• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Smart Structures and Systems
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• 제22권2호
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.

• International journal of advanced smart convergence
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• 제9권1호
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

• 대한인간공학회지
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• 제31권4호
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• Animal cells and systems
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• 제15권2호
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• pp.115-121
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• 2011

Previous studies have demonstrated that N-methyl-D-aspartate (NMDA) receptors and acetylcholine receptors are related to learning and memory in rat and mice. In this study, we examined the effects of MK-801, a non-competitive NMDA receptor antagonist, on learning and memory in zebrafish using a passive avoidance test. We further tested whether or not nicotine, a nicotinic acetylcholine receptor agonist, and physostigmine, an acetylcholinesterase inhibitor, reverse the effects of MK-801. Crossing time was increased significantly in the training and test sessions for the controls. When 20 ${\mu}M$ MK-801 was administered prior to the training session, the crossing time did not increase in either session. The MK-801-induced learning deficit was rescued by pretreatment with 20 ${\mu}M$ physostigmine, and crossing time was increased in the training and test sessions compared to the MK-801-treated zebrafish. Further, the MK-801-induced learning deficit was prevented by pretreatment with 20 ${\mu}M$ nicotine, and crossing time was increased in the training session but not in the test session. These results show that MK-801 induced a learning deficit in zebrafish that was prevented by pretreatment with nicotine and physostigmine.

• 품질경영학회지
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• 제50권3호
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• pp.459-471
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• 2022

Purpose: The purpose of this study was to increase prediction accuracy for an anomaly interval identified using an artificial intelligence-based time series anomaly detection technique by establishing a pre-processing process. Methods: Significant variables were extracted by applying feature selection techniques, and anomalies were derived using the TadGAN time series anomaly detection algorithm. After applying machine learning and deep learning methodologies using normal section data (excluding anomaly sections), the explanatory power of the anomaly sections was demonstrated through performance comparison. Results: The results of the machine learning methodology, the performance was the best when SHAP and TadGAN were applied, and the results in the deep learning, the performance was excellent when Chi-square Test and TadGAN were applied. Comparing each performance with the papers applied with a Conventional methodology using the same data, it can be seen that the performance of the MLR was significantly improved to 15%, Random Forest to 24%, XGBoost to 30%, Lasso Regression to 73%, LSTM to 17% and GRU to 19%. Conclusion: Based on the proposed process, when detecting unsupervised learning anomalies of data that are not actually labeled in various fields such as cyber security, financial sector, behavior pattern field, SNS. It is expected to prove the accuracy and explanation of the anomaly detection section and improve the performance of the model.

• 한국초등수학교육학회지
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• 제11권2호
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• pp.161-176
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• 2007

모든 학습에서 시간은 매우 중요한 자원이다. 수학학습에서는 더욱 더 그러하다. 본 논문에서는 수학학습에서 시간이 어떻게 사용되며 이와 관련된 수학학습과의 관계를 조사 분석하였다. 학습자에게 주어지는 학습 시간은 귀중하고 가치 있게 또한 의미 있는 시간을 영위하도록 학부모나 교사들은 배려해야만 한다. 학습자는 학습의 진정한 주체가 되어 자율적이고 연속적이며 끊임없는 진정한 수행이 되도록 노력해야 한다. 누구에게나 물리적 시간은 같지만 어떻게 보내느냐에 따라서 그 질 및 가치는 매우 다르기 때문이다. 이러한 맥락에서 본 논문이 수학학습의 질을 높이는데 기여하는데 그 의의를 두고자 한다.

• International Journal of Contents
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• 제5권1호
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• pp.15-20
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• 2009

In this paper, we propose u-Learning management systems which are designed and implemented based on learning activities oriented components. The proposed systems are composed of components which can process the functionalities for coming into actions of learning activities. Specially, each component is broken into class units by which learning activities of users can be performed on various devices. When users by to connect the proposed learning management system, the system explores devices of users and the corresponding connection program, and then selects components that are fitted to the activities and combines them in a real-time. Our system provides u-Learning environment so that users can use the learning activity services taking no influence on time, place, various devices and programs. That is different from traditional e-Learning system which cannot support various devices of users directly.

• International Journal of Contents
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• 제1권2호
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• pp.10-12
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• 2005

Along with development of the Internet, education is achieved on-line actively. Therefore, interest about computer aided learning is growing. By a lot of advantages such as expense and time-saving side, this type of learning is widening area gradually. In this paper we discuss some of the learning technology, such as e-learning, m-learning, and u-learning.

• 정보교육학회논문지
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• 제5권3호
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• pp.351-363
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• 2001

기존의 웹기반 교육의 시스템은 학습자들간의 상호작용을 이루어 내지 못하고 있었다. 이러한 대안으로 다중의 학습자 상호작용을 위한 시스템들이 개발되었다. 현재 이러한 시스템은 웹기반 교육분야에도 널리 활용이 되고 있는 실정이다. 그러나 현재 이루어지고 있는 다중 학습자들간의 상호작용과 피드백은 실제적으로 많은 시간차를 염두 해 두고 있어 그 효과와 신뢰성이 매우 낮은 편이다. 이러한 문제에 대한 해결책으로 본 연구에서는 실시간으로 다중의 학습자가 서로의 의견을 교환하며 상호작용을 할 수 있는 시스템을 개발하고자 한다. 또한 퀴즈학습이라는 게임형 웹기반 교육과 접목시켜 학습자의 문제해결 과정 안에서 이러한 상호작용을 이끌어 내어 학습자에게 학습의 집중도와 흥미를 유발하고 학습 효과의 극대화를 기대할 수 있다. 이에 본 연구에서는 다중 학습자 상호작용을 위한 웹기반 실시간 퀴즈학습 시스템을 설계하고 구현한다.