• Journal of Technologic Dentistry
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• v.42 no.1
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• pp.1-8
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• 2020

Purpose: Among the virtual teeth generated by Deep Convolutional Generative Adversarial Networks (DCGAN), the optimal data was analyzed for the number of learning. Methods: We extracted 50 mandibular first molar occlusal surfaces and trained 4,000 epoch with DCGAN. The learning screen was saved every 50 times and evaluated on a Likert 5-point scale according to five classification criteria. Results were analyzed by one-way ANOVA and tukey HSD post hoc analysis (α = 0.05). Results: It was the highest with 83.90±6.32 in the number of group3 (2,050-3,000) learning and statistically significant in the group1 (50-1,000) and the group2 (1,050-2,000). Conclusion: Since there is a difference in the optimal virtual tooth generation according to the number of learning, it is necessary to analyze the learning frequency section in various ways.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.389-396
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• 2022

Deep Learning is based on a perceptron, and is currently being used in various fields such as image recognition, voice recognition, object detection, and drug development. Accordingly, a variety of learning algorithms have been proposed, and the number of neurons constituting a neural network varies greatly among researchers. This study analyzed the learning characteristics according to the number of neurons of the currently used SGD, momentum methods, AdaGrad, RMSProp, and Adam methods. To this end, a neural network was constructed with one input layer, three hidden layers, and one output layer. ReLU was applied to the activation function, cross entropy error (CEE) was applied to the loss function, and MNIST was used for the experimental dataset. As a result, it was concluded that the number of neurons 100-300, the algorithm Adam, and the number of learning (iteraction) 200 would be the most efficient in deep learning learning. This study will provide implications for the algorithm to be developed and the reference value of the number of neurons given new learning data in the future.

• The Mathematical Education
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• v.43 no.4
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• pp.419-440
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• 2004

The purpose of this paper is to know the effects on number and operation abilities of the 1st grade children of elementary school by applying teaching and learning activity throught communication. For this purpose, we have studied according to the following procedure. 1. We divised teaching and learning model through communication and applied in the actual teaching and learning activity. 2. We investigated the effects of number and operations abilities of the 1st grade children by applying teaching and learning activity through communication. To accomplish this purpose, we applied learning activity through communication to the 1st grade of 40 elementary school children for about six months(September 1, 1999 ～ February 20, 2000). In process of applying this model, we collected all sorts of cases in the children's learning activity and investigated children's response on learning activity through communication, interview with children and researcher's observation. We applied the model through communication in class and compared with the traditional learning. 1. In learning through communication, children could solve the problem in themselves with a sense of responsibility. 2. It was impossible to find out the degree of children's comprehension in the explanatory learning. But in the learning through communication, it was a great help to individualize and plan the learning because children express their ideas clearly. It has conclusion as follows. The learning activity through communication has effected on forming number and operations abilities of the 1st grade of elementary school children importantly. 1. Children have improved in the abilities through communication to express their own ideas. 2. Children have studied with a sense of responsibility not in the teacher-oriented learning but in the self-directed learning 3. Children could find out the mathematical concepts in themselves - correcting false concepts, reguiding concepts by errors, finding invisible errors, solving problems variously and knowing the easy method. 4. The activity through communication in mathematics was a base of children's individual learning and important data of next learning plan. 5. The mathematical concepts formed through communication had a high transfer of learning. 6. Children have taken pleasure of discovery and had affirmative attitude about mathematics learning. We can make sure that number and operations abilities of the 1st grade children are formed by applying teaching and learning activity through communication. However, help and control of teacher have to be with it.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.68-75
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• 1992

In this paper the back-propagation algorithm through omitting redundant learning has been proposed to improve learning speed. The proposed algorithm has been applied to XOR, Parity check and pattern recognition of hand-written numbers. The decrease of the number of patterns to be learned has been confirmed as learning proceeds even in early learning stage. The learning speed in pattern recognition of hand-written numbers is improved more than 2 times in various cases of hidden neuron numbers. It is observed that the improvement of learning speed becomes better as the number of patterns and the number of hidden numbers increase. The recognition rate of the proposed algorithm is nearly the same as that conventional method.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• The Journal of Information Systems
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• v.29 no.3
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• pp.35-52
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• 2020

Purpose Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications. Design/methodology/approach For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used. Findings This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

• Journal of Engineering Education Research
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• v.19 no.6
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• pp.57-62
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• 2016

In this work, we present a case study of using the assessments for the enhancement of students' learning motivation in engineering education. The assessments, given in between summative assessments such as midterms and finals, may have a component of formative evaluation, which are reported as very effective tools as the sources of feedback to improve teaching and learning. We studied how the students' performance is improved by additional tests in engineering education. Also, we examined the factors of successful results of the cooperative learning model, Student Teams-Achievement Division, which is based on imposing a number of tests, achieved in our previous work.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• Educational Technology International
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• v.10 no.2
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• pp.127-143
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• 2009

The purpose of this study was to identify the interaction patterns in distance only mode e-Learning. In order to investigate this study, messages shown in the electronic notice board were analyzed to see how interaction occurs between teacher and learner or learner and learner under the e-learning of cyber university. To analyze messages was applied according to the framework by Henri's contents analysis model. As a result of contents analysis on electronic board, the participative dimension was 399 messages. A learner put on 7~8 messages a day. The number of messages was low compared to the number of learners, but the number of inquiries was about 140. That means that each learner contacts and checks messages at least once a day. The meaning dimension was 600 units. The main interaction patterns were Interactive-social-cognitive-metacognitive. This means that e-Learning in distance only mode leads a positive attitude of learners as a self-directed learning, and needs teacher's well-structured instructional strategies for increasing interaction. In conclusion, social dimension and interactive dimension of messages support learners psychologically in the process of learning though they directly guide learning under the circumstances of e-learning lacking face-to-face element. It can be interpreted that the teacher's role is significantly important in order to attract learners' positive participation and cognitive and meta-cognitive dimension of messages and activities

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.