• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.585-590
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• 2000

Recently, It is gradually raised necessity that thickness of thin film is measured accuracy and managed in industrial circles and medical world. Ultrasonic Signal processing method is likely to become a very powerful method for NDE method of detection of microdefects and thickness measurement of thin film below the limit of Ultrasonic distance resolution in the opaque materials, provides useful information that cannot be obtained by a conventional measuring system. In the present research, considering a thin film below the limit of ultrasonic distance resolution sandwiched between three substances as acoustical analysis model, demonstrated the usefulness of ultrasonic Signal processing technique using information of ultrasonic frequency for NDE of measurements of thin film thickness, sound velocity, and step height, regardless of interference phenomenon. Numeral information was deduced and quantified effective information from the image. Also, pattern recognition of a defected input image was performed by neural network algorithm. Input pattern of various numeral was composed combinationally, and then, it was studied by neural network. Furthermore, possibility of pattern recognition was confirmed on artifical defected input data formed by simulation. Finally, application on unknown input pattern was also examined.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.238-243
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• 2007

The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.5
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• pp.187-196
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• 2003

Effects of Airgap and anisotropy substrate on input impedance and radiation pattern of rectangular microstrip patch antenna are studied in terms of an integral equation formulation. The input impedance and radiation pattern of microstrip patch antenna is investigated by using Galerkin's moment method in solving the integral equation. Sinusoidal functions are selected as basis functions, which resemble in the actual standing wave on the Patch. From the numerical results, the variation of input impedance and radiation patterns in the variation of air gap thickness, anisotropy ratio of substrate, and relative permittivity of anisotropy substrate are presented.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.1956-1964
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• 1989

In this paper, a new method of Hangul recognition is proposed to solve the problems of misrecognition owing to the contacts of FCEs (Fundamental Character Elements) in a Hangul pattern. Structures of FCFs are represented with PAG(Programmed Array Grammar) to recognize an input pattern on 2-D. array of pels., and the unnecessary deformation of the conventional approach can be eliminated by using PEACE parsing which extracts primitives and computes attributes in the course of analyzing the structure of an input pattern. Also, primitive transformation at contacts can afford to confirm all the possible structures of an input pattern and solve the problem of misrecognition owing to the contacts of FCEs. The recognition rate of proposed method for printed Hangul characters shows 96.2% for 1978 Gothic-letters and 92.0% for 1920 Myng-style-letters, respectively.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.473-476
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• 2003

Nowaday, Internet is doing the role of a large distributed information service tenter and various information and database servers managing it are in distributed network environment. However, the we have several difficulties in deciding the server to disposal input data depending on data properties. In this paper, we designed server mining mechanism and Intellectual data mining system architecture for the best efficiently dealing with input data pattern by using neural network among the various data in distributed environment. As a result, the new input data pattern could be operated after deciding the destination server according to dynamic binding method implemented by neural network. This mechanism can be applied Datawarehous, telecommunication and load pattern analysis, population census analysis and medical data analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.86-93
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• 2006

The TAM(Topographic Attentive Mapping) network based on a biologically-motivated neural network model is an especially effective one for pattern analysis. It is composed of of input layer, category layer, and output layer. Fuzzy rule, for input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of core competency model for subcontractors of construction companies and show its usefulness.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.81-86
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• 2012

In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.