• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.8
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• pp.816-824
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• 1991

It is well known that dynamic control is needed for fast and accurate control. Neural networks are ideal for representing the strongly nonlinear relationship in the dynamic equations including complex unmodeled effects. It thus creates many advantages over conventional methods such as simple, fast and accurate control through neural network's inherent learning and massive parallelism. In this paper, dynamic control of the full six degrees of freedom of an industrial robot arm will be presented using neural networks. Moreover, through application to a real robot the usefulness of neurocontrol is demonstrated. The back propagation and feedback-error learning is used to train the neurocontroller. Simulated control of a PUMA 560 arm demonstrates that it moves at high speed with good accuracy and generalizes over untrained trajectories as well as adapt to unforseen load changes and sensor noise.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.150-155
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• 1993

The neural network MRAC system is presented. The purpose of this paper is applied to a plant that is to be controlled in a strongly nonlinear environment. The proposed system has a learning and adaptive ability in the varying environment by using the back-propagation learning algorithm based on Lyapunov stability theory. N.N. regulator is a part of overall system and is guaranteed to be stable in initial stage. Nonlinear terms of the varying mass, colilori, centifugal, and gravity are compensated for by feedforward N.N. regulator. And the feedback controller (adaptive mechanism) works to eliminate errors of position, velocity which the feedforward controller cannot compensate for. Finally, the proposed system will be demonstrated by simulation of a two d.o.f robot manipulator.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2265-2271
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• 1994

With the improvement of standard of living, requirement for comfortable and quiet environment has been increased and, therefore, there has been a many researches for active noise reduction to overcome the limit of passive control method. In this study, active noise control is performed in a duct system using intelligent control technique which needs not decide the coefficients of high order filter and the mathematical modeling of a system. Back propagation algorithm is applied as an intelligent control technique and control system is organized to exclude the error microphone and high speed operational device which are indispensable for conventional active noise control techniques. Furthermore, learning is performed by organizing acoustic feedback model, and the effect of the proposed control technique is verified via computer simulation and experiment of active noise control in a duct system.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.1
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• pp.289-299
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• 2016

The purpose of the study was to develop evaluation methods which would measure student achievement and progress without traditional paper-pencil tests such as mid-term and final examinations. More specifically, the main research focus were to establish general directions of student assessment during free semester, to build evaluation models supporting student's participation and learning, and to report and record various student evaluation results. As research results, we found that student evaluation of free semester should be organized to improve a) experience learning activity, self-regulatory and collaboratory study, b) high-order thinking ability and character-building, and c) teacher-student-parent cooperation. Since traditional paper-pencil tests were restricted in free semester, student achievement should be provided by a way of performance descriptions on transcripts rather than quantitative grade points. Student performance descriptions had to show not only subject knowledge but also students efforts, motivation, and participation. These multiple and educationally meaningful information would be collected by teacher-student-parent communication and collaboration.

• Journal of The Korean Association For Science Education
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• v.25 no.6
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• pp.635-641
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• 2005

This paper suggests a research & education (R&E) model for the gifted in science education. The model has been developed under three assumptions. The first is that using the sequences of a gifted educational program designed to facilitate the process will assist in gifted students' construction of scientific knowledge and comprehension of laboratory practice through concrete experimental experience. The second is that gifted students will be able to apply this learning to further study using and extending scientific knowledge and experience. The third is that challenging tasks and feedback at the requisite stage of development will improve instructional effectiveness. The R&E Model has five phases: engaging, exploring, planning, performing and elaborating; furthermore, it suggests roles for the mentee and mentor. The R&E model has two functions for gifted education. The first is providing guidance for gifted curriculum developers as they design a mentor program, and the second is helping a mentor improve instructional effectiveness through use of strategies. This model has potentials to educate the gifted students in the Science Education Institute for the Gifted.

• Architectural research
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• v.15 no.3
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• pp.123-132
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• 2013

Design studio is a unique type of course in architecture and interior design education, in which learning is based on student-instructor interaction and learning by doing; yet little research has been conducted on student perceptions of the ideal design studio instructor. The purpose of this paper was to identify characteristics of the ideal studio instructor from student perspectives. Three award-winning design studio instructors' studio activities were observed, and the three instructors and their 40 students were interviewed. As a result, characteristics in four categories were identified. The author argues that providing customized feedback and allowing student autonomy are the two distinct characteristics that students value in design studio as compared to students in other fields or type of courses. The findings provide valuable insights to design educators who would like to strengthen their teaching studios by listening to student voices.

• The Mathematical Education
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• v.43 no.1
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• pp.70-85
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• 2004

Comparing to the other subject, hierarchy among mathematical contents is strong from the perspective of knowledge order as grades go up. That is, the knowledge that students already have learned, are learning and will learn are closed related from grade to grade. We expect students to be proactive and creative in studying mathematics, which is the goal of 21th century, analyzing their. knowledge structure based on the hierarchy of knowledge through assessment. Especially, using computer system we provide students with substantial feedback for the assessment as well as objective validity is increased along with speedy and exact process in a bid to help students' mathematical understanding grow. This paper seeks to analyze the assessment data by applying knowledge spaces to computer system and develops efficient methods based on the analyzed results, to diagram each student's knowledge structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2211-2220
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• 1998

This paper presetns an adaptive critic self-learning control system with cerebellar model articulation controller (CMAC)-based decoder integrated with the associative search element (ASE) and adatpive critic element(ACE)- based scheme. The tast of the system is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. The problem is that error feedback information is limited. This problem can be sloved when some adaptive control devices are involved. The ASE incorporates prediction information for reinforrcement from a critic to produce evaluative information for the plant. The CMAC-based decoder interprets one state to a set of patways into the ASE/ACE. These signals correspond to te current state and its possible preceding action states. The CMAC's information interpolation improves the learning speed. And design inverted pendulum hardware system to show control capability with neural network.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.530-532
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• 1998

In this paper, We Designed the torque compensatory controller for robot manipulator using modified chaotic neural networks with self feedback loop. The proposed torque compensatory controller compensate torque of the PD controller. In order to estimate the proposed controller, we implemented to the Cartesian space control of three-axis PUMA robot and compared the simulation results with recurrent neural networks(RNNs) controller. Simulation results show that the learning error drastically decrease at on-line learning. The proposed CNNs controller shows much better control performance and shorter processing time compared to the recurrent neural network controller in the robot trajectory control.