The Experts below are selected from a list of 1661703 Experts worldwide ranked by ideXlab platform
Lu Chen - One of the best experts on this subject based on the ideXlab platform.
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Flood season segmentation based on the probability change-Point Analysis technique
Hydrological Sciences Journal, 2010Co-Authors: Pan Liu, Shenglian Guo, Lihua Xiong, Lu ChenAbstract:Abstract The segmentation of flood seasons has both theoretical and practical importance in hydrological sciences and water resources management. The probability change-Point Analysis technique is applied to segmenting a defined flood season into a number of sub-seasons. Two alternative sampling methods, annual maximum and peaks-over-threshold, are used to construct the new flow series. The series is assumed to follow the binomial distribution and is analysed with the probability change-Point Analysis technique. A Monte Carlo experiment is designed to evaluate the performance of proposed flood season segmentation models. It is shown that the change-Point based models for flood season segmentation can rationally partition a flood season into appropriate sub-seasons. China's new Three Gorges Reservoir, located on the upper Yangtze River, was selected as a case study since a hydrological station with observed flow data from 1882 to 2003 is located 40 km downstream of the dam. The flood season of the reservoi...
Francesca Di Iorio - One of the best experts on this subject based on the ideXlab platform.
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Change Point Analysis of imprecise time series
Fuzzy Sets and Systems, 2013Co-Authors: Carmela Cappelli, Pierpaolo D'urso, Francesca Di IorioAbstract:Abstract In this paper we describe how to conduct a change-Point Analysis when dealing with time series imprecisely or vaguely observed, i.e. time ordered observations whose values are not known exactly, such as interval or ordinal time series (imprecise time series). In order to treat such time series, we propose to employ a fuzzy approach i.e. data are parameterized in the form of fuzzy variables. Then, to detect the number and location of change Points we employ a deviation measure for fuzzy variables in the framework of Atheoretical Regression Trees (ART). We present simulation results pertaining to the behavior of the proposed approach as well as two empirical applications to real imprecise time series.
Ho Leung Tsoi - One of the best experts on this subject based on the ideXlab platform.
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Modelling the probabilistic behaviour of function Point Analysis
Information & Software Technology, 1998Co-Authors: Ho Leung TsoiAbstract:Abstract Function Point Analysis is one of the most popular methods for estimating software size and, sometimes, development effort. However, accuracy for the result of estimation by using this method has not been ascertained significantly. Depending on human professional subjective judgement is one major reason that causes this problem. Subjective judgement may lead to inconsistency and inaccuracy in the estimation process. Fuzzified function Point Analysis model (FFPA) is proposed (C. Yau and R.H.L. Tsoi, Assessing the fuzziness of general system characteristics for estimating software size, ANZIIS Conference, Brisbane, Australia, Nov. 1994; C. Yau and R.H.L. Tsoi, A fuzzified approach to overcome the imprecisions of software sizing, IEEE SICICI '95, Singapore, July 1995.) to help software size estimators express their judgement in a more realistic manner. Surely, arangements given by FFPA will provide more information and can reflect the real life situation, but it does not show the confidence interval. Therefore, it is no way to assess the confidence level of the estimate. This paper will discuss the way to use fuzzy B-spline membership function (BMF) to derive the assessment values for 14 system characteristics of FFPA model. At the end of this paper, an empirical case study will show the merits of using confidence level in the estimate. These results imply that there may be potential for improving the way of using the estimation's result in different application environments.
Yu Fang - One of the best experts on this subject based on the ideXlab platform.
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Simplified Function Point Analysis Method Aiming at Small-to-medium-sized Software
Computer Engineering, 2008Co-Authors: Yu FangAbstract:Existing simplified Function Point Analysis(FPA) methods have different effect among different software individuals,especially when applied to the small-to-medium-sized software,which may result in larger result.This paper proposes a simplified function Point Analysis method based on NESMA indicative method,aiming at the small-to-medium-sized software.Experimental results show that the proposed approach is more accurate than other simplified methods when applied to this kind of software.
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Research on Function Point Analysis
Computer Science, 2007Co-Authors: Yu Fang, Li Juan, Wang YongAbstract:Software size measurement is the base of effort estimation, cost estimation and project plan. In this paper, the Function Point Analysis is presented in detail, including the concepts, methods and tools. This paper also presents a summary of the current art of the state of these methods, the problems of the Function Point Analysis methods, and a discussion on the future research topics.
Pan Liu - One of the best experts on this subject based on the ideXlab platform.
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Flood season segmentation based on the probability change-Point Analysis technique
Hydrological Sciences Journal, 2010Co-Authors: Pan Liu, Shenglian Guo, Lihua Xiong, Lu ChenAbstract:Abstract The segmentation of flood seasons has both theoretical and practical importance in hydrological sciences and water resources management. The probability change-Point Analysis technique is applied to segmenting a defined flood season into a number of sub-seasons. Two alternative sampling methods, annual maximum and peaks-over-threshold, are used to construct the new flow series. The series is assumed to follow the binomial distribution and is analysed with the probability change-Point Analysis technique. A Monte Carlo experiment is designed to evaluate the performance of proposed flood season segmentation models. It is shown that the change-Point based models for flood season segmentation can rationally partition a flood season into appropriate sub-seasons. China's new Three Gorges Reservoir, located on the upper Yangtze River, was selected as a case study since a hydrological station with observed flow data from 1882 to 2003 is located 40 km downstream of the dam. The flood season of the reservoi...
