• The Journal of Korean Physical Therapy
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• 제31권4호
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• pp.212-215
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• 2019

Purpose: This study examined whether there is a difference in motor learning through short-term repetitive movement practice in stroke survivors with a unilateral brain injury compared to normal elderly participants. Methods: Twenty-six subjects who were divided into a stroke group (n=13) or sex-aged matched normal elder group (n=13) participated in this study. To evaluate the effects of motor learning, the participants conducted a tracking task for visuomotor coordination. The accuracy index was calculated for each trial. Both groups received repetitive tracking task training of metacarpophalangeal joint for 50 trials. The stroke group performed a tracking task in the upper extremity insi-lesional to the damaged hemisphere, and the normal elder group performed the upper extremity matched for the same side. Results: Two-way repetitive ANOVA revealed a significant difference in the interactions ($time{\times}group$) and time effects. These results indicated that the motor skill improved in both the stroke and normal elder group with a tracking task. On the other hand, the stroke group showed lesser motor learning skill than the normal elder group, in comparison with the amount of motor learning improvement. Conclusion: These results provide novel evidence that stroke survivors with unilateral brain damage might have difficulty in performing ipsilateral movement as well as in motor learning with the ipsilateral upper limb, compared to normal elderly participants.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제12권3호
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• pp.235-249
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• 2008

The paper used the quantitative method to compare Chinese students' and teachers' mathematics related beliefs, including beliefs about mathematics, mathematics teaching and learning. The result indicated that there are some differences between their beliefs. Based on the results, we give some recommendations.

• Educational Technology International
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• 제22권2호
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• pp.255-283
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• 2021

In the early 2020, COVID-19 changed the traditional way of teaching and learning. This paper aimed to explore the impact of college students' perception of course quality on their online learning satisfaction. A total of 4,812 valid samples were extracted, and the difference analysis and hierarchical regression analysis were used to make an empirical analysis of college students' online learning satisfaction. The research results were as follows. Firstly, there was no difference in online learning satisfaction among students by gender and grade. Secondly, learning assessment, course materials, course activities and learner interaction, and course production had a significant positive impact on online learning satisfaction. Course overview and course objectives had an insignificant correlation with online learning satisfaction. Thirdly, the total effect of online learning satisfaction was as follows. Course production had the greatest effect, followed by course activities and student-student interactions, followed by course materials. It was the learning evaluation that showed the least effect. This study can provide empirical reference for college teachers on how to continuously improve online teaching and increase students' satisfaction with online learning.

• 동의생리병리학회지
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• 제19권1호
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• pp.254-261
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• 2005

The effects on memory and learning ability of the Korean herbal medicine, Xingyo-tang(XGT, 神交湯), which consists of Ginseng Radix(人蔘) 4 g, Liriopis Tuber(麥門冬) 40 g, Morindae Officinalis Radix(巴戟天) 40 g, Biotae Semen(柏子仁) 20 g, Dioscoreae Rhizoma(山藥) 40 g, Euryales Semen(?實) 20 g, Scrophulariae Radix(玄蔘) 40 g, Salviae Miltiorrhizae Radix(丹蔘) 12 g, Poria(茯神) 12 g, Cuscutae Semen(免絲子) 40 g, was investigated. The effects of XGT on learning and memory performance were examined in normal or memory impaired mice by using avoidance tests, Pentobarbital -induced sleep test, fear conditioning task, novel object recognition task, and water maze task. Hot water extract from XGT was used for the studies. Learning ability and memory are based on modifications of synaptic strength among neurons that are simultaneously active. Enhanced synaptic coincidence detection leads to better learning and memory. The XGT-treated (30 mg/100 g and 60 mg/100 g, p.o.) mice exhibit superior ability in learning and memorizing when performing various behavioral tasks. XGT did not affect the passive avoidance responses of normal mice in the step through and step down tests, the conditioned and unconditioned avoidance responses of normal mice in the shuttle box, lever press performance tests, and the ambulatory activity of normal mice in normal condition. In contrast, XGT produced ameliorating effects on the memory retrieval impairment induced by ethanol. XGT also improved the memory consolidation disability induced by electric convulsive shock (ECS). XGT extended the sleeping time induced by pentobarbital dose-dependently, suggesting its transquilizing or antianxiety action. These results suggest that XGT has an improving effect on the impaired learning through the effects on memory registration and retrieval.

• 한국학교수학회논문집
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• 제10권1호
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• pp.71-89
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• 2007

본 연구는 피드백 집단(과제 평가 결과에 따른 교정 피드백 집단 I, 단계 분기의 피드백학습 집단 II)과 일반학습 집단(집단 III)간의 수학 성취도와 수학에 대한 태도의 변화를 조사하였다. 수학 성취도에서, 피드백 집단(집단 I, 집단 II)은 일반학습 집단(집단 III)보다 향상을 보였으나(p<.05), 집단 I과 집단 II간에는 유의한 차이가 없었다 (p>.05). 그리고 하 수준에서 피드백 집단(집단 I, 집단 II)과 일반학습 집단(집단 III)간에는 유의한 차이가 있었지만(p<.05), 상 수준에서 피드백 집단(집단 I, 집단 II)과 일반학습 집단(집단 III)간에는 유의한 차이가 없었다(p>.05). 수학에 대한 태도에서, 피드백학습 집단(집단 I, 집단 II)은 일반학습 집단(집단 III)보다 향상을 보였으나(p<.05), 집단 I과 집단 II간에는 유의한 차이가 없었다(p>.05). 수학 성취수준(상 하위수준)에 따라서는, 집단 I과 집단 II간에는 유의한 차이가 없었지만(p>.05), 피드백 집단(집단 I, 집단 II)과 일반학습 집단(집단 III)간에는 유의한 차이가 있었다(p<.05).

• 한국수학교육학회지시리즈D:수학교육연구
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• 제22권4호
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• pp.305-318
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• 2019

To improve students' classroom engagement is not only the demand of curriculum revolution, but also the reflection of learning democracy. Students' responses and thinking are the main manifestations of students' participation in classroom learning. To reduce the amount of questions and increase the opportunities and time for students to think, this study, by employing SPSS, makes attempts to analyze the data by using multivariate GLM analysis to explore the effects of students' responses and thinking on learning results. The results indicated the students learning effect will be promoted through reducing the quantity and increasing the quality of question and adding the thinking opportunities.

• 산업경영시스템학회지
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• 제47권1호
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.