Walairacht, Aranya

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Walairacht, Aranya
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Walairacht, A.
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aranya.wa@kmitl.ac.th

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    Preprocessor to improve performance of GA in determining bending process for sheet metal industry
    (2002-01-01)
    Thanapandi, Chitra Malini
    ;
    Walairacht, Aranya
    ;
    Periasamy, Thanapandi
    ;
    Ohara, Shigeyuki
    In manufacturing fabricated sheet metal parts, the required shape has to be bent from the flat 2-D layouts. In this bending process, the most complex and critical work is determining the bend sequence and assigning appropriate tools for each bend. Determining the bend sequence is itself a combinatorial problem and this when coupled with tool assignment leads to a huge combination and clearly shows an exhaustive approach is impossible and we propose Genetic Algorithm (GA), an adaptive algorithm to solve the problem. Information regarding the operator knowledge and operator desire are input to the system to generate efficient bending process. And moreover, in order to improve the performance of GA, a preprocessor is being implemented which searches combinable bends and thereby reduce search space and solve the problem in time-economic way. © Springer-Verlag Berlin Heidelberg 2002.
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    Spam mail templates using genetic algorithm
    (2007-12-01)
    Walairacht, Aranya
    In this paper, we propose a mechanism for filtering e-mails by constructing spam mail prototypes by using genetic algorithm. Keywords extracted from e-mail's subject and body are categorized by their meaning into 8 categories. The binary representation of a chromosome string having 8 genes is constructed from keywords. Genetic operations are applied to create varieties of spam mail prototypes for filtering. From the experiments, the proposed technique shows the accuracy of filtering spam mails by 87.05% in average. When comparing with the other technique liked Bayesian, the proposed technique still has higher accuracy.
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    A learning mechanism for adaptive fitness function in auto 3D graphics layout using genetic algorithm
    (2001-01-01)
    Walairacht, A.
    ;
    Thanapandi, C.
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    Ohara, S.
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    Wongwirat, O.
    ;
    Burintramart, I.
    In computer-aided drafting and designing area, interactive graphics are used for designing components, systems, layouts, and structures. There are several approaches using for automated graphical layout tools in designing the graphic currently. Our research work aims to reduce design-working time, to learn user preferences, and to generate 3D graphical layout design automatically. An attempt to enable computers to generate 3D graphical layout design and presentation automatically by using Genetic Algorithm (GA) has been discussed for a decade. An effective use of GA in automated graphical layout design relies on how close to define a fitness function that can be reflected the user preferences. In this paper, we introduce the methods to define chromosome structures and fitness functions and of the selected objects. A learning mechanism is employed to adjust the fitness values of the objects in the selected layout choosing by the users. By this approach, the fitness functions can be changed adaptively reflecting the selection and user preferences.
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    Adaptive spam mail filtering using genetic algorithm
    (2006-11-17)
    Sanpakdee, Usarat
    ;
    Walairacht, Aranya
    ;
    Walairacht, Somsak
    In this paper, we propose a mechanism for filtering incoming spam mails by generating spam mail prototypes using genetic algorithm. Firstly, words from e-mails are extracted and are categorized by their relating meaning into 7 groups. Then, we compose a string of chromosome having 7 genes, i.e., groups of words. Each gene, represented words in each group, is encoded into binary value. The genetic algorithm and its operations are applied to create varieties of spam mail prototypes which inherit from old spam mails. It saves time for preparing training sets and need no large training set for learning like other methods. The spam mail prototypes are the result of this learning mechanism. The experimental results show that the proposed system has efficiency. When testing with both spams and hams, the accuracy is about 85% in average.
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    PCA in wavelet domain for face recognition
    (2006-11-17)
    Puyati, Wayo
    ;
    Walairacht, Somsak
    ;
    Walairacht, Aranya
    In this paper, the preprocessing process aimed to reduce size of input image by using wavelet transform before transformed image is sent to the process of PCA for recognition. We used ORL Face Databases from AT&T Laboratories Cambridge in the experiments. The results show that the 4<sup>th</sup> Order Symlets level 2 and level 3 improve the accuracy rate of recognition when compare among Haar wavelets, the 4<sup>th</sup> Order Daubechies wavelets, and Biorthogonal wavelets (orthogonal 6.8). In the case of overall processing time for training, the length of filter of wavelet is directly effect the time consuming. Since LL subband of wavelet decomposition becomes the input for PCA, the memory usage can be greatly reduced.
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    Efficiency improvement for unconstrained face recognition by weightening probability values of modular PCA and Wavelet PCA
    (2008-05-29)
    Puyati, Wayo
    ;
    Walairacht, Aranya
    Principal Component Analysis (PCA) is a well-known classical appearance-base method in face recognition. In the previous works, the preprocessing process significantly improved the recognition rate. Modular PCA and Wavelet PCA are the preprocessing processes of PCA, which increase the recognition rate of the original PCA. Modular PCA is suitable for the highvaried face database, while Wavelet PCA for the low-varied face database. In this paper, we propose the preprocessing method which combines between Modular PCA and Wavelet PCA with the weightening probability values. The experiments are compared among our propose method, Modular PCA, Wavelet PCA and original PCA with face database from Yale, ORL and UMIST. The experimental results show that the recognition rate of our method is higher compared to the other methods and also support variety of face database.