rotation speed of a discharging roller is 64 rpm, rise time would be 0.26 sec and fertilizing machine would cover the distance of $0.07{\sim}0.15\;m$ with settling time of 0.4 sec, fertilizing machine would cover the distance of $0.12{\sim}0.24\;m$.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.613-618
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• 2012

ICT education guidelines revised in 2005 reinforce computer science elements such as algorithm, data structure, and programming covering all schools. And Ministry of Education emphasizes STEAM education. Most important is that "How instruct them". This means necessity of contents. So this paper suggests learning method of Stack and Queue using LEGO MINDSTORMS NXT. The main purpose is that how stack and queue are used, when robot explore realistic maze. Teaching and learning strategies are algorithm, flowchart, and NXT-G programming. Simple maze has path in left or right, but complex maze has three-way intersection. These are developed by authors. Master robot explores maze and push stack, and then return to entrance using stack. Master robot explores maze and transmits path to slave's queue. And then slave robot drives without exploration. Students can naturally learn principles and applications of them. Through these studies, it can improves ability of logical and creative thinking. Furthermore it can apply to ICT and STEAM education.

• Journal of Educational Research in Mathematics
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• v.22 no.2
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• pp.293-309
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• 2012

The algorithm of natural number multiplication is one of the basic topics of elementary school mathematics. Mastery of algorithm and understanding of the principles are important educational aims. In this paper we analyzed elementary school mathematics textbooks of South Korea, the United States, Singapore, Japan. As a result of analysis, we found out that there are much differences in the teaching of multiplication with three numbers, '${\times}10$', and '${\times}tens$'. We suggested some implication for the teaching of algorithm of natural number multiplication.

• The Journal of Korean Association of Computer Education
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• v.11 no.4
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• pp.23-36
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• 2008

It's known by recent surveys that many students have difficulty in understanding the concepts of programming algorithms, and don't feel interested in learning them. Dynamic programming, one of the most important and widely-used algorithms in computer science, is especially feared by students and unlike other algorithms, it also requires understanding of the process of problem solving and storage space design as well as basic principles of the algorithm. And so it has not been properly covered in classes. In this paper, we developed a visual simulator to solve the above problems in learning dynamic programming. This learning simulator is designed for students to run the algorithms themselves and learn how it works by visualizing each step of dynamic programming and corresponding states of storage space.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.106-117
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• 2022

In 2021, AI basics were introduced in the high school curriculum. There are many worries that the problem of utilization-oriented education will be repeated with the introduction of artificial intelligence education rather than the principles that occurred when ICT was applied to education in the past. Most of the existing AI education platforms focus only on the use of AI. For artificial intelligence education of middle school students, there are difficulties in learning about the process by which artificial intelligence derives results and learning the principles of artificial intelligence algorithms. Recently, as the educational application of metaverse has become a hot topic, research has been started to improve learning achievement by arousing students' immersion and interest. This research developed educational game contents about decision tree algorithm using metaverse as educational contents that can be used in middle school AI education. By applying games to education, it was intended to increase students' interest and immersion in artificial intelligence, and to increase educational effectiveness. In this paper, the educational effectiveness, difficulty, and level of interest were analyzed for pre-service teachers regarding the developed game content. Based on this, a future principle-oriented artificial intelligence education method was suggested.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.159-171
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• 2009

The objective of this research is to explore a method to utilize a programmable robot, as a potential learning tool in the elementary school's curricula. Due to their programmability and operational ease of use, programmable robots are among digital toys that today offer specially instructive features. In this research, we developed the robot game-based project contents as a tool for teaching the elementary school children to learn the algorithm, the essential part of computer programming. The LEGO material, selected as the construction kit for robot, consists of a mechanical assembly system, a set of sensors and actuators, a central control unit, a programming environment. The project requires the children to complete 3 separate tasks, each of which is developed based on the principles of algorithm. The classroom feedback supports that the robotic experiences provided the children with fun and absorption. It is likely that implementing learning with robot in regular classroom in elementary school can bring new possibilities to the educational system, provided that a thorough preparation backs up the plan.

• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.