• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.133-136
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• 1997

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.47-50
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• 1992

This paper describes pattern classification algorithm of ECG using back-propagation neural network. We presents new feature extractor using second order approximating function as the input signals of neural network. We use 9 significant parameters which were extracted by feature extractor. 5 most characterized ECG signal pattern is classified accurately by neural network. We use AHA database to evaluate the performance ol the proposed pattern classification algorithm.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.833-834
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• 2008

In this paper, a pattern classification method using closest decision method based on the mean of norm in the closet prototype from an input pattern and its k nearest neighbor prototypes is presented to do accurate classification in arbitrary distributed patterns when the number of patterns is very low. Also this method can be used to classify input pattern precisely when the number patterns is very low because this method considers the weight by the difference of variance in prototypes around the discrimination boundary.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.296-300
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• 2002

We proposed a method of a pattern classification using unsupervised teaming rules, SOFM, and supervised teaming rules, Multilayer neural network. Establish result that classify and get input pattern using SOFM by initial weighting vector of Multilayer neural network and target value. Got superior Performance as result that do simulation about face image to confirm usefulness of way that propose.

• Journal of Biomedical Engineering Research
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• v.4 no.1
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• pp.37-44
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• 1983

This paper deals with the processing of statistical pattern classification of unifunctional EMG signal. The signal was detected from bicep and tricep of a simulated amputees instead of below the humerus amputees or paralyzed persons.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.111-120
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• 2005

Software development creates many product that class components, Class Diagram, form, object, and design pattern. So this Paper suggests Index/XML Sequence Relation information system for product abstraction and classification, the system of design product Sequence Relation abstraction which can store, reuse design patterns in the meta modeling database with pattern Relation information. This is Index/XML Sequence Relation system which can easily change various relation information of product for product abstraction and classification. This system designed to extract and classify design pattern efficiently and then functional indexing, sequence base indexing for standard pattern, code indexing to change pattern into code and grouping by Index-ID code, and its role information can apply by structural extraction and design pattern indexing process. and it has managed various products, class item, diagram, forms, components and design pattern.

• Wind and Structures
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• v.3 no.3
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• pp.159-175
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• 2000

Many practical time series, including pressure signals measured on roof-corners of low-rise buildings in quartering winds, consist of relatively quiescent periods interrupted by intermittent transients. The dyadic wavelet transform is used to detect these transients in pressure time series and a relatively simple pattern classification scheme is used to detect underlying structure in these transients. Statistical analysis of the resulting pattern classes yields a library of signal "building blocks", which are useful for detailed characterization of transients inherent to the signals being analyzed.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.555-559
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• 1998

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis have been carried out, some of which produced commercial systems. However, there still remains much room for improving the accuracy of chromosome classification. In this paper, We proposed an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of two-step multi-layer neural network(TMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted four morphological features parameters such as centromeric index (C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.). These Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results shown that the chromosome classification error was reduced much more than that of the other classification methods.