• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.

• Journal of The Korean Society of Integrative Medicine
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• v.7 no.4
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• pp.23-32
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• 2019

Purpose : The purpose of this study was to evaluate the effects of multi-sensory treatment programs on attention and learning in ADHD children. Methods : The program was provided for 50 minutes twice a week for a total of 12 times over 6 weeks. The FAIR concentration test was used to identify the children's concentration of attention before and after the intervention. The children's learning ability was evaluated using K-ABC. Results : When attention was evaluated using FAIR, there was a significant increase in all dependencies of performance value (P), quality value (Q), and continuity value (C) (p>.05). In addition, when learning ability was evaluated using K-ABC, learning ability in general increased significantly (p>.05). The multi-sensory play therapy program had a positive effect on the children's attention and learning ability and thus it is a positive intervention method for children with ADHD. Conclusion : In addition to providing challenging activities, the program showed that it was possible to elicit the children's interest by engaging a variety of senses at the same time. This is believed to have motivated them internally to engage actively in the program.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.82-89
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• 2017

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.68-74
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• 2019

Along with the deeper architecture in the deep learning approaches, the need for the data becomes very big. In the real problem, to get huge data in some disciplines is very costly. Therefore, learning on limited data in the recent years turns to be a very appealing area. Meta-learning offers a new perspective to learn a model with this limitation. A state-of-the-art model that is made using a meta-learning framework, Meta-SGD, is proposed with a key idea of learning a hyperparameter or a learning rate of the fast adaptation stage in the outer update. However, this learning rate usually is set to be very small. In consequence, the objective function of SGD will give a little improvement to our weight parameters. In other words, the prior is being a key value of getting a good adaptation. As a goal of meta-learning approaches, learning using a single gradient step in the inner update may lead to a bad performance. Especially if the prior that we use is far from the expected one, or it works in the opposite way that it is very effective to adapt the model. By this reason, we propose to add a weight term to decrease, or increase in some conditions, the effect of this prior. The experiment on few-shot learning shows that emphasizing or weakening the prior can give better performance than using its original value.

• Education of Primary School Mathematics
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• v.6 no.2
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• pp.55-64
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• 2002

According to changing the society rapidly in the 21s1 century, the self-regulated learning ability is considered as an ability of which people should carry on their lives. The purpose of this study was to investigate prospective elementary school teachers in mathematics teaching method class in terms of the following areas: (1) the degree of their abilities shown the lower level factors of motivated strategies for learning such as self-efficacy, intrinsic value, anxiety, cognitive strategy use, and self-regulation (2) relations between factors of motivated strategies for loaming and performance of prospective elementary school teachers The results show that the prospective elementary school teachers showed above the mean value of the motivated strategies for learning and there are positive relations among lower level factors of motivated strategies fur learning except anxiety, positive relation between motivated strategies for learning and achievement. In order to help the prospective elementary school teacher to improve their motivated strategies fur learning in their elementary mathematics teaching method lecture, several methods such as mathematical connections to real world problem, history of mathematics and interview with mathematicians and application of feller's ARCS model to elementary mathematics education are suggested.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.803-829
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• 2022

With the advances in technology and the trend towards increased computer-based experiential learning in education settings, business games are being increasingly used by business educators. This article utilizes Toulmin's Sensemaking Framework to investigate the sensemaking process of business professionals to reveal how they consciously reason about the value of business games for learning complex business concepts and principles. Using the analysis of responses from 43 business professionals, our study identifies key areas where business professionals find value in business games and the limitations of using business games. First, business games are found to be an effective tool when teaching practical business skill sets to business professionals. Second, business games enhance the overall learning process in professional business training. Third, despite the advantages, some pitfalls in applying business games to practice are found. We also found sub-themes, claims, and argument patterns of how business professionals evaluate the value of business games through a grounded theory qualitative analysis method. Analysis results show several ground-warrant patterns exist in the arguments on values of business games including general principle - causal reasoning, personal experience - generalization, and personal projection - generalization. With these findings, we believe this paper contributes to the theory and practice of business game design, development, and the game playing and learning process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.15-23
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• 2022

Among the learning methods for Continuous Learning environments, "Learning without Forgetting" has fixed regularization strengths, which can lead to poor performance in environments where various data are received. We suggest a way to set weights variable by identifying the features of the data we want to learn. We applied weights adaptively using correlation and complexity. Scenarios with various data are used for evaluation and experiments showed accuracy increases by up to 5% in the new task and up to 11% in the previous task. In addition, it was found that the adaptive weight value obtained by the algorithm proposed in this paper, approached the optimal weight value calculated manually by repeated experiments for each experimental scenario. The correlation coefficient value is 0.739, and overall average task accuracy increased. It can be seen that the method of this paper sets an appropriate lambda value every time a new task is learned, and derives the optimal result value in various scenarios.

• The Journal of Information Technology
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• v.4 no.1
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• pp.71-79
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• 2001

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and configuration of the range for the initial connecting weight according to the different maximum target value from minimum target value. This algorithm is expected to escaping from the local minimum and make the best environment for the convergence. On the simulation tested this algorithm on three learning pattern. The first was 3-parity problem learning, the second was $7{\times}5$ dot alphabetic font learning and the third was handwritten primitive strokes learning. In three examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in the alphabetic font and handwritten primitive strokes learning, the neural network enhanced to loaming efficient about 27%~57.2% for the standard back propagation(SBP).

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.69-80
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• 2018

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.