• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1113-1119
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• 2000

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.3
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• pp.15-23
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• 2019

This study aims to suggest a Biophilic classroom design of high school to obtain attention restoration for students. The learning space for youth in the modern society is composed of dry artificial structures. This space is considered to be a space that can not relieve stress caused by learning. "The Attention Restoration Theory" is divided into "Directed Attention" of humans, which is the cause of fatigue and stress, and "Involuntary Attention" as a solution to it. "Involuntary Attention" takes place in a rest state and helps the brain recover when exposed to nature. And the core of "Biophilic-Design Theory" is that humans can recover physical and mental conditions when exposed to nature. The purpose of this study is to apply "The Biophilic-Design Theory" that emphasizes the importance of exposure to nature to the educational space and plan the space where the 'Attention Restoration' can be achieved. The research method is as follows. First, we review previous studies related to "The Biophilic-Design Theory" and "The Attention Restoration Theory". Second, we analyze the application examples of "The Biophilic-Design Theory" and "The Attention Restoration Theory" in domestic and foreign educational spaces. Third, the concept of educational space is set up based on the elements derived from previous studies. Finally, we propose the planning direction of classroom design based on Biophilic-Design. The following conclusions were drawn. First, The creation of the education space to restore the learner's attention requires a visual space plan that utilizes natural elements such as natural light, artificial light, plants, and natural materials that can directly experience nature. Second, the direction in which students in the classroom can be "The Attention Restoration Theory" should consider the use of indirect natural elements that bring the surrounding natural landscape into the interior. This study will be used as the baseline data for the spatial design and planning of education facilities based on Biophilic-Design.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.51-58
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• 2009

Reinforcement learning is a sub area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. In the case of multi-agent, especially, which state space and action space gets very enormous in compared to single agent, so it needs to take most effective measure available select the action strategy for effective reinforcement learning. This paper proposes a multi-agent reinforcement learning model based on fuzzy inference system in order to improve learning collect speed and select an effective action in multi-agent. This paper verifies an effective action select strategy through evaluation tests based on Robocup Keepaway which is one of useful test-beds for multi-agent. Our proposed model can apply to evaluate efficiency of the various intelligent multi-agents and also can apply to strategy and tactics of robot soccer system.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.149-158
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• 2023

HMD, which is applied with the latest technology, minimizes motion sickness with high-resolution displays and fast motion recognition, and can accurately track location and motion. This can provide an environment where you can immerse yourself in a virtual three-dimensional space, and virtual reality contents such as disaster simulators and high-risk equipment learning spaces are developing using these characteristics. These advantages are also applicable in the field of basic science education. In particular, expanding the concepts of electric and magnetic fields in physics described by existing two-dimensional data into three-dimensional spaces and visualizing them in real time can greatly help improve learning understanding. In this paper, realistic physical education environments and contents based on three-dimensional virtual reality are developed and the developed learning contents are experienced by actual learning subjects to prove their effectiveness. A total of 46 middle school and college students were taught and experienced in real time the electric and magnetic fields expressed in three dimensions in a virtual reality environment. As a result of the survey, more than 85% of positive responses were obtained, and positive results were obtained that three-dimensional virtual space-based physical learning could be effectively applied.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.173-179
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• 2017

The purpose of this study is to theoretically explore the pedagogical meaning of Flipped Learning (FL) through the lens of Bakhtinnian Dialogism. Flipped learning has emerged based on technological development, which enables educators to reorganize educational space and time. In the new converged time and space, teachers changed the activities in and out of classes, flipped lectures with students' activity, and redefined the role of teachers and students. These dialogical characteristics of FL results in several educational consequences: first, underscoring educational dialogue, second, extending the participation in the dialogical chain, third, deconstructing the existing authority of knowledge, and escalating the self-directedness of learners.

• Steel and Composite Structures
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• v.37 no.2
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• pp.193-209
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• 2020

In this paper, the efficiency of five Machine Learning (ML) methods consisting of Deep Learning (DL), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Gradient Tree Booting (GTB) for regression and classification of the Ultimate Load Factor (ULF) of nonlinear inelastic steel frames is compared. For this purpose, a two-story, a six-story, and a twenty-story space frame are considered. An advanced nonlinear inelastic analysis is carried out for the steel frames to generate datasets for the training of the considered ML methods. In each dataset, the input variables are the geometric features of W-sections and the output variable is the ULF of the frame. The comparison between the five ML methods is made in terms of the mean-squared-error (MSE) for the regression models and the accuracy for the classification models, respectively. Moreover, the ULF distribution curve is calculated for each frame and the strength failure probability is estimated. It is found that the GTB method has the best efficiency in both regression and classification of ULF regardless of the number of training samples and the space frames considered.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.155-162
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• 2002

Reinforcement learning is a learning method that uses trial-and-error to perform Learning by interacting with dynamic environments. It is classified into online reinforcement learning and delayed reinforcement learning. In this paper, we propose an online reinforcement learning system (ONRELS : Outline REinforcement Learning System). ONRELS updates the estimate-value about all the selectable (state, action) pairs before making state-transition at the current state. The ONRELS learns by interacting with the compressed environments through trial-and-error after it compresses the state space of the mage environments. Through experiments, we can see that ONRELS can search the shortest path faster than Q-learning using TD-ewor and $Q(\lambda{)}$-learning using $TD(\lambda{)}$ in the maze environments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1017-1020
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• 1993

This paper describes a neo fuzzy neuron which was produced by a fusion of fuzzy logic and neuroscience. Some learning algorithms are presented. The guarantee for the global minimum on the error-weight space is proved by a reduction to absurdity. Enhanced is that the learning speed of the neo fuzzy neuron exceeds 100,000 times of that of conventional multi-layer neural networks.