• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.3
• /
• pp.109-119
• /
• 1996

We propsoed a multiplexed optical correlation filter method for an optical parallel addition based on symbolic substitution. In the proposed mthod, we used redundant binary number which was easy to minimize the number of the symbolic substitution rules. We chose MACE filter which had very low sidelobes and good correlation peak compared with SDF filter as the optical correlation recognition filter and encoded input numbers properly to increase the discrimination capability. In order to minimize the number of symbolic substitution rules, sixteen input patterns were divided into six groups of the same addition results and six filters for recognizing the input patterns were used. these filters were multiplexed in two MMACE filter planes and the corresponding substitution method was proposed. Through the computer simulation, we confirmed the proposed method was suitable to implement the optical parallel adder.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.1
• /
• pp.269-280
• /
• 1996

We proposed a symbolic substitution method based on an optical correlator for an optical parallel addition. In the proposed symbolic substitution method, we used redundant binary number of the symbolic substitution rules as a number system and chose MAC3E filter which had very low sidelobes and good correlation peak compared with SDF filter as the optical correlation filter. We encoeded input numbers property to increase the discrimination capability and divided inpt patterns into 5 groups of the same addition results to minimize the number of symbolic substitution rules. Through the computer simulation, we confirmed the proposed method was suitable to implement the optical parallel adder.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1147-1161
• /
• 2015

Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.448-451
• /
• 2005

For measuring a traffic symbolic confusion quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information quantity. But for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.3
• /
• pp.193-201
• /
• 2002

For measuring a traffic symbolic confusion Quantity and symbolic air pleasantness, we use fuzzy sensor algorithm maded by symbolic information Quantity. Hut for implementation of fuzzy sensor, we use some symbolic information item, this method cannot produce precise output because we use vague fuzzy rule method and we cannot abundance fuzzy for precision of fuzzy rule method. For this reason, this paper introduce new fuzzy sensor algorithm composed of not fuzzy rule method but using Analytic Hierachy Process. To prove that new method is good, two type of fuzzy sensor applied to traffic signal controller and through much passing vehicle, two fuzzy sensor compared each other.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.4 no.6
• /
• pp.247-259
• /
• 1996

Kinematic design sensitivity analysis for vehicle in suspension systems design is performed. Suspension systems are modeled using composite joins to reduce the number of the constraint equations. This allows a semi-analytical approach that is computerized symbolic manipulation before numerical computations and that may compensate for their drawbacks. All the constraint equations including design variables are derived in symbolic equations for sensitivity analysis. By directly differentiating the equations with respect to design variables, sensitivity equations are obtained. Since the proposed method only requires the hard point data, sensitivity analysis is possible in suspension design stage.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.481-488
• /
• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.729-734
• /
• 2012

Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.

• International Journal of Advanced Culture Technology
• /
• v.11 no.2
• /
• pp.238-248
• /
• 2023

This study aims to examine the symbolism of the emblems of World Expositions by using Victor Turner's symbolic theory as a research method, and to reveal the symbolic types behind them by classifying the emblem designs of different periods and themes. The research object is 12 comprehensive World's Fair emblems from the 1939 New York World's Fair in the United States to the 2025 Osaka World's Fair in Japan, as identified by Bureau International des Expositions. The research method mainly adopts documentary research to collect historical information and theoretical frameworks related to the design of World's Fair emblems. In the analysis process, Victor Turner's symbolic sign theory is used as the main analytical framework to link the design elements of emblems to their relevance to specific societies and cultures in order to reveal the themes, values and ideas represented by the emblem symbolism. The results of the study show that the design of the Expo emblem uses different symbols, including material symbols, behavioral symbols, sensory symbols, natural symbols, social symbols and virtual symbols, to convey the core concepts, themes and values of the Expo. Through different types of symbols, the Expo emblem shows a wide range of concerns about technology and the future, mankind and the world, nature and ecology, and society and innovation. The symbolic design of the emblem plays an important role in conveying the core concept and theme of the Expo.

• Communications for Statistical Applications and Methods
• /
• v.21 no.3
• /
• pp.225-234
• /
• 2014

We propose a novel hierarchical clustering for distribution valued dissimilarities. Analysis of large and complex data has attracted significant interest. Symbolic Data Analysis (SDA) was proposed by Diday in 1980's, which provides a new framework for statistical analysis. In SDA, we analyze an object with internal variation, including an interval, a histogram and a distribution, called a symbolic object. In the study, we focus on a cluster analysis for distribution valued dissimilarities, one of the symbolic objects. A hierarchical clustering has two steps in general: find out step and update step. In the find out step, we find the nearest pair of clusters. We extend it for distribution valued dissimilarities, introducing a measure on their order relations. In the update step, dissimilarities between clusters are redefined by mixture of distributions with a mixing ratio. We show an actual example of the proposed method and a simulation study.