• The Journal of the Acoustical Society of Korea
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• v.13 no.1E
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• pp.25-36
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• 1994

A problem of making a neural network learning self-adaptive to the training set supplied is addressed in this paper. This arises from the aspect in choice of an adequate stepsize for the update of the current weigh vectors according to the training pairs. Related issues in this attempt are raised and fundamentals in neural network learning are introduced. In comparison to the most popular back-propagation scheme, the usefulness and superiority of the proposed weight update algorithm are illustrated by examing the identification of unknown nonlinear systems only from measurements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2275-2284
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• 2015

In this paper, the Walsh coded orthogonal training signals for 4 × 4 multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems are designed and the channel estimation equations are derived as a closed form, taking account of the inter training signal interference problems caused by the multi-path delayed signals. The performances of the proposed channel estimation method are analyzed and compared with the conventional methods[9,14] by using computer simulation. The simulation results show that the proposed methods has better performances, compared with the conventional methods[9,14]. As a result, the proposed method can be used for MIMO-OFDM systems with null sub-carriers.

• Journal of KIISE:Software and Applications
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• v.32 no.9
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• pp.912-919
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• 2005

When we apply binary classification to multi-class classification for text categorization, we use the One-Against-All method generally, However, this One-Against-All method has a problem. That is, documents of a negative set are not labeled by human. Thus, they can include many noisy documents in the training data. In this paper, we propose that the Sliding Window technique and the EM algorithm are applied to binary text classification for solving this problem. We here improve binary text classification through extracting noise documents from the training data by the Sliding Window technique and re-assigning categories of these documents using the EM algorithm.

• Korean Journal of Childcare and Education
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• v.10 no.6
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• pp.5-23
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• 2014

The purpose of this study was to investigate the teachers' multi-media application ability, their perception on the use of multi-media for story-telling, and actual use in class. The survey, using a questionnaire, was conducted targeting 225 teachers in Seoul, Gyeonggi and Incheon. The results of the study are as follows: Firstly, teachers who were younger, had a higher level of education, were experienced in taking multi-media courses, and took charge of larger classes showed better ability of multi-media application. And teachers who were trained on multi media as well as kindergarten teachers indicated a more positive perception about applying multi-media for story-telling. In addition, teachers who had higher levels of education, were experienced in taking multi-media courses, and teaching larger classes at national/public institutions applied multi-media more frequently. Secondly, the teachers' ability of applying multi-media, their perception on the use of multi-media for story-telling, and their multi-media use in class indicated strong positive correlations. Thirdly, a teacher's perception on the significance of the multi-media application for story-telling, their skills for multi-media use, the size of classes, and the training experiences on multi-media affected their actual application of multi-media for story-telling.

• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.303-314
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• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• English Language & Literature Teaching
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• v.16 no.1
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• pp.123-142
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• 2009

The present study used multi-faceted Rasch measurement to explore the characteristics and bias patterns of non-native raters when they scored L2 writing tasks. Three raters scored 254 writing tasks written by Korean university students on two topics adapted from the TOEFL Test of Written English (TWE). The written products were assessed using a five-category rating scale (Content, Organization, Language in Use, Grammar, and Mechanics). The raters only showed a difference in severity with regard to rating categories but not in task types. Overall, the raters scored Grammar most harshly and Organization most leniently. The results also indicated several bias patterns of ratings with regard to the rating categories and task types. In rater-task bias interactions, each rater showed recurring bias patterns in their rating between two writing tasks. Analysis of rater-category bias interaction showed that the three raters revealed biased patterns across all the rating categories though they were relatively consistent in their rating. The study has implications for the importance of rater training and task selection in L2 writing assessment.

• Speech Sciences
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• v.11 no.1
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• pp.7-20
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• 2004

In this paper, we propose a two-step noise compensation algorithm in feature extraction for achieving robust speech recognition. The proposed method frees us from requiring a priori information on noisy environments and is simple to implement. First, in frequency domain, the Harmonics-based Spectral Subtraction (HSS) is applied so that it reduces the additive background noise and makes the shape of harmonics in speech spectrum more pronounced. We then apply a judiciously weighted variance Feature Vector Normalization (FVN) to compensate for both the channel distortion and additive noise. The weighted variance FVN compensates for the variance mismatch in both the speech and the non-speech regions respectively. Representative performance evaluation using Aurora 2 database shows that the proposed method yields 27.18% relative improvement in accuracy under a multi-noise training task and 57.94% relative improvement under a clean training task.

• Journal of the Korean Society for Nondestructive Testing
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• v.28 no.3
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• pp.254-262
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• 2008

The objective of the proposed study is to produce a tool-condition monitoring (TCM) strategy that will lead to a more efficient and economical drilling tool usage. Drill-wear monitoring is an important attribute in the automatic cutting processes as it can help preventing damages of the tools and workpieces and optimizing the tool usage. This study presents the architectures of a multi-layer feed-forward neural network with back-propagation training algorithm for the monitoring of drill wear. The input features to the neural networks were extracted from the AE signals using the wavelet transform analysis. Training and testing were performed under a moderate range of cutting conditions in the dry drilling of steel plates. The results indicated that the extracted input features from AE signals to the supervised neural networks were effective for drill wear monitoring and the output of the neural networks could be utilized for the tool life management planning.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.112-121
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• 2002

This paper describes a new bike system for the postural balance rehabilitation training. Virtual environment and three dimensional graphic model is designed with CAD tools such as 3D Studio Max and World Up. For the real time bike simulation, the optimized WorldToolKit graphic library is embedded with the dynamic geometry generation method, multi-thread method, and portal generation method. In this experiment, 20 normal adults were tested to investigate the influencing factors of balancing posture. We evaluated the system by measuring the parameters such as path deviation, driving velocity, COP(center for pressure), and average weight shift. Also, we investigated the usefulness of visual feedback information by weight shift. The results showed that continuous visual feedback by weight shift was more effective than no visual feedback in the postural balance control It is concluded this system might be applied to clinical use as a new postural balance training system.

• Journal of Industrial Technology
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• v.24 no.A
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• pp.99-107
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• 2004

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and the environments as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to parition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, an inverted pendulum of the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.