9,885 research outputs found

    The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

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    Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

    Representation of maxitive measures: an overview

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    Idempotent integration is an analogue of Lebesgue integration where σ\sigma-maxitive measures replace σ\sigma-additive measures. In addition to reviewing and unifying several Radon--Nikodym like theorems proven in the literature for the idempotent integral, we also prove new results of the same kind.Comment: 40 page

    Design Equation: A Novel Approach to Heteropolymer Design

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    A novel approach to heteropolymer design is proposed. It is based on the criterion by Kurosky and Deutsch, with which the probability of a target conformation in a conformation space is maximized at low but finite temperature. The key feature of the proposed approach is the use of soft spins (fuzzy monomers) that leads to a design equation, which is an analog of the Boltzmann machine learning equation in the design problem. We implement an algorithm based on the design equation for the generalized HP model on the 3x3x3 cubic lattice and check its performance.Comment: 7 pages, 3 tables, 1 figures, uses jpsj.sty, jpsjbs1.sty, epsf.sty, Submitted to J. Phys. Soc. Jp

    One-class classifiers based on entropic spanning graphs

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    One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach takes into account the possibility to process also non-numeric data by means of an embedding procedure. The spanning graph is learned on the embedded input data and the outcoming partition of vertices defines the classifier. The final partition is derived by exploiting a criterion based on mutual information minimization. Here, we compute the mutual information by using a convenient formulation provided in terms of the α\alpha-Jensen difference. Once training is completed, in order to associate a confidence level with the classifier decision, a graph-based fuzzy model is constructed. The fuzzification process is based only on topological information of the vertices of the entropic spanning graph. As such, the proposed one-class classifier is suitable also for data characterized by complex geometric structures. We provide experiments on well-known benchmarks containing both feature vectors and labeled graphs. In addition, we apply the method to the protein solubility recognition problem by considering several representations for the input samples. Experimental results demonstrate the effectiveness and versatility of the proposed method with respect to other state-of-the-art approaches.Comment: Extended and revised version of the paper "One-Class Classification Through Mutual Information Minimization" presented at the 2016 IEEE IJCNN, Vancouver, Canad

    Implementation, integration, and optimization of a fuzzy foreground segmentation system

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    Foreground segmentation is often an important preliminary step for various video processing systems. By improving the accuracy of the foreground segmentation process, the overall performance of a video processing system has the potential for improvement. This work introduces a Fuzzy Foreground Segmentation System (FFSS) that uses Mamdani-type Fuzzy Inference Systems (FIS) to control pixel-level accumulated statistics. The error of the FFSS is quantified by comparing its output with hand-segmented ground-truth images from a set of image sequences that specifically model canonical problems of foreground segmentation. Optimization of the FFSS parameters is achieved using a Real-Coded Genetic Algorithm (RCGA). Additionally, multiple central composite designed experiments used to analyze the performance of RCGA under selected schemes and their respective parameters. The RCGA schemes and parameters are chosen as to reduce variation and execution time for a set of known multi-dimensional test functions. The selected multi-dimensional test functions represent assorted function landscapes. To demonstrate accuracy of the FFSS and implicate the importance of the foreground segmentation process, the system is applied to real-time human detection from a single-camera security system. The Human Detection System (HDS) is composed of an IP Camera networked to multiple heterogeneous computers for distributed parallel processing. The implementation of the HDS, adheres to a System of Systems (SoS) architecture which standardizes data/communication, reduces overall complexity, and maintains a high level of interoperability

    Approximation Theory and Related Applications

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    In recent years, we have seen a growing interest in various aspects of approximation theory. This happened due to the increasing complexity of mathematical models that require computer calculations and the development of the theoretical foundations of the approximation theory. Approximation theory has broad and important applications in many areas of mathematics, including functional analysis, differential equations, dynamical systems theory, mathematical physics, control theory, probability theory and mathematical statistics, and others. Approximation theory is also of great practical importance, as approximate methods and estimation of approximation errors are used in physics, economics, chemistry, signal theory, neural networks and many other areas. This book presents the works published in the Special Issue "Approximation Theory and Related Applications". The research of the world’s leading scientists presented in this book reflect new trends in approximation theory and related topics

    Benchmarking in cluster analysis: A white paper

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    To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance. This means that proposals of new methods of data pre-processing, new data-analytic techniques, and new methods of output post-processing, should be extensively and carefully compared with existing alternatives, and that existing methods should be subjected to neutral comparison studies. To date, benchmarking and recommendations for benchmarking have been frequently seen in the context of supervised learning. Unfortunately, there has been a dearth of guidelines for benchmarking in an unsupervised setting, with the area of clustering as an important subdomain. To address this problem, discussion is given to the theoretical conceptual underpinnings of benchmarking in the field of cluster analysis by means of simulated as well as empirical data. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made
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