• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.672-681
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• 2017

This paper is concerned with the design methodology of face recognition system based on pose estimation. In 2-dimensional face recognition, the variations of facial pose cause the deterioration of recognition performance because object recognition is carried out by using brightness of each pixel on image. To alleviate such problem, the proposed face recognition system deals with Learning Vector Quantizatioin(LVQ) or K-Nearest Neighbor(K-NN) to estimate facial pose on image and then the images obtained from LVQ or K-NN are used as the inputs of networks such as Convolution Neural Networks(CNNs) and Radial Basis Function Neural Networks(RBFNNs). The effectiveness and efficiency of the post estimation using LVQ and K-NN as well as face recognition rate using CNNs and RBFNNs are discussed through experiments carried out by using ICPR and CMU PIE databases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.375-386
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• 2008

Recently, development paradigm of information system is turning into object oriented and component based, and this methodology is leading the software industry. To acclimatize aptly to this trend, users demand the assessment of software expenses to change with the appropriate model of computing costs of the environment, and some people are actually studying the concept of Object Oriented Function Point and UCP method. Especially, Object Oriented Function Point Measurement Process has good points in overcoming the bound of LOC and the existing the Function Point Measurement Process because Object Oriented Function Point Measurement Process is applicable to the early stage of development project mainly with the used cases, and valid to the life long period as the each stage of software products develops, and always understandable to communicate with users by the UML mark rules. Accordingly, this research is to measure Functional Point at ROFP and AOFP in accordance with the development project of information system by the national defense CBD methodology procedures and UML Interrelation Analysis that are recently and widely used in the developmental environment of object oriented information system. Furthermore, this study suggests the measurement method to obtain Functional Point, and identifies service function and object/class function in the correlation analysis of use case and class based on the products and UML modeling via traditional FPA model and object oriented FPA model. Above all, this study is to demonstrate the improvement of traditional Function Point Measurement Process, IFPUG-CPM and software cost basis, and reveal Function Point Measurement Process, which is appropriate to the development of object oriented information system, and suggest the evaluation results of the compatibility through case studies.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.47-62
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• 2018

Purpose - Based on preceding studies, this thesis focuses on the finding of the definition and category of mobile tourism application and deriving out its characteristics. And after looking for how they make influences on continuous intention to use, we make empirical study with TAM model. Research design, data, and methodology - There are many Chinese tourist who visit Korea with user's constant intention to use of tourism application. This study is to find out the definition and category of mobile tourism application through research of preceding study and to fomulate the research model and hypothesis that how tourism application attributes (convenience, interaction, accessibility, local basis, security) affect constant intention to use of mobile tourism application. In order to verify a hypothesis, we conducted a survey for Chinese users of tourism application. In empirical study, we analyzed a structure model for frequency analysis, reliability analysis, exploratory factor analysis, validity analysis through IBM SPSS Statistics 21.0 and IBM SPSS AMOS 21.0 Results - Among tourism applications, convenience, interaction, accessibility and local basis have positive effects on both perceived usefulness and perceived easiness respectively. But security does not. Also perceived easiness has a positive effect on perceived usefulness. Finally, perceived usefulness and perceived easiness have positive effect on constant intent to use. Conclusions - Tourism application enterprises should put emphasis on design such as menu or function in order to simplify the operation of new services for new customers. Therefore, comfortable user interface and development of useful function can improve tourism application. Consequently, it leads to the promotion of tourism application. Also, when users perceive tourism application as a useful media which is easy, comfortable and useful content, the degree of constant intention to use becomes increased. It is important to provide plentiful and useful contents for customers and to develop user interface such as easy operation because these factors have positive effects on constant demand and use of tourism application.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• Journal of KIISE:Software and Applications
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• v.33 no.8
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• pp.655-667
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• 2006

This paper proposes a method for counting function point using goal and scenario based requirements. Function Point is a software sizing method and widely used as a basis to estimate software development cost. Requirements elicitation and analysis should be performed before function point analysis but function point analysis method doesn't deal with requirements elicitation and analysis. For that reason, Function point extraction method from existing requirements method is needed and if the requirements method has advantage for traceability and elicitation, it is suitable for managing cost. Goal and scenario method is widely used as requirements elicitation and analysis. It has also good traceability. Therefore, this paper discusses a method for extracting function point from requirements text gathered using the goal and scenario based requirements elicitation technique. The proposed method aims to establish and maintain traceability between function point and requirements text. Text based function point extraction guidance rules have been developed. The proposed methodology has been applied to Order Processing System development.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.153-156
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• 2000

This paper presents a SOC design methodology using the newly-emerging SystemC. The suggested methodology firstly uses SystemC to define blocks from the previously-developed system level algorithm with internal behavior and interface being separated and validate such a described blocks' functionality when integrated. Next, the partitioning between software and hardware is considered. With software, the interface to hardware is described cycle-accurate and the other internal behavior in conventional ways. With hardware, I/O transactions are refined gradually in several abstraction levels and internal behavior described on a function basis. Once hardware and software have been completed functionally, system performance analysis is performed on the built model with assumed performance factors and influences such decisions regressively as on optimum algorithm selection, partitioning and etc. The analysis then gives constraint information when hardware description undergoes scheduling and fixed-point trans- formation with the help of automatic translation tools or manually. The methodology enables C/C++ program developers and VHDL/Verilog users to migrate quickly to a co-design & co-verification environment and is suitable for SoC development at a low cost.

• Microbiology and Biotechnology Letters
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• v.51 no.1
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• pp.69-82
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• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.4
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• pp.373-381
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• 2017

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.