• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.436-439
• /
• 1995

In this paper we present a method to construct fuzzy model with multi-dimension input membership function, which can construct fuzzy inference system on one node of the network directly. This method comes from a common framework called Universal Learning Network (ULN). The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural network. We also introduce how to imitate a real system with ULN and a control scheme using ULNFIS.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.183-186
• /
• 1995

A method is presented for optimization of Universal Learning Networks (ULN), where, together with gradient method, Simulated Annealing (SA) is employed to elude local minima. The effectiveness of the method is shown by its application to control of a crane system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.235-238
• /
• 1996

In this paper, a new optimization method which is a kind of random searching is presented. The proposed method is called RasVal which is an abbreviation of Random Search Method with Variable Seaxch Length and it can search for a global minimum based on the probability density functions of searching, which can be modified using informations on success or failure of the past searching in order to execute intensified and diversified searching. By applying the proposed method to a nonlinear crane control system which can be controlled by the Universal Learning Network with radial basis function(R.B.P.), it has been proved that RasVal is superior in performance to the commonly used back propagation learning algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.111-114
• /
• 1996

This paper proposes a robust control method using Universal Learning Network(U.L.N.) and second order derivatives of U.L.N.. Robust control considered here is defined as follows. Even if external input (equal to reference input in this paper) to the system at control stage changes awfully from that at learning stage, the system can be controlled so as to maintain a good performance. In order to realize such a robust control, a new term concerning the perturbation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivative of the criterion function with respect to the parameters.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.95-98
• /
• 1996

Universal Learning Network(U.L.N.), which can model and control the large scale complicated systems naturally, consists of nonlinearly operated nodes and multi-branches that may have arbitrary time delays including zero or minus ones. Therefore, U.L.N. can be applied to many kinds of systems which are difficult to be expressed by ordinary first order difference equations with one sampling time delay. It has been already reported that learning algorithm of parameter variables in U.L.N. by forward and backward propagation is useful for modeling, managing and controlling of the large scale complicated systems such as industrial plants, economic, social and life phenomena. But, in the previous learning algorithm of U.L.N., time delays between the nodes were fixed, in other words, criterion function of U.L.N. was improved by adjusting only parameter variables. In this paper, a new learning algorithm is proposed, where not only parameter variables but also time delays between the nodes can be adjusted. Because time delays are integral numbers, adjustment of time delays can be carried out by a kind of random search procedure which executes intensified and diversified search in a single framework.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.123-126
• /
• 1995

Characteristics of control system design using Universal Learning Network (U.L.N.) are that a system to be controlled and a controller are both constructed by U.L.N. and that the controller is best tuned through learning. U.L.N has the same generalization ability as N.N.. So the controller constructed by U.L.N. is able to control the system in a favorable way under the condition different from the condition of the control system in learning stage. But stability can not be realized sufficiently. In this paper, we propose a robust control method using U.L.N. and second order derivatives of U.L.N.. The proposed method can realize better performance and robustness than the commonly used Neural Network. Robust control considered here is defined as follows. Even though initial values of node outputs change from those in learning, the control system is able to reduce its influence to other node outputs and can control the system in a preferable way as in the case of no variation. In order to realize such robust control, a new term concerning the variation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivatives of criterion function with respect to the parameters. Finally it is shown that the controller constricted by the proposed method works in an effective way through a simulation study of a nonlinear crane system.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.315-321
• /
• 2022

Many students with disabilities and special needs are enrolled in higher education, which substantiated the need for research regarding faculty members' knowledge and willingness to implement supportive strategies in higher education in Saudi Arabia. This study explored Saudi university faculty members' knowledge and willingness to apply UDL (Universal Design for Learning) principles in their teaching practice. Surveys were used for data collection for this descriptive research. The findings indicated faculty members felt that they were knowledgeable regarding UDL and were willing to use UDL principles in teaching their students. Furthermore, there were no statistically significant differences between faculty members' knowledge levels regarding UDL based on their current position and years of experience. The findings indicated there was a significant relationship between gender and knowledge, with males having a significantly higher mean knowledge, although further analyses revealed it was a small effect. Finally, the results suggest more years of experience are related to greater willingness to use UDL principles, and this is particularly true for those in a lecturing position. These findings could be helpful, particularly for the Ministry of Education in Saudi Arabia to shed light on faculty members' UDL knowledge. Further research is needed to substantiate the findings.

• Journal of Korea Multimedia Society
• /
• v.22 no.11
• /
• pp.1242-1250
• /
• 2019

Super resolution technique aims to convert a low-resolution image with coarse details to a corresponding high-resolution image with refined details. In the past decades, the performance is greatly improved due to progress of deep learning models. However, universal solution for various objects is a still challenging issue. We observe that learning super resolution with a general dataset has poor performance on faces. In this paper, we propose a super resolution fusion scheme that works well for both general- and face datasets to achieve more universal solution. In addition, object-specific feature extractor is employed for better reconstruction performance. In our experiments, we compare our fusion image and super-resolved images from one- of the state-of-the-art deep learning models trained with DIV2K and FFHQ datasets. Quantitative and qualitative evaluates show that our fusion scheme successfully works well for both datasets. We expect our fusion scheme to be effective on other objects with poor performance and this will lead to universal solutions.

• The Journal of the Convergence on Culture Technology
• /
• v.6 no.4
• /
• pp.9-14
• /
• 2020

This study examined whether the integrated education science and resource e-book developed for students with disabilities were properly implemented in terms of universal design for learning. For analysis, "Teaching and learning materials for inclusive education of students with disabilities: grade 3~6 sciences", which were instructional adaptation, were selected for students with disabilities who are unable to learn the contents of general textbooks for the 3rd to 6th grade of the elementary school science course in the 2015 revised curriculum. The science grades are composed of 40 units, including basic science inquiry, matter, life, kinetic and energy, earth and universe. The content analysis standard was based on detailed items of 9 definitions according to the 3 principles of UDL presented in CAST (2018). As a result of the study, the strategy network was the largest among the UDL principles. As for the domain of the science curriculum, the kinetic and energy was the most common. As UDL detailed items, informations presentation suitable for learners was most frequent in cognitive network. Various ways of searching for data, was most frequent in strategies network. Diverse materials optimized difficulty of contents was most frequent in affective network.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.687-701
• /
• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.