• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.145-151
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• 2001

An extension of interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupter by both process and output disturbances is presented. With only an assumed upper bound on the order of the system and an assumed upper bound on the number of disturbance frequencies, it is shown that both the disturbance-free model and disturbance effect can be recovered exactly from disturbance-corrupted input-output data without direct measurement of the periodic disturbances. The rich information returned by the identification can be used by a performance-oriented model-based loaming or repetitive control system to eliminate unwanted periodic disturbances.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.1
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• pp.90-97
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• 2004

DC motor requires a rotor speed sensor for accurate speed control. The speed sensors such as resolvers and encoders are used as speed detectors. but they increase cost and size of the motor and restrict the industrial drive applications. So in these days. many Papers have reported on the sensorless operation or DC motor(3)-(5). This paper Presents a new sensorless strategy using neural networks(6)-(8). Neural network structure has three layers which are input layer. hidden layer and output layer. The optimal neural network structure was tracked down by trial and error and it was found that 4-16-1 neural network has given suitable results for the instantaneous rotor speed. Also. learning method is very important in neural network. Supervised learning methods(8) are typically used to train the neural network for learning the input/output pattern presented. The back-propagation technique adjusts the neural network weights during training. The rotor speed is gained by weights and four inputs to the neural network. The experimental results were found satisfactory in both the independency on machine parameters and the insensitivity to the load condition.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.65-71
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• 2018

Using mobile technology in educational and learning environments has attracted a lot of attention in recent years. In this mobile environment, mobile phones have been used to enhance the effectiveness of education in the field, which has been recognized through numerous experimental studies so far. The study was proposed and conducted to find out how much the use of mobile phones can have to improve the grammatical knowledge of EFL students. Introduction of 95 intermediate courses to Chungnam area The second grade students of 4-year college participated in this study. Everyone in the experimental and control groups was given the opportunity to review and recur to use the six grammar formats, including the current complete tense, simple past tense, direct and indirect question sentences, and comparative and superative-based methods. During the class discussion, the participants of the group record their voice on their cell phones, analyze the mistakes in the expressions recorded as a task after the class, and explain the results in the next session. However, in the class of the control group participants, this recording process is omitted. Participants benefited from mobile learning were much more positive in multidimensional grammar tests than those in control groups.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1372-1377
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• 2013

In this Paper, a back propagation neural network learning algorithm based on the complex multilayer perceptron is represented for controling and detecting interference of the received signals in cellular mobile communication system. We proposed neural network adaptive correlator which has fast convergence rate and good performance with combining back propagation neural network and the receiver of cellular. We analyzed and proved that NNAC has lower bit error probability than that of traditional RAKE receiver through results of computer simulation in the presence of the tone and narrow-band interference and the co-channel interference.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.163-168
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• 2024

Technologies that are considered to be a part of the fourth industrial revolution include holograms, augmented reality, and virtual reality. As technology advances, the industry's scale is growing quickly as well. While the development of technology for direct use is moving slowly, awareness of floating holograms-which are considered realistic content-is growing as the industry's scale and rate of technological advancement continue to accelerate. Specifically, holograms that have been incorporated into museums and exhibition spaces are static forms of content that viewers gaze at inertly. Additionally, their use in educational fields is very passive and has a low rate of utilization. Therefore, in order to improve usability from the viewpoint of viewers of realistic content, such as exhibition halls or museums, we introduce realistic content control technology in this study using a machine learning framework to recognize hands. It is anticipated that using the study's findings, manipulating realistic content independently will enhance comprehension of objects presented as realistic content and boost its applicability in the industrial and educational domains.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.254-257
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• 1993

This paper presents a new method to implement Hebbian learning method on artificial neural network. In hebbian learning algorithm, complexity in terms of multiplications is high. To save the chip area, we consider a new learning circuit. By calculating similarity, or correlation between $X_i$ and $O_i$, large portion of circuits commonly used in conventional neural networks is not necessary for this new hebbian learning circuit named COR. The output signals of COR is applied to weight storage capacitors for direct control the voltages of the capacitors. The weighted sum, ${\Sigma}W_{ij}O_j$, is realized by multipliers, whose output currents are summed up in one line which goes to learning circuit or output circuit. The drain current of the multiplier can produce positive or negative synaptic weights. The pass transistor selects eight learning mode or recall mode. The layout of an learnable six-neuron fully connected Hopfield neural network is designed, and is simulated using PSPICE. The network memorizes, and retrieves the patterns correctly under the existence of minor noises.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.215-222
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• 2022

The purpose of this study was to analyze effects of blended learning on abilities to use smart-phone and applications among students with intellectual disabilities. To do this, 30 students with intellectual disabilities who were enrolled in special school and special classroom in Jellanam-do and Gwanju metropolitan city were selected for this study, and were placed experimental and control groups of 15 students. The experimental group was provided with blended learning in which direct instruction, anchored instruction, experience activities, and community-based instruction were combined, and the control group was provided with traditional teacher-centered lecture style intervention. Pre-, post-, and maintenance evaluations were conducted two weeks after intervention. The collected data was analyzed the repeated two-way ANOVA. In the result of study, the experimental group improved on abilities to use smart-phone and applications than control group. Blended learning is a teaching method that can be a usefully used when educating how to use smart-phone and applications to students with intellectual disabilities.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.4
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• pp.595-603
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• 2002

Purpose : The purpose of this study was to identify the effect of clinical nurse's locus of control of personality on job stress and job satisfaction and organizational commitment out of organizational effectiveness. Methods : The subjects were 463 staff nurses, who were employed in four university hospitals located in Seoul, Pusan and Kyungki-do. Data was collected from October 4th to October 14th in 2000 by a self-report questionnaire. Data was analyzed by the SAS for the general characteristics of the subjects, descriptive statistics, test for reliability and correlations. The effect of variables were tested using the Lisrel 8.12(a) program. Results : With the findings from this study, the internal-external locus of control affects job stress directly. Also it affects job satisfaction directly and via job stress indirectly. But it affects organizational commitment only via job stress and job satisfaction indirectly. Direct effect of locus of control to organizational commitment is not significant. Job stress affects job satisfaction and organizational commitment out of organizational effectiveness directly. Finally, job satisfaction was direct predictor of organizational commitment. Conclusion : Therefore, nursing managers ought to develop social learning programs to change the perception of individual personality and job stress management programs in order to improve organizational effectiveness.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.401-403
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• 1998

This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.606-611
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• 2014

The understanding of another's behavior is a fundamental cognitive ability for primates including humans. Recent neuro-physiological studies suggested that there is a direct matching algorithm from visual observation onto an individual's own motor repertories for interpreting cognitive ability. The mirror neurons are known as core regions and are handled as a functionality of intent recognition on the basis of imitative learning of an observed action which is acquired from visual-information of a goal-directed action. In this paper, we addressed previous works used to model the function and mechanisms of mirror neurons and proposed a computational model of a mirror neuron system which can be used in human-robot interaction environments. The major focus of the computation model is the reproduction of an individual's motor repertory with different embodiments. The model's aim is the design of a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and execution. We also propose a biologically inspired plausible equation model.