• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.121-125
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• 2007

This paper proposes automatic generation method of proxy client code to automation of web service selection process through a monitoring agent. The technique of this paper help service consumer to provide source code of proxy client as it bring an attribute value of specific element of WSDL document using template rule. Namely, a XSLT script file provide code frame of dynamic invocation interface model. The automatic code generation technique need to solving starvation status of selection architecture. It is required to creating request HTTP message for every service on the result of search. The created proxy client program code generate dummy message about services. The proposed client code generation method show us a possibility of application in the automatic generation programming domain.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.228-235
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• 2008

This paper proposes automatic generation method of proxy client code to automation of web service selection process through a monitoring agent. The technique of this paper help service consumer to provide source code of proxy client as it bring an attribute value of specific element of WSDL document using template rule. Namely, a XSLT script file provide code frame of dynamic invocation interface model. The automatic code generation technique need to solving starvation status of selection architecture. It is required to creating request HTTP message for every service on the result of search. The created proxy client program code generate dummy message about services. The proposed client code generation method show us a possibility of application in the automatic generation programming domain.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.829-835
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• 2012

When the input features are generated by factors in a classification problem, it is more meaningful to identify important factors, rather than individual features. The $F_{\infty}$-norm support vector machine(SVM) has been developed to perform automatic factor selection in classification. However, the $F_{\infty}$-norm SVM may suffer from estimation inefficiency and model selection inconsistency because it applies the same amount of shrinkage to each factor without assessing its relative importance. To overcome such a limitation, we propose the adaptive $F_{\infty}$-norm ($AF_{\infty}$-norm) SVM, which penalizes the empirical hinge loss by the sum of the adaptively weighted factor-wise $L_{\infty}$-norm penalty. The $AF_{\infty}$-norm SVM computes the weights by the 2-norm SVM estimator and can be formulated as a linear programming(LP) problem which is similar to the one of the $F_{\infty}$-norm SVM. The simulation studies show that the proposed $AF_{\infty}$-norm SVM improves upon the $F_{\infty}$-norm SVM in terms of classification accuracy and factor selection performance.

• Phonetics and Speech Sciences
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• v.8 no.4
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• pp.103-113
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• 2016

This paper aims to propose articulatory features as novel predictors for automatic pronunciation assessment of English produced by Korean learners. Based on the distinctive feature theory, where phonemes are represented as a set of articulatory/phonetic properties, we propose articulatory Goodness-Of-Pronunciation(aGOP) features in terms of the corresponding articulatory attributes, such as nasal, sonorant, anterior, etc. An English speech corpus spoken by Korean learners is used in the assessment modeling. In our system, learners' speech is forced aligned and recognized by using the acoustic and pronunciation models derived from the WSJ corpus (native North American speech) and the CMU pronouncing dictionary, respectively. In order to compute aGOP features, articulatory models are trained for the corresponding articulatory attributes. In addition to the proposed features, various features which are divided into four categories such as RATE, SEGMENT, SILENCE, and GOP are applied as a baseline. In order to enhance the assessment modeling performance and investigate the weights of the salient features, relevant features are extracted by using Best Subset Selection(BSS). The results show that the proposed model using aGOP features outperform the baseline. In addition, analysis of relevant features extracted by BSS reveals that the selected aGOP features represent the salient variations of Korean learners of English. The results are expected to be effective for automatic pronunciation error detection, as well.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.1
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• pp.29-37
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• 1985

During Severe emergencies which result in the case of outage of large generator units, an automatic underfrequency protection scheme can prevent the system frequency from decaying and improve the system stability. This paper presents methods and results of a study on the optimum load shedding scheme which covering as follows. 1) Detail representation of governor model 2) Determination of optimum load shedding amount 3) Selection of action time settings of UFR 4) Comparsson of load shedding programs By this study, the optimum system operating method was recommended for reliable operation of power system.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.39-54
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• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.10
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• pp.941-950
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• 2011

In this paper, we analysis the end-to-end performance of the incremental cooperative communication with relay selection. In the conventional cooperative scheme, the source(S) broadcasts the signal to the relay(R) and the destination(D) at 1st phase, and the R forwards the signal to the D at 2nd phase. Although this scheme can improve performance and provide diversity gain, it suffers from decreasing spectrum efficiency. In order to overcome this problem, the incremental cooperative model can be used. In this paper, we study two incremental cooperative method : the first uses ARQ with threshold SNR and the second uses HARQ with channel coding. we also evaluated performance of the incremental cooperative communication based on the R arrangement by using both methods.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.49-56
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• 2015

For designing working devices of construction equipment, it is necessary to consider not only sufficient working ability but also available working range. Therefore, it is important to select the appropriate pivot positions of links. This paper presents a study on selection of pivot points of links used in construction equipment. To analyze the effect of each pivot point, a design program for pivot selection is developed. A conventional pivot design method requires a complicated process because it needs to create a certain working position manually to evaluate its performance. However, the developed program includes an automatic link assembly algorithm; thus, the working device can easily be analyzed by using pivot information of links. The developed program also included a kinematic/static analysis module and characteristic analysis algorithms. Therefore, it is possible to easily analyze a working device model created through the automatic assembly algorithm, whereby users can easily analyze the effect of each link pivot point for the actual product design.