• The Journal of Korean Physical Therapy
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• v.15 no.2
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• pp.75-84
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• 2003

The purpose of this study was to review of mechanism and application methodology about mental practice. The mental practice is symbolic rehearsal of physical activity in the absence of any gross muscular movements. Human have the ability to generate mental correlates of perceptual and motor events without any triggering external stimulus, a function known as imagery, Practice produces both internal and external sensory consequences which are thought to be essential for learning to occur, It is for this reason that mental practice, rehearsal of skill in imagination rather than by overt physical activity, has intrigued theorists, especially those interested in cognitive process. Several studies in sport psychology have shown that mental practice can be effective in optimizing the execution of movements in athletes and help novice learner in the incremental acquisition of new skilled behaviors. There are many theories of mental practice for explaining the positive effect In skill learning and performance. Most tenable theories are symbolic learning theory, psyconeuromuscular theory, Paivio's theory, regional cerebral blood flow theory, motivation theory, modeling theory, mental and muscle movement nodes theory, insight theory, selective attention theory, and attention-arousal set theory etc.. The factors for influencing to effects of mental practice are application form, application period, time for length of the mental practice, number of repetition, existence of physical practice.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.534-540
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• 1996

This paper presents an Adaptive Time-Delay Recurrent Neural Network (ATRN) for learning and recognition of temporal correlations of temporal patterns. The ATRN employs adaptive time-delays and recurrent connections, which are inspired from neurobiology. In the ATRN, the adaptive time-delays make the ATRN choose the optimal values of time-delays for the temporal location of the important information in the input parrerns, and the recurrent connections enable the network to encode and integrate temporal information of sequences which have arbitrary interval time and arbitrary length of temporal context. The ATRN described in this paper, ATNN proposed by Lin, and TDNN introduced by Waibel were simulated and applied to the chaotic time series preditcion of Mackey-Glass delay-differential equation. The simulation results show that the normalized mean square error (NMSE) of ATRN is 0.0026, while the NMSE values of ATNN and TDNN are 0.014, 0.0117, respectively, and in temporal learning, employing recurrent links in the network is more effective than putting multiple time-delays into the neurons. The best performance is attained bythe ATRN. This ATRN will be sell applicable for temporally continuous domains, such as speech recognition, moving object recognition, motor control, and time-series prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.276-282
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• 2006

PID controller has been widely used in industry. Because it has a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and a robustness to system parameters variation and different velocity command. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1547-1556
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• 2022

As mechanical facilities are interacting with each other, the failure of some equipment can affect the entire system, so it is necessary to quickly detect and diagnose the abnormality of mechanical equipment. This study proposes a deep learning model that can effectively diagnose abnormalities in rotating machinery and equipment. CNN is widely used for feature extraction and LSTMs are known to be effective in learning sequential information. In LSTM, the number of parameters and learning time increase as the length of input data increases. In this study, we propose a method of segmenting an input segment signal into shorter-length sub-segment signals, sequentially inputting them to CNN through a time-distributed method for extracting features, and inputting them into LSTM. A failure diagnosis test was performed using the vibration data collected from the motor for ventilation equipment installed at the urban railway station. The experiment showed an accuracy of 99.784% in fault diagnosis. It shows that the proposed method is effective in the fault diagnosis of rotating machinery and equipment.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• 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%.

• Journal of The Korean Association For Science Education
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• v.13 no.2
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• pp.296-307
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• 1993

The purpose of this study was to identify concretely how to improve inquiry learning. To put the purpose in detail : 1) to define the inquiry skills 2) to select the factors of inquiry skills appropriate to the content of Science I (earth science) textbook 3) to develop items which consist of experimental inquiry and concept inquiry in due proportion, to evalute inquiry skills 4) to analyze the relationship between high school students' cognitive levels and the achievement of science inquiry skills. To achieve these objectives, the investigator sampled 558 students in eleventh grade, living in Seoul, Chung-Ju and Kwang-Ju, and evaluated their cognitive levels and the achievement of science inquiry skills. The results of this study showed that the cognitive levels of students were lower than those identified in Piaget's work and that the achievement of science inquiry skills were low also. It may be thought that one of most important reasons to bring about those results is lacking in adaptation capability of science inquiry items and inquiry learning. So, it can be recommended as a way to heighten cognitive levels to make inquiry learning using the textbook content. In conclusion, the investigator make suggestions as follows : 1) to give inquiry learning which consist of experimental inquiry and concept inquiry in due proportion 2) to develop inquiry items to include content for evaluating inquiry learning, and test items for psycho-motor areas 3) to publish textbooks which motivate students' inquiry activities and develop their creative thinking, considering students' cognitive levels and inquiry skills.

• Journal of Practical Engineering Education
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• v.10 no.2
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• pp.113-118
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• 2018

The most important thing in vocational education and training is to enhance students' interest and understanding of the whole process of the production site. In this paper, we present a case on the implementation of tele-operation robot hand by learning factory based technology convergence education. It also suggests some points to be taken when applying the learning factory in the future curriculum. In order to implement the tele-operation robot hand, it is necessary to support the compulsory subjects of university level courses in domestic curriculum such as mechanic design, motor control, local communication implementation, sensing and feedback control. The educational research presented in this paper guides the students with the skills they need and understands the skills through self-study and practice, and implements the final products. This study will be useful as a base data when introducing the training process of training factory in the future.

• Physical Therapy Korea
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• v.8 no.1
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• pp.9-19
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• 2001

During therapy sessions, feedback is often provided concurrently by the physical therapist as the patient attempts to perform a movement and after the movement attempt. This feedback is provided to enhance the patient's balance abilities. However, recent studies in nondisabled populations have suggested that frequent feedback may be detrimental to retention or learning of motor skills. This study compared the effects of 100% relative frequency of knowledge of performance (KP) with 66% relative frequency of KP for motor learning on balance retraining in patients with hemiplegia. Twenty patients with hemiplegic were randomly assigned to one of two experimental groups. The acquisition phase consisted of 16 blocks of 5 trials for 2 days (80 total practice trials). The retention phase consisted of 2 blocks of a short-term retention test, one day after the end of the acquisition phase and a long-term retention test, one week after the end of the short-term retention test. In the 100% feedback condition, participants received feedback after every practice trial. A faded KP schedule was used in the 66% condition. No significant differences were found between the two groups during all experimental phases (acquisition and retention phases), (p>.05). However, there were significant decreases in balance index for both groups of acquisition phase (p<.05). These results suggest that 66% relative frequency of KP is not more effective than 100% relative frequency of KP with respect to retention over time when hemiparetic patients attempt to learn balance.

• Toxicological Research
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• v.28 no.2
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• pp.107-112
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• 2012

Polychlorinated biphenyls (PCBs) are persistent and bioaccumulative environmental pollutants. Recently, it is suggested that neurotoxic effects such as motor dysfunction and impairment in memory and learning have been associated with PCB exposure. However, structure relationship of PCB congeners with neurotoxic effects remains unknown. Since PKC signaling pathway is implicated in the modulation of motor behavior as well as learning and memory and the role of PKC are subspecies-specific, we attempted to study the effects of structurally distinct PCBs on the total PKC activity as well as subspecies of PKC in cerebellar granule cell culture model. Cells were exposed to 0, 25 and 50 ${\mu}M$ of PCB-126, PCB-169, PCB-114, PCB-157, PCB-52 and PCB-4 for 15 min. Cells were subsequently analyzed by [$^3H$] phorbol ester binding assay or immunoblotted against PKC-${\alpha}$ and -${\varepsilon}$ monoclonal antibodies. While non-dioxin-like-PCB (PCB-52 and PCB-4) induced a translocation of PKC-${\alpha}$ and -${\varepsilon}$ from cytosol to membrane fraction, dioxin-like PCBs (PCB-126, -169, -114, -157) had no effects. [$^3H$] Phorbol ester binding assay also revealed structure-dependent increase similar to translocation of PKC isozymes. While PCB-4 induced translocation of PKC-${\alpha}$ and -${\varepsilon}$ was inhibited by ROS inhibitor, the pattern of translocation was not affected in presence of AhR inhibitor. It is suggested that PCB-4-induced PKC activity may not be mediated via AhR-dependent pathway. Taken together, our findings suggest that chlorination of ortho-position in PCB may be a critical structural moiety associated with neurotoxic effects, which may be preferentially mediated via non-AhR-dependent pathway. Therefore, the present study may contribute to understanding the neurotoxic mechanism of PCBs as well as providing a basis for establishing a better neurotoxic assessment.