• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.701-708
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• 2017

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the conventional control methods. In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum parameters of fuzzy PID controller are selected by Teaching-Learning optimization. For the fitness function of Teaching-Learning optimization, the performance index of PID controller is used. To verify the performances of the proposed method, we use a Maglev model and compare the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1029-1032
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• 1993

The construction of the rulebase of a fuzzy controller is usually difficult because experts' knowledge is often hard to derive. To remedy such a problem, a number of self-learning schemes for rulebase formulations were proposed. One of the popular approaches is the reinforcement learning. Many successful examples employing such an idea were proposed and claimed to be with good results in the literature. The purpose of this paper is to discuss and make comparisons between some of the related work in order to provide a better picture regarding their performances. A numerical algorithm for the analysis of nonlinear as well as fuzzy dynamic systems, the Cell-to-Cell Mapping, is used. The analytical results reveals the true behavior of the learning schemes.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.97-105
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• 2022

Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.6
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• pp.96-104
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• 1995

A study for a lathe to machine workpiece with noncircular cross-section is presented. The noncircular cutting is accomplished by controlling radial tool position synchronized with revolution angle of the spindle according to the desired cross-sectional shape. A learning control algorithm is suggested for the tool positioning. The learning law of the algorithm is based on pole-zero cancellation, which guarantees the control stability. The control performances are analyzed and simulated on a numerical computer that the effectiveness of the control algorithm is convinced. The algorithm is tested on a conventional NC-lathe which shows some successful results.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.125-139
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• 2023

Facial expression recognition, a topical problem in the field of computer vision and pattern recognition, is a direct means of recognizing human emotions and behaviors. This paper first summarizes the datasets commonly used for expression recognition and their associated characteristics and presents traditional machine learning algorithms and their benefits and drawbacks from three key techniques of face expression; image pre-processing, feature extraction, and expression classification. Deep learning-oriented expression recognition methods and various algorithmic framework performances are also analyzed and compared. Finally, the current barriers to facial expression recognition and potential developments are highlighted.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.672-676
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• 2010

This paper presents a learning rule that weights more on data near decision boundary. This learning rule generates better decision boundary by reducing the effect of outliers on the decision boundary. The proposed learning rule is integrated into IAFC neural network. IAFC neural network is stable to maintain previous learning results and is plastic to learn new data. The performance of the proposed fuzzy neural network is compared with performances of LVQ neural network and backpropagation neural network. The results show that the performance of the proposed fuzzy neural network is better than those of LVQ neural network and backpropagation neural network.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1252-1256
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• 2007

U-learning (ubiquitous learning) systems, which deliver learning materials anytime and anywhere, allow learners to watch live lectures on PDAs, tablet PCs and notebook computers via broadband and wireless Internet. These systems have various problems; first, terminal devices are expensive, and it is difficult to maintain their efficiencies. Secondly, Internet does not guarantee quality of service (QoS), and in general it does not provide real-time services. Finally, the security of these systems is weaker in a local network than in an external network. The USB-based terminal system based on the OSGi service platform was designed as a ubiquitous system, in order to solve those problems. The USB terminals, used in this system, are inexpensive, and it is easy to maintain their performances. Also, this system solves the problems of security in a local network and provides guaranteed QoS. To accomplish this, the number of USB terminals connected to the system has to be limited according to the formula proposed in our paper. This system uses the OSGi specification as a middleware. It supports the discovery mechanism of the USB terminals, maintenance and administration of the system. Finally, this paper shows a driver's license testing system as an example u-learning application1.

• Journal of Global Scholars of Marketing Science
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• v.8
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• pp.111-138
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• 2001

Currently marketing researchers are investigating the causal variables affecting to salesforces' performances. Some researchers found personal and organizational affecting variables as well as structural context of variables. But almost affecting variables examined in salesforce performance researches are personal characteristics. Such organizational variables like leadership, organization's market orientation would be worth to examine in salesforce performance researches. Thus this research is intended to analyze effects of personal and organizational characteristics on salesforces' performances. Data for this research were elicited from sales representatives of motor companies. Data collected were analyzed by regression analysis using SPSSWIN Ver.10.0. The following are major findings of this research. 1. Leadership whether transformational or transactional affected on salesforces' performances. But it was not accepted that transformational leadership would be superior than transactional leadership. 2. Market Orientation of organization affected on its salesforces' performances. 3. Personal characteristics such as need for achievement, compensation predispositon, self efficacy, learning goal orientation were affect on salesforces' performances. But it found that effects of intrinsic compensation predisposition on salesforces, performances were reverser (-). Based on the above findings, the following conclusion could be drawn: 1. Organizational variables like leadership and market orientation are key managerial variables in the sales organization, meaning that sales manager development and organization's market-driven culture are important. 2. Through recruiting and educating, raising salesforces' self-esteem is necessitated.

• School Mathematics
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• v.4 no.1
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• pp.79-95
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• 2002

This paper is to make strides toward an enriched understanding of student learning and performance in mathematics that acknowledges the roles social and cultural contexts play in what students learn as well as what we are able to team about student learning. A student's mathematical practice over a year and a half is presented in detail in order to explore the relationships between classroom contexts and student performance. This study was situated at a K-4 urban elementary school in the United States. The data used for this study included classroom observations, interviews with the teachers and the student, and document collection. The data were analyzed by characterizing each classroom context and exploring the student's practice both in the classrooms and in the interviews. Despite the student's ongoing status as a struggling student, there were tremendous changes in his level of engagement in and persistence with mathematical tasks. The student was substantially more engaged in and enthusiastic about the daily mathematics lessons in third grade than he had been in second. However, we found little improvement in his mathematical understanding and performance during class or in the interviews. This highlights that increased engagement in the mathematical tasks does not necessarily signal increased learning. This paper discusses several issues of learning and performance raised by the student, looking at the relationship between classroom context and student performance. This paper also considers implications for how students' performances are interpreted and how learning is assessed.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.