• Educational Technology International
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• v.11 no.1
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• pp.145-169
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• 2010

Many universities are searching for educational methods to cultivate problem-solving ability and cooperative learning ability or already trying to implement them. Problem Based Learning(PBL) and Action Learning(AL) are effective teaching and learning methods to cultivate men of talent qualified for problem-solving and cooperative learning abilities that universities are seeking after. PBL and AL have something in common in that learning is accomplished while learners are solving the authentic problem. But, in spite of this similarity, PBL and AL have differences. However, most literatures and cases on these two models introduce only the outline of commons and differences and do not provide teachers with actual helping aids to select a model appropriate for the actual design or operation of classes. Accordingly, many teachers usually select and utilize a familiar model rather than select a proper model to the nature of a subject and the educational goal. Teaching and learning methods or learning environment should be selected appropriately to the educational goal. This study indicates the characteristics of PBL and AL that are being introduced and utilized as a principal teaching and learning method of college education and then shows how this method can be realized in the university by comparing the cases of classes applied in two methods.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.428-433
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• 2008

The online advertising industry's business model undertakes the change from CPM (cost-per-mille)-based to CPC (cost-per-click)-based. However, due to the problem of 'Click Fraud', CPA (cost-per-action) has been regarded as a new step. For CPA, publishers need to get information after a user clicks an advertisement. Therefore, in CPA, the key is to get Conversion Action Data (CAD). This paper introduces two existing mechanisms for getting CAD, compare their characteristics, and analyze their limitations. Then the two new mechanisms are introduced and their requirements and feasibility are analyzed. Lastly, we compare the existing two and the new two mechanisms, and point out each mechanism's business possibility, value and Application Area. This paper will help publishers choose the most appropriate mechanism on the basis of their situation.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.153-165
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• 2007

This paper deals with the empirical Bayes two-action problem of testing $H_0\;:\;{\theta}{\leq}{\theta}_0$: versus $H_1\;:\;{\theta}>{\theta}_0$ using a linear error loss for some discrete nonexponential families having probability function either $$f_1(x{\mid}{\theta})=(x{\alpha}+1-{\theta}){\theta}^x\prod\limits_{j=0}^x\;(j{\alpha}+1)$$ or $$f_2(x{\mid}{\theta})=[{\theta}\prod\limits_{j=0}^{x-1}(j{\alpha}+1-{\theta})]/[\prod\limits_{j=0}^x\;(j{\alpha}+1)]$$. Two empirical Bayes tests ${\delta}_n^*\;and\;{\delta}_n^{**}$ are constructed. We have shown that both ${\delta}_n^*\;and\;{\delta}_n^{**}$ are asymptotically optimal, and their regrets converge to zero at an exponential decay rate O(exp(-cn)) for some c>0, where n is the number of historical data available when the present decision problem is considered.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.423-430
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• 2021

Aiming Graph convolutional networks (GCNs) have achieved outstanding performances on skeleton-based action recognition. However, several problems remain in existing GCN-based methods, and the problem of low recognition rate caused by single input data information has not been effectively solved. In this article, we propose a Dual-stream fusion method that combines video data and skeleton data. The two networks respectively identify skeleton data and video data and fuse the probabilities of the two outputs to achieve the effect of information fusion. Experiments on two large dataset, Kinetics and NTU-RGBC+D Human Action Dataset, illustrate that our proposed method achieves state-of-the-art. Compared with the traditional method, the recognition accuracy is improved better.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.4
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• pp.482-490
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• 2014

Purpose: This research compares self-leadership, team efficacy, problem solving processes and task satisfaction in response to teaching methods applied to nursing students, and determines whether variations exist. Method: This research experiments before and after the training of a nonequivalent group. The subjects were 36 learners of action learning methods and 39 learners of nursing course methods, and the research took place from October through December 2012. Results: Prior to the training, the general features and measurable variables of the two groups of subjects were similar, and self-leadership, team efficacy, problem solving process and task satisfaction in both groups were elevated compared to pre-training. In particular, in comparison with the nursing course, there was a notable difference in scores, the action learning method receiving high scores in the problem solving process (t=2.92, p=.005) and task satisfaction (t=2.54, p=.013) Conclusion: It is recommended that educators not only conduct the practice training course for teaching methods, but also incorporate action learning.

• Information Systems Review
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• v.10 no.2
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• pp.123-135
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• 2008

The online advertising industry's business model undertakes the change from CPM (cost-per-mille)-based to CPC(cost-per-click)-based. However, due to the problem of 'Click Fraud', CPA (cost-per-action) has been regarded as a new step. For CPA, publishers need to get information after a user clicks an advertisement. Therefore, in CPA, the key is to get Conversion Action Data (CAD). This paper introduces two existing mechanisms for getting CAD, compare their characteristics, and analyze their limitations. Then the two new mechanisms are introduced and their requirements and feasibility are analyzed. Lastly, we compare the existing two and the new two mechanisms, and point out each mechanism's business possibility, value and Application Area. This paper will help publishers choose the most appropriate mechanism on the basis of their situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.8
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• pp.1534-1544
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• 2002

The TDCSA(Time Delay Control with Switching Action) method, which consists of Time Delay Control(TDC) and a switching action of sliding mode control(SMC), has been proposed as a promising technique in the robust control area, where the plant has unknown dynamics with parameter variations and substantial disturbances are preset. When TDCSA is applied to the plant with saturation nonlinearity, however, the so-called windup phenomena are observed to arise, causing excessive overshoot and instability. The integral element of TDCSA and the saturation element of a plant cause the windup phenomena. There are two integral effects in TDCSA. One is the integral effect occurred by time delay estimation of TDC. Other is the integral term of an integral sliding surface. In order to solve this problem, we have proposed an anti-windup scheme method for TDCSA. The stability of the overall system has been proved for a class of nonlinear system. Experiment results show that the proposed method overcomes the windup problem of the TDCSA.

• The Journal of Korean Society for School & Community Health Education
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• v.17 no.2
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• pp.17-30
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• 2016

Objectives: The purpose of this study was to examine differences between the traditional lecture teaching learning method and action learning teaching learning method of nursing students' leadership, problem solving competence, creativity, and critical thinking disposition. Methods: This study was carried out from February 24, 2014 to May 30, 2014 with 171 nursing students (an experimental group of 88 students and a control group of 83 students) assigned among $2^{nd}$ year students attending nursing departments in U city. The Action learning teaching learning method applied to the experimental group by two experts. The Traditional lecture teaching learning method applied to the control group. In order to compare the differences, a pre and post questionnaire were used. The data gathered was analyzed using the SPSS 22. Results: Upon completion of education the nursing students' leadership, problem solving competence and creativity significantly increased both in the experimental group and in the control group compared to the pre testing phase. The critical thinking disposition significantly increased after education in the experimental group, but there was no significant change in the critical thinking disposition of the control group. Conclusion: To improve nursing students' leadership, problem solving competence, creativity, and critical thinking disposition the action learning teaching learning method appears to be more effective than the traditional lecture teaching learning method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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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.