Software Reliability Modelling and Identification (Lecture Notes in Computer Science)

Software Reliability Modelling and Identification (Lecture Notes in Computer Science) Review

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Software Reliability Modelling and Identification (Lecture Notes in Computer Science) Feature

This volume contains five tutorial papers based on the lectures given at the intensive course on Software Reliability Modelling and Identification in Como (Italy) from September 2 to 4, 1987. The purpose of this volume is to present some important models used to forecast the reliability growth during the software testing process, and discuss the practical applicability of models in the management of software techniques for model identification from data (parameter estimation, complexity selection, validation, etc.). The basic reliability concepts are also introduced for those readers who are not familiar with the reliability ideas. Besides the basic models, a new family of models is introduced in the book. This family is flexible enough to describe a variety of different reliability trends. Particular attention is given to the problem of the provision of tools to assist the user in selecting an appropriate model in a particular situation.

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Modelling and Management of Engineering Processes

Modelling and Management of Engineering Processes Review

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Modelling and Management of Engineering Processes Feature

Modelling for Business Improvement contains the proceedings of the First International Conference on Process Modelling and Process Management (MMEP 2010) held in Cambridge, England, in March 2010. It contains contributions from an international group of leading researchers in the fields of process modelling and process management. This conference will showcase recent trends in the modelling and management of engineering processes, explore potential synergies between different modelling approaches, gather and discuss future challenges for the management of engineering processes and discuss future research areas and topics. Modelling for Business Improvement is divided into three main parts: 1. Theoretical foundation of modelling and management of engineering processes, and achievements in theory. 2. Experiences from management practice using various modelling methods and tools, and their future challenges. 3. New perspectives on modelling methods, techniques and tools.

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Modelling Covariances and Latent Variables Using EQS

Modelling Covariances and Latent Variables Using EQS Review

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Modelling Covariances and Latent Variables Using EQS Feature

This primer has been designed as a self-instructional text which serves to introduce the reader to both the principles of statistical modelling of covariance structures and to the use of the EQS software package. It is divided into three parts - the first covering the basic ideas and language of covariance structure modelling together with an introduction to the EQS package. The second section covers a wide variety of models suitable for cross-sectional and longitudinal data and the final section discusses a wide variety of practical problems. This book should be of interest to researchers in psychology, sociology and medicine who use the EQS software; applied and consultant statisticians.

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Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software]

Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software] Review

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Web-based tools for data analysis and quality assurance on a life-history trait database of plants of Northwest Europe [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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Most data mining techniques have rarely been used in ecology. To address the specific needs of scientists analysing data from a plant trait database developed during the LEDA project, a web-based data mining tool has been developed. This paper presents the DIONE data miner and the project it has been developed in. It addresses the nature of plant trait data from a data mining perspective and points out problems that arise when preparing data for this process. The availability of a large amount of high quality data is an essential prerequisite for successful data mining. To ensure this, a software aided reviewing process has been integrated into the LEDA Traitbase system. The process enables third-party contributors to easily commit their data to LEDA, while assuring adherence of the data to the LEDA standard. The paper concludes with information about data mining results on plant trait data achieved so far and gives an outlook on the applicability of data mining to the fields of ecology.

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A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software]

A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software] Review

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A comparison between the uncertainties in model parameters and in forcing functions: its application to a 3D water-quality model [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This paper illustrates the application of both local and global sensitivity analysis techniques to an estimation of the uncertainty in the output of a 3D reaction-diffusion ecological model; the model describes the seasonal dynamics of dissolved Nitrogen and Phosphorous, and those of the phytoplanktonic and zooplanktonic communities in the lagoon of Venice. Two sources of uncertainty were taken into account and compared: (1) uncertainty concerning the parameters of the governing equation; (2) uncertainty concerning the forcing functions. The mean annual concentrations of Dissolved Inorganic Nitrogen (DIN) was regarded as the model output, as it represents the largest fraction of the Total Dissolved Nitrogen, TDN, for which the current Italian legislation sets a quality target in the lagoon of Venice. A local sensitivity analysis was initially used, so as to rank the parameters and provide an initial estimation of the uncertainty, which is a result of an imperfect knowledge of the dynamic of the system. This uncertainty was compared with that induced by an imperfect knowledge of the loads of Nitrogen, which represent the main forcing functions. On the basis of the results of the local analysis, the most important parameters and loads were then taken as the sources of uncertainty, in an attempt to assess their relative contributions. The global uncertainty and sensitivity analyses were carried out by means of a sampling-based Monte Carlo method. The results of the subsequent input-output regression analysis suggest that the variance in the model output could be partitioned among the sources of uncertainty, in accordance with a linear model. Based on this model, 79% of the variance in the mean annual concentration of DIN was accounted for by the uncertainty in the parameters which specify the dynamics of the phytoplankton and zooplankton, and only 5% by the uncertainties in the three main Nitrogen sources.

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software]

A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software] Review

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A recursive estimation approach to the spatio-temporal analysis and modelling of air quality data [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

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This paper presents the methodology for the spatial and temporal interpolation of air quality data. As a practical example, the methodology is applied to the daily nitric oxide NO concentrations measured at 23 stations around Paris. Analysis of the temporal and spatial variability of observations of NO in the Paris area is divided into: (i) time series analysis of AirParif data; and (ii) development of combined spatial and temporal analysis techniques using NO observations from 19 stations. The first part of the paper shows how advanced methods of nonstationary time series analysis can be used to interpolate the data sets of NO concentrations over periods where measurements are missing and to decompose the time series into trend and harmonic components. The results of this analysis applied to 19 stations around Paris are then used in further spatio-temporal analysis of the data. This consists of two steps: (i) preliminary analysis of spatial relations within the data sets; and (ii) the development of a spatio-temporal model for log-transformed NO measurements. The results of the analysis indicate that the simple spatio-temporal model consisting of trend and noise efficiently represents the spatio-temporal variations in the data and it can be applied to predict air pollution variations in time and space at un-sampled locations.

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Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software]

Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software] Review

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Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
To evaluate the future state of river water in view of actual pollution loading or different management options, water quality models are a useful tool. However, the uncertainty on the model predictions is sometimes too high to draw proper conclusions. Because of the complexity of process based river water quality models, it is best to investigate this problem according to the origin of the uncertainty. If the uncertainty stems from input data or parameter uncertainty, more reliable results are obtained by performing specific measurement campaigns. The aim of the research reported in this paper is to guide these measurement campaigns based on an uncertainty analysis. The practical case study is the river Dender in Flanders, Belgium. First an overview of different techniques that give valuable information for the reduction of input and parameter uncertainty is given. A global sensitivity analysis shows the importance of the different uncertainty sources. Further an analysis of the uncertainty bands is performed to find differences in uncertainty between certain periods or locations. This shows that the link between periods with high uncertainty and specific circumstances (climatological, eco-regional, etc.) can help in gathering data for the calibration of submodels (e.g. diffuse pollution vs. point pollution).

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Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software]

Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software] Review

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Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995-2002) monitoring of 12 parameters at 13 different sites (14@?976 observations). Hierarchical cluster analysis grouped 13 sampling sites into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, based on the similarity of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

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System analysis of water quality management for the Elbe river basin [An article from: Environmental Modelling and Software]

System analysis of water quality management for the Elbe river basin [An article from: Environmental Modelling and Software] Review

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System analysis of water quality management for the Elbe river basin [An article from: Environmental Modelling and Software] Feature

This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.

Description:
A decision support system for integrated river basin management of the German part of the Elbe river basin (Elbe-DSS) is currently under development. It considers water quantity, chemical quality, and ecological status of surface waters. User needs were identified and refined by repeated consultation of water managers. A list of management objectives, measures, and external scenarios emerged, which was taken as the basis for the DSS development. A comprehensive system analysis was carried out to meet the various spatial and temporal scales when dealing with hydrologic, ecologic, economic, and social aspects related to water quantity and quality. System diagrams for the catchments and the river network were constructed. They describe the properties, processes, and data influencing the water flow and substance load. One model for the calculation of the long-term nutrient discharges in 132 sub-catchments from non-point sources, one simulation model for wastewater pathways (point sources) and aquatic fate assessment, and one model for hydrological dynamics were selected for integration into the Elbe-DSS. The interaction of management objectives, external scenarios of climate, agro-economic and demographic change, and selected measures to achieve the desired state of good water quantity and quality is investigated.

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