• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.384-387
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• 1998

This paper presents a newly developed speed sensorless drive using RLS(Recursive Least Squares) based on Neural Network Training Algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known back-propagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the back-propagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.

• Korean Medical Education Review
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• v.23 no.1
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• pp.46-55
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• 2021

Since the emergence of coronavirus disease 2019 (COVID-19), medical schools have experienced a sudden, full-scale transition to online classes. As the COVID-19 pandemic continues, it is important to evaluate current educational programs and to assess their implications. This study explored perceptions of online classes and learning behavior among medical students. Twenty preclinical medical students were interviewed in focus groups for 2 months. They generally expressed positive perceptions about online classes, and in particular, positively assessed the ability to lead their individual lifestyles and study in comfortable environments with fewer time and space constraints. Students thought that the online environment provided a fair chance of facilitating positive interactions with the professor and considered communication with the professor to be an important factor only when it was related to the class content or directly helped with their grades and careers. Students also had negative views, such as feeling uncertain when they could not see their peers' learning progress and assess themselves in comparison and feeling social isolation. Learning behaviors have also changed, as students explored their learning styles and adapted to the changed learning environment. Students expanded their learning by using online functions. However, students sometimes abused the online class format by "just playing" the lecture while not paying attention and relying on other students' lecture transcripts to study. The results of this study are hoped to provide a useful foundation for future research on online class-based teaching and learning.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.106-119
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• 2005

Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1182-1189
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• 1994

In this paper, we evaluate the performance of a fuzzy controller with a self-learning structure. The fuzzy controller is based on a fuzzy logic that approximates and effectively represents the uncertain phenomena of the real world. The fuzzy controller has control of a plant with a fuzzy inference logic. However, it is not easy to decide the membership function of a fuzzy controller and its controlrule. This problem can be solved by designing a self-learning controller that improves its own contropllaw to its goal with a performance table. The fuzzy controller is implemented with a 386PC, an interface board, a D/A converter, a PWM(Pulse Width Modulation) motor drive-circuit, and a sensing circuit, for error and differential of error. Since a Ball and Beam System is used in the experiment, the validity of the fuzzy controller with the self-learning structure can be evaluated through the actual experiment and the computer simulation of the real plant. The self-learning fuzzy controller reduces settling time by just under 10%.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.559-570
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• 2003

In this paper, we investigate a problem with Web-based learing contents and introduce the solving method of the problem. To be more accurate, existing Web-based learning contents were one fixd and uniformed courseware file with a large size and HTML format. Also, They were written files with authoring tools of which depended upon providing a multimedia vender. These learning contents were difficult to reuse among cyber education systems and change the applicable contents to the learner for learning experiences in time. So in this paper, we produced reusable and interopreable learning contents among instruction designers and education systems. They were deconstructed into smaller chunks and added to its properties. For the purpose of this producing method, we used the pedagogical designs for units of reuse. These are just turorial link-more and tell-more and was implemented with CBD method. As a result, The problem of existing Web-based learning contents system was resolved and then the power of understanding about objectified learning content was increased for the learner and instruction designers.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.39-51
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• 2022

Image-based gender classification and age estimation of human are classic problems in computer vision. Most of researches in this field focus just only one task of either gender classification or age estimation and most of the reported methods for each task focus on accuracy performance and are not computationally light. Thus, running both tasks together simultaneously on low cost mobile or embedded systems with limited cpu processing speed and memory capacity are practically prohibited. In this paper, we propose a novel light-weight gender classification and age estimation method based on ensemble multitasking deep learning with light-weight processing neural network architecture, which processes both gender classification and age estimation simultaneously and in real-time even for embedded systems. Through experiments over various well-known datasets, it is shown that the proposed method performs comparably to the state-of-the-art gender classification and/or age estimation methods with respect to accuracy and runs fast enough (average 14fps) on a Jestson Nano embedded board.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.529-541
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• 2015

Collaborative learning of collective intelligence-based model is also ideal in higher education did not yet consensus still in the theoretical level. To become collective intelligence-based collaborative learning is to mobilize the competence of the various members should be promoted as much as possible with their own services designed to actively participate in and contribute to the goals of the joint. Is still based collaborative learning model of collective intelligence, which does the actual model is not developed in education is a key program in creative fusion judge called talent. The evolution of the main features of the house just in shaping the content of a modern lecture geureohagi need to check from time to time to see and pay attention. As part of this study, attempts were associated with the tvN planning and attention to trying connector Executive Lecture show "Creative Club 199" content. Well oriented intention to converge the needs of the times, but it is even more compelling naeeotda implement the collective intelligence based on 'how' the reality is that together with the participants.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.4
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• pp.425-432
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• 2004

This study for Customer Satisfaction(Customer Focus) by Profit security' in the field Process improvement activity and man-power upgrade in the learning of organization activity or upgrading ability of each peoples. This thesis study on the focus of KAPEC which introduce Toyota system can apply to VM, 3jeong, Right Box and Right Position), 5S, JIT(Just In Time), KAlZEN, KANBAN System, CIM, ERP, DAS an output of Factory. For strategic changes to take place in industry 3 key important factors need to be included ; integration of tasks functions and process, decentralization of information and responsibility and finally simplification of products and product structures. These describes how CIM can be implemented using these factors. This study for (1)System Integration, (2) Help Logistic Problems, (3) Partly facilitated growth. (4) Improved production planning (5) Real-time management. (6) Fast reporting (7) Productivity. Quality. Delivery Up, Cost reduction and Autonomy management, FMS in the Plant etc.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.491-502
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• 2010

When human beings acquire and develop knowledge through education, their prior knowledge influences the next learning process. As this is a fact that should be considered in machine learning, we need to examine the effects of controlling the order of training sequence on machine learning. In this research, the role of the supervisor is extended to control the order of training samples, in addition to just instructing the target values for classification problems. The supervisor sequences the training examples categorized by SOM to the learning model which in this case is MLP. The proposed method is distinguished in that it selects the most instructive example from categories formed by SOM to assist the learning progress, while others use SOM only as a preprocessing method for training samples. The result shows that the method is effective in terms of the number of samples used and time taken in training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.