• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• International Journal of Contents
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• v.3 no.2
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• pp.30-34
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• 2007

This paper concerns with the application of e-based education methodology for people with disabilities targeting to empower them. Instead of the classic educational support such as extended time to take exams, a reader and/or scribe to assist with exams and note taking services, we suggest the use of new pedagogy-the science of educations integrating the state-of-the art. In this paper, we introduce the definition of disabilities for the people who does not fully understand what they means, first, and then possible implementing tools which can empower them with accomplishments. Most of the research in the field of pedagogy has tended to concentrate on the behavioral aspects of instructional sciences. Therefore we would like to point out that we concentrate on the aspects of instructional science particularly related with people with disabilities.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2016-2019
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• 2003

In this paper, we take some features for face recognition out of face image, using a simple type of templates. We use the extracted features to do Adaboost learning for face recognition. Using a carefully-chosen feature among these features, we can make a weak face classifier for face recognition. And doing Adaboost learning on and on with those chosen several weak classifiers, we can get a strong face classifier. By using Adaboost Loaming, we can choose particular features which is not easily subject to changes in illumination and facial expression about several images of one person, and construct face recognition system. Therefore, the face classifier bulit like the above way has robustness in both facial expression and illumination variation, and it finally gives capability of recognizing face fast due to the simple feature.

• Journal of Mechanical Science and Technology
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• v.20 no.11
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• pp.1873-1880
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• 2006

The results of this study suggest the use of a Back Propagation Network (BPN) algorithm for the detection of esophageal erosions or abnormalities - which are the important signs of esophagitis - in the analysis of the color and textural aspects of clinical images obtained by endoscopy. The authors have investigated the optimization of the learning condition by the number of neurons in the hidden layer within the structure of the neural network. By optimizing learning parameters, we learned and have validated esophageal erosion images and/or ulcers functioning as the critical diagnostic criteria for esophagitis and associated abnormalities. Validation was established by using twenty clinical images. The success rates for detection of esophagitis during calibration and during validation were 97.91% and 96.83%, respectively.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.69-77
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• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.2
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• pp.57-81
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• 1993

This paper is concerned with analyzing the bankruptcy prediction power of three methods : Multivariate Discriminant Analysis (MDA), Inductive Learning, Neural Network, MDA has been famous for its effectiveness for predicting bankrupcy in accounting fields. However, it requires rigorous statistical assumptions, so that violating one of the assumptions may result in biased outputs. In this respect, we alternatively propose the use of two AI models for bankrupcy prediction-inductive learning and neural network. To compare the performance of those two AI models with that of MDA, we have performed massive experiments with a number of Korean bankrupt-cases. Experimental results show that AI models proposed in this study can yield more robust and generalizing bankrupcy prediction than the conventional MDA can do.

• School Mathematics
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• v.2 no.2
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• pp.489-508
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• 2000

With the increased use of computational technology, many educators question about spending large amount of class time for dealing with computational algorithms in elementary school math classroom at the expense of more holistic aspects of mathematics such as number sense, spatial sense, problem solving and data management. This paper introduce the new method for learning addition and subtraction so called ‘Mrs. Weill’s Hill’, which is believed as a suitable remedial method for children with mathematical learning disabilities, with perceptual problems, or with limited working memory capacities. This method provides children with external memory strategies by allowing them to solve the addition and subtraction problems in a stage by stage fashion with as many steps as they require. It also gives the child greater flexibility in the solution process and thus helps reduce anxiety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2191-2201
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• 2012

Traffic classification seeks to assign packet flows to an appropriate quality of service (QoS) class based on flow statistics without the need to examine packet payloads. Classification proceeds in two steps. Classification rules are first built by analyzing traffic traces, and then the classification rules are evaluated using test data. In this paper, we use self-organizing map and K-means clustering as unsupervised machine learning methods to identify the inherent classes in traffic traces. Three clusters were discovered, corresponding to transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier was found to be highly accurate for the traffic data and significantly better compared to a minimum mean distance classifier.

• School Mathematics
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• v.1 no.2
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• pp.433-449
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• 1999

This paper introduces a branch of qualitative method, called an in-depth interview method. By collecting data from the stories of Korean middle school students and a 9th grade American girl who used Geometer's Sketchpad and various software respectively for their mathematical problem solving, the qualitative analysis leads us to understand how such software affect their lives with mathematics subject. The unique characteristics and strands of each student's utterances reflect how software plays a role of learning aid for their mathematics learning. The arm of this study is both to get a good picture of each student's self-perceived relationship to mathematics as well as to explore external and objective parameters and factors in each student's internal situations. The qualitative descriptions of the collected data help us guide the students to the points where they could develop their interests and satisfaction with subject matter better. In this way, teachers may have more realistic understandings of how students become interested and motivated by mathematics, so that they are better able to find out ways of grasping the totalities of how the use of technology is interwoven into the school curricular.