• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.386-388
• /
• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1344-1346
• /
• 1996

In this paper, we discuss an informal analysis of diffusion, global optimization properties of Langevine competitive learning neural network. In the view of the stochastic process, it is important that competitive learning gurantee an optimal solution for pattern recognition. We show that the binary reinforcement function in Langevine competitive learning is a brownian motion as Gaussian process, and construct the Fokker-Plank equation for the proposed neural network. Finally, we show that the informal analysis of the proposed algorithm has a possiblity of globally optimal. solution with the proper initial condition.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.420-437
• /
• 2024

There are some problems in network traffic classification (NTC), such as complicated statistical features and insufficient training samples, which may cause poor classification effect. A NTC architecture based on one-dimensional Convolutional Neural Network (CNN) and transfer learning is proposed to tackle these problems and improve the fine-grained classification performance. The key points of the proposed architecture include: (1) Model classification--by extracting normalized rate feature set from original data, plus existing statistical features to optimize the CNN NTC model. (2) To apply transfer learning in the classification to improve NTC performance. We collect two typical network flows data from Youku and YouTube, and verify the proposed method through extensive experiments. The results show that compared with existing methods, our method could improve the classification accuracy by around 3-5%for Youku, and by about 7 to 27% for YouTube.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.1
• /
• pp.85-92
• /
• 2020

Deep learning shows outstanding performance in image and video analysis, such as object classification, object detection and semantic segmentation. In this paper, it is analyzed that the performances of deep learning models can be affected by characteristics of train dataset. It is proposed as a method for selecting activation function and optimization algorithm of deep learning to classify facial expression. Classification performances are compared and analyzed by applying various algorithms of each component of deep learning model for CK+, MMI, and KDEF datasets. As results of simulation, it is shown that genetic algorithm can be an effective solution for optimizing components of deep learning model.

• Cross-Cultural Studies
• /
• v.19
• /
• pp.411-444
• /
• 2010

This paper explored the academic socialization of a group of Mongolian college students, learning Korean as their L3 (Third Language), by focusing on their uses of an electronic communication channel. From a perspective of the continua of bi-literacy, this case study investigated how Mongolian students who had limited exposure to a Korean learning community overcame academic challenges through the use of a written communication channel as a tool in the socialization process. Data were collected mainly through three methods: written products, interviews, and questionnaires. The results from this study were as follows. Interactional opportunities for these minority students were seriously constrained during the classroom practices in a Korean-speaking classroom. They also described the lack of communicative competence in Korean and the limited roles played by L2 (English) communication as key barriers to classroom practices. However, students' ways of engaging in electronic interactions differed widely in that they were able to broaden interactional circles by communicating their expertise and difficulties with their Korean peers through the electronic channel. More importantly, the communication pattern of "L2-L2/L3-L3" (on a L2-L3 continuum) emerging from data demonstrated how these students used a written channel as a socialization tool to mediate their learning process in a new community of learning. This study argues that a written communication channel should be taken as an essential part of teaching practices especially for foreign students who cannot speak Korean fluently in multi-cultural classes.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.3
• /
• pp.547-552
• /
• 2019

Recently, the massive growth in the scale of data has been observed as a major issue in the Big Data. Furthermore, the Big Data should be preprocessed for normalization to get a high performance of the Machine learning since the Big Data is also an input of Machine Learning. The performance varies by many factors such as the scope of the columns in a Big Data or the methods of normalization preprocessing. In this paper, the various types of normalization preprocessing methods and the scopes of the Big Data columns will be applied to the SVM(: Support Vector Machine) as a Machine Learning method to get the efficient environment for the normalization preprocessing. The Machine Learning experiment has been programmed in Python and the Jupyter Notebook.

• Research in Mathematical Education
• /
• v.9 no.3 s.23
• /
• pp.269-273
• /
• 2005

Nowadays application of modem achievements of information technologies in science, engineering and education is usual phenomenon. Application of these technologies allows easily creating new methods of learning of mathematics. More of new methods of creation of multimedia electronic manuals on high mathematics are founded to application of multimedia and communication opportunities of the computer. But application only multimedia and communication opportunities of the computer at creation of multimedia electronic manuals on high mathematics is insufficient to elimination of 'gap' between training and studying high mathematics. So, we offer a new way of the decision of this problem: creation of a multimedia electronic manual on high mathematics with built-in a mathematical environment MathCad in the national language.

• Journal of Engineering Education Research
• /
• v.22 no.4
• /
• pp.43-49
• /
• 2019

A case of course-embedded assesment in electronic engineering was studied. In particular, a realistic evaluation system was developed in consideration of characteristics of the major field and university realities. 10 program outcomes were mapped with all courses in the program. 5 probe courses were selected and their course learning objectives were defined. Measurements of the course learning objective were made by term project and written course test. With using course-embedded assesment, the measurement system of the program outcomes should be changed.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.299-304
• /
• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.173-178
• /
• 1998

The strategy presented in this paper is based on modifying the past patterens and adjusting the content of the driving patterns by a new algorithm. Learning happens during the driving procedure of a mobile vehicle. The purpose of this paper is to solve the problem how to realize the hardware neurocomputer by back propagation (BP) neural network learning on-line.