• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.63-69
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• 2021

Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2484-2491
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• 2009

This paper proposes robust state feedback control of asynchronous sequential machines with model uncertainty. The considered asynchronous machine is deterministic, but its state transition function is partially known before executing a control process. The main objective is to derive the existence condition for a corrective controller for which the behavior of the closed-loop system can match a prescribed model in spite of uncertain transitions. The proposed control scheme also has learning ability. The controller perceives true state transitions as it undergoes corrective actions and reflects the learned knowledge in the next step. An adaptation is made such that the controller can have the minimum number of state transitions to realize a model matching procedure. To demonstrate control construction and execution, a VHDL and FPGA implementation of the proposed control scheme is presented.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.7-14
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• 2010

Stable-state behaviors of asynchronous sequential machines represented as finite state machines can be corrected by feedback control schemes. In this paper, we propose a state feedback control scheme for input/state asynchronous machines with uncertain transitions. The considered asynchronous machine is deterministic, but its state transition function is partially known due to model uncertainty or inner logic errors. The control objective is to compensate the behavior of the closed-loop system so that it matches a sub-behavior of a prescribed model despite uncertain transitions. Furthermore, during the execution of corrective action, the controller reflects the exact knowledge of transitions into the next step, i.e., the range of the behavior of the closed-loop system can be enlarged through learning. The design procedure for the proposed controller is described in a case study.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.299-302
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• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.685-690
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• 2021

In this paper, we propose a deep learning-based transmit power control scheme to maximize the sum-rates while satisfying the minimum data-rate in downlink non-orthogonal multiple access (NOMA) systems. In downlink NOMA, we consider the co-channel interference that occurs from a base station other than the cell where the user is located, and the user feeds back the signal-to-interference plus noise power ratio (SINR) information instead of channel state information to reduce system feedback overhead. Therefore, the base station controls transmit power using only SINR information. The use of implicit SINR information has the advantage of decreasing the information dimension, but has disadvantage of reducing the data-rate. In this paper, we resolve this problem with deep learning-based training methods and show that the performance of training can be improved if the dimension of deep learning inputs is effectively reduced. Through simulation, we verify that the proposed deep learning-based power control scheme improves the sum-rate while satisfying the minimum data-rate.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.263-270
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• 2020

In this paper, In this paper, we describe the UAV system using image processing for autonomous quadcopters, where they can apply logistics, rescue work etc. we propose high-speed hovering height and posture control method based on state feedback control with CNN from camera because we can get image of the information only every 30ms. Finally, we show the advantages of proposed method by simulations and experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.71-78
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• 2001

Since web-based tutoring systems are generally composed with passive and static hypertext, they could not provide adaptive learning environments according to learning ability of each student. In this study, we suggest an intelligent tutoring system, which grasps the learning state of student and provides each student with dynamic learning materials suitable to individual feature based on learning result. It is an agent based system, in which, courseware knowledge for learning is effectively constructed, the proper feedback according to learning assessment is inferred, and it is given to each student.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.529-539
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• 2005

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to various practical positioning applications with various external loads is described in this paper. A novel modified pulse-width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of the standard PWM technique and that of the modified PWM technique shows that the performance of the proposed technique was significantly increased. A state-feedback controller with position, velocity and acceleration feedback was successfully implemented as a continuous controller. A switching algorithm for control parameters using a learning vector quantization neural network (LVQNN) has newly proposed, which classifies the external load of the pneumatic actuator. The effectiveness of this proposed control algorithm with smooth switching control has been demonstrated through experiments with various external loads.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.85.4-85
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• 2001

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion from that learnt. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is ...