18,478 research outputs found

    GRAMMATICAL EVOLUTION FOR FEATURE EXTRACTION IN LOCAL THRESHOLDING PROBLEM

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    The various lighting intensity in a document image causes diffculty to threshold the image. The conventional statistic approach is not robust to solve such a problem. There should be different threshold value for each part of the image. The threshold value of each image part can be looked as classifcation problem. In such a classifcation problem, it is needed to find the best features. This paper propose a new approach of how to use grammatical evolution to extract those features. In the proposed method, the goodness of each feature is calculated independently. The best features then used for classification task instead of original features. In our experiment, the usage of the new features produce a very good result, since there are only 5 miss-classification of 45 cases. Variasi intensitas pencahayaan pada citra dokumen akan menyebabkan kesulitan dalam menentukan nilai threshold dari citra tersebut. Pendekatan statistik konvensional tidak cukup baik dalam memecahkan masalah ini. Dalam hal ini, diperlukan nilai threshold yang berbeda-beda untuk setiap bagian citra. Nilai threshold dari setiap bagian citra dapat dipandang sebagai masalah klasifikasi. Dalam permasalahan klasifikasi semacam ini, dibutuhkan pencarian fitur-fitur terbaik. Di sini diusulkan sebuah pendekatan baru untuk mengekstrak fitur-fitur tersebut dengan menggunakan grammatical evolution. Nilai kebaikan dari masing-masing fitur akan dihitung secara saling lepas. Dalam percobaan yang dilakukan, tampak bahwa penggunaan fitur-fitur baru tersebut menghasilkan hasil yang sangat baik. Hanya ditemukan 5 kesalahan pengklasifikasian dalam 45 kasus

    Consistent Feature Construction with Constrained Genetic Programming for Experimental Physics

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    A good feature representation is a determinant factor to achieve high performance for many machine learning algorithms in terms of classification. This is especially true for techniques that do not build complex internal representations of data (e.g. decision trees, in contrast to deep neural networks). To transform the feature space, feature construction techniques build new high-level features from the original ones. Among these techniques, Genetic Programming is a good candidate to provide interpretable features required for data analysis in high energy physics. Classically, original features or higher-level features based on physics first principles are used as inputs for training. However, physicists would benefit from an automatic and interpretable feature construction for the classification of particle collision events. Our main contribution consists in combining different aspects of Genetic Programming and applying them to feature construction for experimental physics. In particular, to be applicable to physics, dimensional consistency is enforced using grammars. Results of experiments on three physics datasets show that the constructed features can bring a significant gain to the classification accuracy. To the best of our knowledge, it is the first time a method is proposed for interpretable feature construction with units of measurement, and that experts in high-energy physics validate the overall approach as well as the interpretability of the built features.Comment: Accepted in this version to CEC 201

    Parameter analysis of copper-nickel-tungsten prepared via powder metallurgy process for electrical discharge machining of polycrystalline diamond

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    Polycrystalline Diamond (PCD) tools have an outstanding wear resistance. The electric conductivity of PCD caused by the conductive binding material (Cobalt) makes it possible to machine PCD tools with EDM. Electrode used in EDM of PCD must have better porosity, electrical and thermal conductivity. Therefore, this research presents the works in production of Cu-Ni-W electrode by powder metallurgy route. Production of powder metallurgy parts involve mixing of the powder with additives or lubricants, compacting the mixture and heating the green compacts in an Argon gas furnace so the particle bond to each other. Two levels of full factorial with six centre points and two replication technique was used to study the influence of main and interaction effects of the powder metallurgy parameter. There were four factors involved in this experiment. Factor A which is Type of Cu-Ni; Type A and Type B was defined as categorical factor. Factor B in which Composition of W; 5 Wt.%, 15 Wt. % and 25 Wt.%, was defined as numerical factor. Factor C which is the Compaction load; 7, 8 and 9 tonne and Factor D which is Sintering temperature; 635 ℃, 685 ℃ and 735 ℃ were also defined as numerical factor. Optical Microscope, Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) was used to analysed the microstructure and surface morphology of Cu-Ni-W electrode. The best parameter combination to produced better porosity, electrical and thermal conductivity for both Type A and Type B was 5 Wt.% of W, compaction load at 9 tonne and sintering temperature at 735℃. The best response for Type A is 12.65% of porosity, 14.40 IACS% of electrical conductivity and 413.26 W/m.℃ of thermal conductivity. While that, the best response for Type B were 9.36% of porosity, 16.66 IACS% of electrical conductivity and 345.21W/m.℃ of thermal conductivity. From the calculation of Maxwell’s Equation, Type A and Type B had the highest electrical conductivity of 58.48 IACS% and 77.35 IACS% respectively at W content of 5Wt.%. Type A and Type B also had the highest thermal conductivity of 369.86 W/m.℃ and 310.24 W/m.℃ respectively at W content of 5 Wt.%. Besides that, thermal conductivity also increased with the temperature increased until 450℃

    A Survey on Compiler Autotuning using Machine Learning

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    Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018
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