• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1724-1733
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• 2014

This paper presents a pattern recognition analysis of two spirals problem and optimization of Cascade Correlation learning algorithm using in combination with a non-monotone function as CosExp(cosine-modulated symmetric exponential function) and a monotone function as sigmoid function. In addition, the algorithm's optimization is attempted. By using genetic algorithms the optimization of the algorithm will attempt. In the first experiment, by using CosExp activation function for candidate neurons of the learning algorithm is analyzed the recognized pattern in input space of the two spirals problem. In the second experiment, CosExp function for output neurons is used. In the third experiment, the sigmoid activation functions with various parameters for candidate neurons in 8 pools and CosExp function for output neurons are used. In the fourth experiment, the parameters are composed of 8 pools and displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals. In the optimizing process, the number of hidden neurons was reduced from 28 to15, and finally the learning algorithm with 12 hidden neurons was optimized.

• Journal of the Korean School Mathematics Society
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• v.6 no.1
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• pp.101-113
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• 2003

The purpose of this study is to analyze students misunderstood types relate to trigonometric function and to devise its teaching method using GSP. To do this, we performed several steps as followings: First, we performed questionnaire survey to 70 students belong to second year at high school to find students comprehension degree about radian angle representation and trigonometric function graph. Second, we devised the teaching-learning materials relate to trigonometric function graph using GSP. And then, we used them in the class of 35 students who are at the time to learn trigonometric function in the first year at high school. Third, we conducted Questionnaire survey to students studied through teaching and learning materials using GSP. As a result of doing the survey, we found that general students were interested in the class using GSP and they could also operate computer without difficulty.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Journal of the Korean Data and Information Science Society
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• v.18 no.4
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• pp.1073-1079
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• 2007

The purpose of this study is to present the relationship between the computer learning and the paper and pencil learning through the math learning (simple computation and complex computation) and the cartoon learning and text learning. The canonical correlation and pairwise t-test of the SMR asymmetry brainwaves of the left and the right brain show the brainwaves with the respect to the manner in which they process information during the specified task by identifying the relative activity of the brainwaves of the left and the right brain. SMR brainwave which known as the scientific measure tool for the activity and the function of the neuronal cell were found to predict the level of the awakening to check the readiness of study preparation. Computer education as a medium of the individualized and the repetitive education shows the difference from the paper and the pencil test in the respect of the differences and the relationship of the SMR brainwave of the learning process.

• Journal of Physiology & Pathology in Korean Medicine
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• v.17 no.1
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• pp.50-56
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• 2003

This research was done to make the effective prescription and cope with various senile dementia. So Sprague-Dawley rats were injected with ibotenate to make a damage on learning and memory functions. At first acquisition test and retention rest were done in the Morris water maze. And to evaluate the effects of the sample drug(TBM) on choline acetyltranferase and acetylcholine esterase, immunoreactive measurement and enzymatic activity measuring were carried out. The ibotenic acid were injected to hippocampus CA1 and CA3 area. The results were as following. TBM improved the learning ability in the acquisition test and memory function in the retention test significantly. And TBM increased the level of ChAT which is synthesizing acetylcholine in CA3 area, and at the same time it increased the level of AChE which is resolving acetylcholine. These results show that T8M improved the cholinergic catabolism and anabolism, and the increment of metabolic activity of cholinergic system. In other words, it contributes to the recovery of damaged learning and memory function by ibotenic acid. So it can be concluded that TBM will be helpful to cholinergic brain damage induced by primary or senile reduction of acetylcholine secretive activity.

• The Journal of Korean Association of Computer Education
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• v.21 no.1
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• pp.23-30
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• 2018

In this paper, the elementary students from the Information class of the Science Education Institute for the Gifted were divided into the experimental and control groups. The two groups took Scratch programming classes and the changes in their sociality were analyzed. The experimental group used the Scratch remix function, an educational programming language, to perform the LT cooperative learning. The control group took general instructor-led classes. The experimental group carried out a team project in which learners should cooperate with each other to produce certain results using the remix function. Twelve 3-hour lessons were taken by the experimental and control groups consisting of 30 and 30 students respectively to verify changes depending on remix-based cooperative learning. According to t-test using the survey results, the experimental group that performed cooperative learning using the Scratch remix function showed more improved sociality than the control group did. There was a statistically significant difference as well.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.505-515
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• 2000

THe purpose of this study is to forecast of runoff hydrographs according to rainfall event in a stream. The neural network theory as a hydrologic blackbox model is used to solve hydrological problems. The Back-Propagation(BP) algorithm by the Levenberg-Marquardt(LM) techniques and Radial Basis Function(RBF) network in Neural Network(NN) models are used. Runoff hydrograph is forecasted in Bocheongstream basin which is a IHP the representative basin. The possibility of a simulation for runoff hydrographs about unlearned stations is considered. The results show that NN models are performed to effective learning for rainfall-runoff process of hydrologic system which involves a complexity and nonliner relationships. The RBF networks consist of 2 learning steps. The first step is an unsupervised learning in hidden layer and the next step is a supervised learning in output layer. Therefore, the RBF networks could provide rather time saved in the learning step than the BP algorithm. The peak discharge both BP algorithm and RBF network model in the estimation of an unlearned are a is trended to observed values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Convergence Security Journal
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• v.12 no.3
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• pp.19-26
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• 2012

In this study, software products developed in the course of testing, software managers in the process of testing software and tools for effective learning effects perspective has been studied using the NHPP software. The log type hazard function applied to distribution was based on finite failure NHPP. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. As a result, the learning factor is greater than autonomous errors-detected factor that is generally efficient model could be confirmed. This paper, a failure data analysis of applying using time between failures and parameter estimation using maximum likelihood estimation method, after the efficiency of the data through trend analysis model selection were efficient using the mean square error and $R^2$(coefficient of determination).