1,219 research outputs found

    Deterministic Annealing: A Variant of Simulated Annealing and its Application to Fuzzy Clustering

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    Deterministic annealing (DA) is a deterministic variant of simulated annealing. In this chapter, after briefly introducing DA, we explain how DA is combined with the fuzzy c-means (FCM) clustering by employing the entropy maximization method, especially the Tsallis entropy maximization. The Tsallis entropy is a q parameter extension of the Shannon entropy. Then, we focus on Tsallis-entropy-maximized FCM (Tsallis-DAFCM), and examine effects of cooling functions for DA on accuracy and convergence. A shape of a membership function of Tsallis-DAFCM depends on both a system temperature and q. Accordingly, a relationship between the temperature and q is quantitatively investigated

    An Overview of the Use of Neural Networks for Data Mining Tasks

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    In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

    Computer Assisted Method Development in Liquid Chromatography

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    This paper describes potential applications of computer-assisted chemometrics in method devel-opment in liquid chromatography. These include modeling of retention (isocratic, gradient, molecular modeling, artificial neural networks), assessment of separation (peak capacity), single and multiple objective optimization approach, advanced optimization algorithms (genetic algorithms, simulated annealing) and method transfer issues (transfer of methods between instruments and / or laboratories). Selected topics provide an accessible source of information needed for successful increase of chromatographic efficiency and economic feasibility (higher sample throughput) in liquid chromatography

    Scheduling of a Cyber-Physical System Simulation

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    The work carried out in this Ph.D. thesis is part of a broader effort to automate industrial simulation systems. In the aeronautics industry, and more especially within Airbus, the historical application of simulation is pilot training. There are also more recent uses in the design of systems, as well as in the integration of these systems. These latter applications require a very high degree of representativeness, where historically the most important factor has been the pilot’s feeling. Systems are now divided into several subsystems that are designed, implemented and validated independently, in order to maintain their control despite the increase in their complexity, and the reduction in time-to-market. Airbus already has expertise in the simulation of these subsystems, as well as their integration into a simulation. This expertise is empirical; simulation specialists use the previous integrations schedulings and adapt it to a new integration. This is a process that can sometimes be time-consuming and can introduce errors. The current trends in the industry are towards flexible production methods, integration of logistics tools for tracking, use of simulation tools in production, as well as resources optimization. Products are increasingly iterations of older, improved products, and tests and simulations are increasingly integrated into their life cycles. Working empirically in an industry that requires flexibility is a constraint, and nowadays it is essential to facilitate the modification of simulations. The problem is, therefore, to set up methods and tools allowing a priori to generate representative simulation schedules. In order to solve this problem, we have developed a method to describe the elements of a simulation, as well as how this simulation can be executed, and functions to generate schedules. Subsequently, we implemented a tool to automate the scheduling search, based on heuristics. Finally, we tested and verified our method and tools in academic and industrial case studies

    Generalizing List Scheduling for Stochastic Soft Real-time Parallel Applications

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    Advanced architecture processors provide features such as caches and branch prediction that result in improved, but variable, execution time of software. Hard real-time systems require tasks to complete within timing constraints. Consequently, hard real-time systems are typically designed conservatively through the use of tasks? worst-case execution times (WCET) in order to compute deterministic schedules that guarantee task?s execution within giving time constraints. This use of pessimistic execution time assumptions provides real-time guarantees at the cost of decreased performance and resource utilization. In soft real-time systems, however, meeting deadlines is not an absolute requirement (i.e., missing a few deadlines does not severely degrade system performance or cause catastrophic failure). In such systems, a guaranteed minimum probability of completing by the deadline is sufficient. Therefore, there is considerable latitude in such systems for improving resource utilization and performance as compared with hard real-time systems, through the use of more realistic execution time assumptions. Given probability distribution functions (PDFs) representing tasks? execution time requirements, and tasks? communication and precedence requirements, represented as a directed acyclic graph (DAG), this dissertation proposes and investigates algorithms for constructing non-preemptive stochastic schedules. New PDF manipulation operators developed in this dissertation are used to compute tasks? start and completion time PDFs during schedule construction. PDFs of the schedules? completion times are also computed and used to systematically trade the probability of meeting end-to-end deadlines for schedule length and jitter in task completion times. Because of the NP-hard nature of the non-preemptive DAG scheduling problem, the new stochastic scheduling algorithms extend traditional heuristic list scheduling and genetic list scheduling algorithms for DAGs by using PDFs instead of fixed time values for task execution requirements. The stochastic scheduling algorithms also account for delays caused by communication contention, typically ignored in prior DAG scheduling research. Extensive experimental results are used to demonstrate the efficacy of the new algorithms in constructing stochastic schedules. Results also show that through the use of the techniques developed in this dissertation, the probability of meeting deadlines can be usefully traded for performance and jitter in soft real-time systems

    A Survey of League Championship Algorithm: Prospects and Challenges

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    The League Championship Algorithm (LCA) is sport-inspired optimization algorithm that was introduced by Ali Husseinzadeh Kashan in the year 2009. It has since drawn enormous interest among the researchers because of its potential efficiency in solving many optimization problems and real-world applications. The LCA has also shown great potentials in solving non-deterministic polynomial time (NP-complete) problems. This survey presents a brief synopsis of the LCA literatures in peer-reviewed journals, conferences and book chapters. These research articles are then categorized according to indexing in the major academic databases (Web of Science, Scopus, IEEE Xplore and the Google Scholar). The analysis was also done to explore the prospects and the challenges of the algorithm and its acceptability among researchers. This systematic categorization can be used as a basis for future studies.Comment: 10 pages, 2 figures, 2 tables, Indian Journal of Science and Technology, 201
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