844 resultados para Failure time data analysis
Resumo:
An add-drop filter based on a perfect square resonator can realize a maximum of only 25% power dropping because the confined modes are standing-wave modes. By means of mode coupling between two modes with inverse symmetry properties, a traveling-wave-like filtering response is obtained in a two-dimensional single square cavity filter with cut or circular corners by finite-difference time-domain simulation. The optimized deformation parameters for an add-drop filter can be accurately predicted as the overlapping point of the two coupling modes in an isolated deformed square cavity. More than 80% power dropping can be obtained in a deformed square cavity filter with a side length of 3.01 mu m. The free spectral region is decided by the mode spacing between modes, with the sum of the mode indices differing by 1. (c) 2007 Optical Society of America.
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Based on the data processing technologies of interferential spectrometer, a sort of real-time data processing system on chip of interferential imaging spectrometer was studied based on large capacitance and high speed field programmable gate array( FPGA) device. The system integrates both interferograrn sampling and spectrum rebuilding on a single chip of FPGA and makes them being accomplished in real-time with advantages such as small cubage, fast speed and high reliability. It establishes a good technical foundation in the applications of imaging spectrometer on target detection and recognition in real-time.
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Various analytical physical models are presented to extract the photodissociation dynamics information from the data obtained in the femtosecond pump-probe experiment. The single- and double-component models are employed to explain the single- and double-channel dissociation of parent molecules. Another single-component model for fragment dissociation or deexcitation is also presented. All cases are explanatorily demonstrated on the pump-probe experimental data.
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Neal M J Timmis J and Hunt J. Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 922-927, Tokyo, 1999. IEEE.
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Timmis J and Neal M J. An artificial immune system for data analysis. In Proceedings of 3rd international workshop on information processing in cells and tissues (IPCAT), Indianapolis, U.S.A., 1999.
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In this article, we offer a new way of exploring relationships between three different dimensions of a business operation, namely the stage of business development, the methods of creativity and the major cultural values. Although separately, each of these has gained enormous attention from the management research community, evidenced by a large volume of research studies, there have been not many studies that attempt to describe the logic that connect these three important aspects of a business; let alone empirical evidences that support any significant relationships among these variables. The paper also provides a data set and an empirical investigation on that data set, using a categorical data analysis, to conclude that examinations of these possible relationships are meaningful and possible for seemingly unquantifiable information. The results also show that the most significant category among all creativity methods employed in Vietnamese enterprises is the “creative disciplines” rule in the “entrepreneurial phase,” while in general creative disciplines have played a critical role in explaining the structure of our data sample, for both stages of development in our consideration.
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Book review of: Chance Encounters: A First Course in Data Analysis and Inference by Christopher J. Wild and George A.F. Seber 2000, John Wiley & Sons Inc. Hard-bound, xviii + 612 pp ISBN 0-471-32936-3
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Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.