• Journal of the Korean Society for Precision Engineering
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• v.28 no.1
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• pp.56-62
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• 2011

Terrain recognition ability is crucial to the performance of legged robots in an outdoor environment. For instance, a robot will not easily walk and it will tumble or deviate from its path if there is no information on whether the walking surface is flat, rugged, tough, and slippery. In this study, the ground surface recognition ability of robots is discussed, and to enable walking robots to recognize the surface state and changes, a central moment method was used. The values of the sensor signals (load cell) of robots while walking were detected in the supported section and were analyzed according to signal variance, skewness, and kurtosis. Based on the results of such analysis, the surface state was detected and classified.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.35-43
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• 2001

This thesis proposes a diagnostic formula for student's responses based on linguistic variable concept of fuzzy that makes domain expert to input the kernel elementeasily that constructs domain independent student module. And the domain expert can construct the rule with linguistic variable that is used to inference student's recognition state. This study designs a student module that can inference student's recognition state using this rule represented by linguistic variable.

• Education of Primary School Mathematics
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• v.4 no.2
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• pp.151-163
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• 2000

This research is the object to investigate these thing; how do teachers undertaking at the spot classrooms to recognize performance assessment and how do they decide to question and how go the present practiced state and what is the problem points in the present performance assessment. Additonal things of problem point like a research object are following; Lets look over recognition, actual situations and various problems for mathematics performance assessment of elementary school teachers. Concerning question papers, the problem largely lie in 4 regions, that is to say the recognition of performance assessment, the current state of affairs in practice, deciding questions and problems of putting theory into practices, of the 480 teachers-the object of our studies-about 380 returned our questionaire. However, as there were too many in the age range 30 to 40 are excluded 80, choosing 300 to us as data in our analysis.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.18 no.8
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• pp.78-89
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• 2000

Today's computers are built up from a minimal set of standard pattern recognition operations. Logic gates, such as NAND, are common examples. Biomolecular materials offer an alternative approach, both in terms of variety and context sensitivity. Enzymes, the basic switching elements in biological cells, are notable for their ability to discriminate specific molecules in a complex background and to do so in a manner that is sensitive to particular milieu features and indifferent to others, The enzyme, in effect, is a powerful context sensitivity pattern processor that in a rough way can be analogized to a neuron whose input-output behavior is controlled by enzymatic dynamics.

• Asia Marketing Journal
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• v.22 no.4
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• pp.79-101
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• 2021

With the steady rise in global popularity of the Korean music group BTS, the South Korean government and surrounding industries have swiftly begun utilizing their image and international recognition for specific nation branding purposes. While K-pop soft power strategies are not novel to the South Korean state, what is new is the rapid speed at which BTS have become a beacon for South Korean culture, language, and symbolism in the international arena. However, few scholarly works have sought to investigate the role fans have played in this heightened position for the group as state representatives, with minimal research conducted into the work fans do within the framework of ARMY fan culture. This paper will thus aim to fill the gap in scholarship on ARMY as an organized labour network, focusing on the role fans play as labourers in online spaces that work to promote, disseminate, and cultivate wider recognition for BTS as artists. Through the conjunct engagement of a political economy framework and theories of participatory culture, this paper will explore the manner through which the free labour of ARMY, premised on affect, has constructed the fandom as active agents of soft power alongside BTS themselves.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.1
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• pp.114-119
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• 1986

In this paper, an algorithm for the on-line recognition of handwritten Hangeul is proposed. The strokes are recognized by the minimum distance parser. The phonemes are separated by the finite-state automata resulted from the state graph of phonemes which are produced by the order of strokes. By simulation result for 3,000 characteristics in practical sentences, the recognition rate of strokes is obtained to be 98.5% and the separation rate of phonemes is obtained to be 92.5%.

• The Journal of the Acoustical Society of Korea
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• v.13 no.1
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• pp.5-13
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• 1994

This paper presents a study on Neural Networks for Phoneme Recognition and performs the Phoneme Recognition using TDNN (Time Delay Neural Network). Also, this paper proposes training algorithm for speech recognition using neural nets that is a proper to large scale TDNN. Because Phoneme Recognition is indispensable for continuous speech recognition, this paper uses TDNN to get accurate recognition result of phonemes. And this paper proposes new training algorithm that can converge TDNN to an optimal state regardless of the number of phonemes to be recognized. The recognition experiment was performed with new training algorithm for TDNN that combines backpropagation and Cauchy algorithm using stochastic approach. The results of the recognition experiment for three phoneme classes for two speakers show the recognition rates of $98.1\%$. And this paper yielded that the proposed algorithm is an efficient method for higher performance recognition and more reduced convergence time than TDNN.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.64-68
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• 2020

In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• MALSORI
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• no.59
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.