• PNF and Movement
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• v.6 no.3
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• pp.19-28
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• 2008

Purpose : The objective of this study was to determine efficacy of sleep-dependent motor learning. Methods : This is a literature study with books and internet. We searched the PubMed, Science Direct, KISS and DBpia. Key words were Sleep-dependent, motor learning, RAM and LTP. Results : Procedural memory, like declarative memory, undergoes a slow, time-dependent period of consolidation. A process has recently been described wherein performance on some procedural task improves with the mere passage of time and has been termed "enhancement". Some studies have reported that the consolidation/enhancement of perceptual and motor skill is dependent on sleep. Specially, rapid-eye-movement(REM) sleep seems to benefit procedural aspects of memory. Conclusion : Motor learning is very important for CNS injury patients. And also distribution of practice sessions is important because REM sleep is to benefit procedural aspects of memory consolidation.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.12
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• pp.85-91
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• 2021

In our daily life, artificial intelligence performs simple and complicated tasks like us, including operating mobile phones and working at homes and workplaces. Artificial intelligence is used in industrial technology for diagnosing various types of equipment using the machine learning technology. This study presents a fault mode effect analysis (FMEA) of start motors using machine learning and big data. Through multiple data collection, we observed that the primary failure of the start motor was caused by the melting of the magnetic switch inside the start motor causing it to fail. Long-short-term memory (LSTM) was used to diagnose the condition of the magnetic locations, and synthetic data were generated using the synthetic minority oversampling technique (SMOTE). This technique has the advantage of increasing the data accuracy. LSTM can also predict a start motor failure.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.133-140
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• 2011

Muscle misuse dysphonia (MMD) is defined as a behavioral voice disorder resulting from inappropriate contractions of intrinsic and/or extrinsic laryngeal muscles. The purpose of this study was to investigate the effect of motor learning guided laryngeal motor control therapy (MLG-LMCT) which is designed to improve an existing LMT and further the effective voice treatment on people with muscle misuse dysphonia. Forty-six people with MMD (M:F=16:30) participated in this study. The voice samples of the participants were recorded to investigate the effect of MLG-LMCT before and after the voice therapy. Voice samples were analyzed via electro-glotto-graph (EGG). Contact quotient (CQ), speed quotient (SQ), and waveform were reported. In addition, perceptual and acoustical evaluation were conducted to determine the change of voice improvement after treatment. The experimenter massaged the tensioned muscles around the neck. In order to find more proper phonation the experimenter showed the subjects their EGG wave forms as to whether or not they are moving the vocal folds to the appropriate position. Therefore, the EGG wave forms were used as a type of visual feedback. With the wave form, the experimenter helped subjects move the vocal folds and laryngeal muscles to find more proper voice production. The sensory stimuli from the experimenter gradually faded out. A paired dependent t- test revealed that there was significant differences in CQ between pre- and post-therapy. Perceptually, overall, rough, breathy, strain, and transition were significantly reduced. Acoustically, there were significant differences in Fo, jitter, shimmer, and NHR. After using MLG-LMCT, most of the subjects showed improvements in voice quality. The results from this study led us to the following conclusions: Motor learning guided laryngeal motor control therapy (MLG-LMCT) has reduces muscle misuse dysphonia. These results may occur because a visual feedback from EGG wave form can maintain the effect of the muscle tension reduction from laryngeal manual therapy. In case of people with MMD who reduced muscle tension from the therapy (LMT) but, not appropriately manipulating the location of larynx or adducting the vocal folds, MLG-LMCT might be an alternative therapy approach.

• Proceedings of the KIPE Conference
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• 2003.07b
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• pp.584-587
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• 2003

Iterative learning control is an approach to improve the transient response of systems that operate repetitively over a fixed time interval. It is useful for the system where the system output follows the different type input, in case of design or modeling uncertainty In this paper, we introduce the concept of iterative learning control and then apply the learning control algorithm for multi-motor system for performance Improvement.

• PNF and Movement
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• v.13 no.2
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• pp.55-72
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• 2015

Purpose: The purpose of this study was to propose a new therapy algorithm that combines motor imagery and physiotherapy as a physiotherapeutic clinical intervention technique that can stimulate the recovery of damaged physical function for patients with stroke. Methods: A variety of scientific research results related to motor imagery were reviewed and analyzed to investigate their applicability to physiotherapy in clinics. Results: As a new therapy algorithm for the therapeutic approach of motor imagery in stroke rehabilitation, a therapy algorithm that combines motor imagery with physiotherapy is proposed, which consists of three stages or steps: STEP 1 motor imagery familiarization, STEP 2 explicit learning stage, and STEP 3 implicit learning. Conclusion: The new therapy algorithm proposed in this study is expected to be a very useful clinical therapeutic approach for stimulating the recovery of damaged physical function in patients with stroke. It is believed that it will be necessary to confirm and standardize the effects of the therapeutic algorithm proposed in this study in the future by conducting diverse clinical studies.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.107-113
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• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.12
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• pp.691-696
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for induction motor speed control. When induction motor is continuously used long time, its electrical and mechanical Parameters will change, which degrade the Performance of PID controller considerably. This Paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using dSPACE(DS1102) board are performed to verify the effectiveness of the proposed scheme.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.2
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• pp.15-19
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• 2022

Purpose: This pilot study aimed to clarify features of motor module during walking in exercise experts who experienced lately repeated training for sports skill. To identify motor modules, autoencoder machine learning algorithm was used, and modules were extracted from muscle activities of lower extremities. Research design, data and methodology: A total of 10 university students were participated. 5 students did not experience any sports training before, and 5 students did experience sports training more than 5 years. Eight muscle activities of dominant lower extremity were measured. After modules were extracted by autoencoder, the numbers of modules and spatial muscle weight values were compared between two groups. Results: There was no significant difference in the minimal number of motor modules that explain more than 90% of original data between groups. However, in similarity analysis, three motor modules were shown high similarity (r>0.8) while one module was shown low similarity (r<0.5). Conclusions: This study found not only common motor modules between exercise novice and expert during walking, but also found that a specific motor module, which would be associated with high motor control ability to distinguish the level of motor performance in the field of sports.

• Physical Therapy Korea
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• v.13 no.3
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• pp.24-32
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• 2006

Theoretical framework of motor learning is used to enhance perceptual motor skill in physical therapy intervention, which can be subdivided into two main types-explicit and implicit. The purpose of this study was to examine whether stroke patients with unilateral brain damage learn implicitly a motor skill using the arm ipsilateral to the damaged hemisphere. Speculation then followed as to the formation of therapeutic plans and instructions provided to patients with stroke. 20 patients with stroke and 20 normal participants were recruited. All the subjects practiced serial reaction time tasks for 30 minutes a day and retention tests on the following day. The tasks and tests involved pressing the corresponding buttons to 4 colored circles presented on a computer screen as quickly and accurately as possible. Patients with stroke responded more slowly than controls. However, both groups showed decreased reaction time in the experimental and retention periods. Also, there was no significant difference between both groups regarding explicit knowledge of consecutive order. Therefore, patients with stoke had the ability to learn implicitly a perceptual motor skill. Prescriptive instruction using implicit and explicit feedback may be beneficial for motor skill learning in physical therapy intervention for patients with brain damage.