Relevant bibliographies by topics / Multi-criteria estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Multi-criteria estimation'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Multi-criteria estimation"

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Livneh, B., and D. P. Lettenmaier. "Multi-criteria parameter estimation for the Unified Land Model." Hydrology and Earth System Sciences 16, no. 8 (August 29, 2012): 3029–48. http://dx.doi.org/10.5194/hess-16-3029-2012.

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Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operational Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting Model, is the basis for these experiments. Calibrations were made using each of the data sets individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large scales, calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.
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Livneh, B., and D. P. Lettenmaier. "Multi-criteria parameter estimation for the unified land model." Hydrology and Earth System Sciences Discussions 9, no. 4 (April 3, 2012): 4417–63. http://dx.doi.org/10.5194/hessd-9-4417-2012.

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Abstract. We describe a parameter estimation framework for the Unified Land Model (ULM) that utilizes multiple independent data sets over the Continental United States. These include a satellite-based evapotranspiration (ET) product based on MODerate resolution Imaging Spectroradiometer (MODIS) and Geostationary Operation Environmental Satellites (GOES) imagery, an atmospheric-water balance based ET estimate that utilizes North American Regional Reanalysis (NARR) atmospheric fields, terrestrial water storage content (TWSC) data from the Gravity Recovery and Climate Experiment (GRACE), and streamflow (Q) primarily from the United States Geological Survey (USGS) stream gauges. The study domain includes 10 large-scale (≥105 km2) river basins and 250 smaller-scale (<104 km2) tributary basins. ULM, which is essentially a merger of the Noah Land Surface Model and Sacramento Soil Moisture Accounting model, is the basis for these experiments. Calibrations were made using each of the criteria individually, in addition to combinations of multiple criteria, with multi-criteria skill scores computed for all cases. At large-scales calibration to Q resulted in the best overall performance, whereas certain combinations of ET and TWSC calibrations lead to large errors in other criteria. At small scales, about one-third of the basins had their highest Q performance from multi-criteria calibrations (to Q and ET) suggesting that traditional calibration to Q may benefit by supplementing observed Q with remote sensing estimates of ET. Model streamflow errors using optimized parameters were mostly due to over (under) estimation of low (high) flows. Overall, uncertainties in remote-sensing data proved to be a limiting factor in the utility of multi-criteria parameter estimation.
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Brigadnov, Igor A. "Multi-criteria Estimation of Load-Bearing Capacity of Solids." Journal of Elasticity 140, no. 1 (January 10, 2020): 121–33. http://dx.doi.org/10.1007/s10659-019-09762-8.

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Sveshnikov, Sergey, Victor Bocharnikov, Anatoly Pavlikovsky, and Andrey Prima. "Estimating the potential willingness of the state to use military force based on the Sugeno fuzzy integral." Yugoslav Journal of Operations Research, no. 00 (2022): 2. http://dx.doi.org/10.2298/yjor210515002s.

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Estimation of the potential willingness of the state to use military force is an integral part of the analysis of international relations and the preparation of key decisions in security sphere. Our problem was to develop a method for numerically estimating the potential willingness of any state to use military force. This method should take into account a large number of quantitative and qualitative criteria, the uncertainty of their relationships, as well as the uncertainty of the initial data, some of which can only be obtained with the help of experts. Our analysis has shown that the known methods have a number of serious shortcomings. We proposed to solve this problem based on the representation of partial estimations of states in the form of fuzzy sets, and the importance of criteria in the form of a fuzzy measure. We also proposed to aggregate the partial estimations using the Sugeno fuzzy integral. We developed a hierarchical structure of estimation criteria, determined the importance of the criteria, built an observation channel based on the Harrington curve to obtain input estimations, and also developed an aggregation algorithm. As a result, we calculated estimations for 137 states and examined their potential willingness to use military force. The results disclose new aspects of using fuzzy-integral calculus to construct hierarchical models of multi-criteria estimating, and also demonstrate the possibility of using artificial intelligence methods to obtain numerical estimations in the sphere of international relations.
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KaurSehra, Sumeet, Yadwinder Singh Brar, and Navdeep Kaur. "Multi Criteria Decision Making Approach for Selecting Effort Estimation Model." International Journal of Computer Applications 39, no. 1 (February 29, 2012): 10–17. http://dx.doi.org/10.5120/4783-6989.

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Benkesmia, Yamina, Nadjla Bentekhici, and Lahcen Wahib Kebi. "ESTIMATION AND MAPPING OF INFILTRATION BASED ON A MULTI-CRITERIA ANALYSIS." Acta Geobalcanica 3, no. 1 (July 15, 2017): 27–33. http://dx.doi.org/10.18509/agb.2017.03.

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Coit, D. W., T. Jin, and N. Wattanapongsakorn. "System Optimization With Component Reliability Estimation Uncertainty: A Multi-Criteria Approach." IEEE Transactions on Reliability 53, no. 3 (September 2004): 369–80. http://dx.doi.org/10.1109/tr.2004.833312.

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Ganicheva, A. V., and A. V. Ganichev. "VECTOR MODEL FOR MULTI-CRITERIA SCORING." Scientific Review: Theory and Practice 10, no. 10 (October 30, 2020): 2245–53. http://dx.doi.org/10.35679/2226-0226-2020-10-10-2245-2253.

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To assess the performance of economic systems, criteria and performance indicators are used that show the degree of achievement of the desired performance result. To characterize complex systems in most real cases, one criterion is not enough. Therefore, several criteria are used to comprehensively characterize the system. The multi-criteria problem raises the problem of matching individual criteria when used together. There are many ways to select alternatives in the face of multiple decision criteria. This article discusses the multi-criteria comparison of objects with Pareto dominance, vector estimates with arbitrary non-negative coordinates of vectors. The advantages of vector estimates in comparison with average estimates are considered. In this work, three important theorems of multi-criteria estimation are proved. The theorems define the conditions for the equivalence of three options for comparing objects by a variety of attributes: 1) arithmetic mean values; 2) the lengths of the corresponding vectors; 3) scalar products of vectors. As the research results, the application of the developed methods for aggregating the quality indicators of the educational process into a single assessment is shown. The following indicators of the educational process were used: coefficient of diligence, coefficient of interest, coefficient of difficulty of mastering the studied material, assessment of academic performance. A four-criteria optimization problem is formulated. It is shown that the objective function reaches its extreme value at the point that is as close as possible to the point of the optimal solution of the formulated problem. The issues of multi-criteria assessment developed in the article can find application in the management of the educational process in the formation of individual trajectories of trainees, in the tasks of working with the personnel of firms and organizations (selection, training, retraining), in assessing the quality of products and in other areas of activity.
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Alfares, Hesham K., and Salih O. Duffuaa. "Simulation-Based Evaluation of Criteria Rank-Weighting Methods in Multi-Criteria Decision-Making." International Journal of Information Technology & Decision Making 15, no. 01 (January 2016): 43–61. http://dx.doi.org/10.1142/s0219622015500315.

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This paper presents a simulation study to assess the performance of the five known methods for converting ranks of several criteria into weights in multi-criteria decision-making. The five methods assessed for converting criteria ranks into weights are: rank- sum (RS) weights, rank reciprocal (RR) weights, rank order centroid (ROC) weights, geometric weights (GW), and variable-slope linear (VSL) weights. The methods are compared in terms of weight estimation accuracy considering different numbers of criteria and decision makers’ (MS) preference structures. Alternative preference structures are represented by different probability distributions of randomly generated criteria weights, namely the uniform, normal, and exponential distributions. Results of the simulation experiments indicate that no single method is consistently superior to all others. On average, RS is best for uniform weights, VSL is best for normal weights, and ROC is best for exponential weights. However, for any multi-criteria decision-making (MCDM) problem, the best method for converting criteria ranks into weights depends on both the number of criteria and the weight distribution.
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Owais, Mahmoud, Ghada S. Moussa, and Khaled F. Hussain. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach." Operations Research Perspectives 6 (2019): 100100. http://dx.doi.org/10.1016/j.orp.2019.100100.

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Dissertations / Theses on the topic "Multi-criteria estimation"

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Bastidas, Luis Alberto 1950. "Parameter estimation for hydrometeorological models using multi-criteria methods." Diss., The University of Arizona, 1998. http://hdl.handle.net/10150/282748.

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There are three components of error in the ability of land-atmosphere models (e.g., BATS, SiB, etc.) to simulate/predict observed land-surface state variables and output fluxes (e.g. lambdaE, H, Tg, Q, etc.). The first is caused by model structural error associated with simplifications and/or inadequacies in the functional representations of underlying physical processes. The second component is measurement error associated with the input and output data. The third is caused by error in specification of the values of the model parameters. Automatic parameter tuning (model calibration) methods allow minimizing of the parameter error, thereby obtaining an estimate of the remaining error components. This work describes an automatic multi-criteria approach and its use to tune all 27 parameters of the BATS model using data measured in the field. The parameters were adjusted to simultaneously optimize the ability of the model to reproduce observed values of several output fluxes and/or state variables (e.g., latent heat flux, sensible heat flux, ground temperature, etc.). The results indicate that not only does the procedure result in conceptually reasonable and consistent parameter estimates, but the calibrated model is able to provide significant improvement in performance (33% or more reduction in error) over the "un-calibrated" model (i.e., using the BATS default parameter values for the associated region). Substantial improvements of this kind can have important implications for studies that seek to evaluate alternative model structures or to regionalize parameters. To reduce the dimensionality of the optimization problem a multi-criteria extension of the Regionalized Sensitivity Analysis (RSA) has been developed.
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Vander, Biest Alexis. "Developing multi-criteria performance estimation tools for Systems-on-chip." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210356.

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The work presented in this thesis targets the analysis and implementation of multi-criteria performance prediction methods for System-on-Chips (SoC).

These new SoC architectures offer the opportunity to integrate complete heterogeneous systems into a single chip and can be used to design battery powered handhelds, security critical systems, consumer electronics devices, etc. However, this variety in terms of application usually comes with a lot of different performance objectives like power consumption, yield, design cost, production cost, silicon area and many others. These performance requirements are often very difficult to meet together so that SoC design usually relies on making the right design choices and finding the best performance compromises.

In parallel with this architectural paradigm shift, new Very Deep Submicron (VDSM) silicon processes have more and more impact on the performances and deeply modify the way a VLSI system is designed even at the first stages of a design flow.

In such a context where many new technological and system related variables enter the game, early exploration of the impact of design choices becomes crucial to estimate the performance of the system to design and reduce its time-to-market.

In this context, this thesis presents:

- A study of state-of-the-art tools and methods used to estimate the performances of VLSI systems and an original classification based on several features and concepts that they use. Based on this comparison, we highlight their weaknesses and lacks to identify new opportunities in performance prediction.

- The definition of new concepts to enable the automatic exploration of large design spaces based on flexible performance criteria and degrees of freedom representing design choices.

- The implementation of a couple of two new tools of our own:

- Nessie, a tool enabling hierarchical representation of an application along with its platform and automatically performs the mapping and the estimation of their performance.

-Yeti, a C++ library enabling the defintion and value estimation of closed-formed expressions and table-based relations. It provides the user with input and model sensitivity analysis capability, simulation scripting, run-time building and automatic plotting of the results. Additionally, Yeti can work in standalone mode to provide the user with an independent framework for model estimation and analysis.

To demonstrate the use and interest of these tools, we provide in this thesis several case studies whose results are discussed and compared with the literature.

Using Yeti, we successfully reproduced the results of a model estimating multi-core computation power and extended them thanks to the representation flexibility of our tool.

We also built several models from the ground up to help the dimensioning of interconnect links and clock frequency optimization.

Thanks to Nessie, we were able to reproduce the NoC power consumption results of an H.264/AVC decoding application running on a multicore platform. These results were then extended to the case of a 3D die stacked architecture and the performance benefits are then discussed.

We end up by highlighting the advantages of our technique and discuss future opportunities for performance prediction tools to explore.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Richard, Aliénor. "Development and validation of NESSIE: a multi-criteria performance estimation tool for SoC." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210044.

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The work presented in this thesis aims at validating an original multicriteria performances estimation tool, NESSIE, dedicated to the prediction of performances to accelerate the design of electronic embedded systems.

This tool has been developed in a previous thesis to cope with the limitations of existing design tools and offers a new solution to face the growing complexity of the current applications and electronic platforms and the multiple constraints they are subjected to.

More precisely, the goal of the tool is to propose a flexible framework targeting embedded systems in a generic way and enable a fast exploration of the design space based on the estimation of user-defined criteria and a joint hierarchical representation of the application and the platform.

In this context, the purpose of the thesis is to put the original framework NESSIE to the test to analyze if it is indeed useful and able to solve current design problems. Hence, the dissertation presents :

- A study of the State-of-the-Art related to the existing design tools. I propose a classification of these tools and compare them based on typical criteria. This substantial survey completes the State-of-the-Art done in the previous work. This study shows that the NESSIE framework offers solutions to the limitations of these tools.

- The framework of our original mapping tool and its calculation engine. Through this presentation, I highlight the main ingredients of the tool and explain the implemented methodology.

- Two external case studies that have been chosen to validate NESSIE and that are the core of the thesis. These case studies propose two different design problems (a reconfigurable processor, ADRES, applied to a matrix multiplication kernel and a 3D stacking MPSoC problem applied to a video decoder) and show the ability of our tool to target different applications and platforms.

The validation is performed based on the comparison of a multi-criteria estimation of the performances for a significant amount of solutions, between NESSIE and the external design flow. In particular, I discuss the prediction capability of NESSIE and the accuracy of the estimation.

-The study is completed, for each case study, by a quantification of the modeling time and the design time in both flows, in order to analyze the gain achieved by our tool used upstream from the classical tool chain compared to the existing design flow alone.

The results showed that NESSIE is able to predict with a high degree of accuracy the solutions that are the best candidates for the design in the lower design flows. Moreover, in both case studies, modeled respectively at a low and higher abstraction level, I obtained a significant gain in the design time.

However, I also identified limitations that impact the modeling time and could prevent an efficient use of the tool for more complex problems.

To cope with these issues, I end up by proposing several improvements of the framework and give perspectives to further develop the tool.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Wang, Tairan. "Decision making and modelling uncertainty for the multi-criteria analysis of complex energy systems." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0036/document.

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Ce travail de thèse doctorale traite l'analyse de la vulnérabilité des systèmes critiques pour la sécurité (par exemple, les centrales nucléaires) dans un cadre qui combine les disciplines de l'analyse des risques et de la prise de décision de multi-critères.La contribution scientifique suit quatre directions: (i) un modèle hiérarchique et quantitative est développé pour caractériser la susceptibilité des systèmes critiques pour la sécurité à plusieurs types de danger, en ayant la vue de `tous risques' sur le problème actuellement émergeant dans le domaine de l'analyse des risques; (ii) l'évaluation quantitative de la vulnérabilité est abordé par un cadre de classification empirique: à cette fin, un modèle, en se fondant sur la Majority Rule Sorting (MR-Sort) Méthode, généralement utilisés dans le domaine de la prise de décision, est construit sur la base d'un ensemble de données (en taille limitée) représentant (a priori connu) des exemples de classification de vulnérabilité; (iii) trois approches différentes (à savoir, une model-retrieval-based méthode, la méthode Bootstrap et la technique de validation croisée leave-one-out) sont élaborées et appliquées pour fournir une évaluation quantitative de la performance du modèle de classification (en termes de précision et de confiance dans les classifications), ce qui représente l'incertitude introduite dans l'analyse par la construction empirique du modèle de la vulnérabilité; (iv) basé sur des modèles développés, un problème de classification inverse est résolu à identifier un ensemble de mesures de protection qui réduisent efficacement le niveau de vulnérabilité du système critique à l’étude. Deux approches sont développées dans cet objectif: le premier est basé sur un nouvel indicateur de sensibilité, ce dernier sur l'optimisation.Les applications sur des études de cas fictifs et réels dans le domaine des risques de centrales nucléaires démontrent l'efficacité de la méthode proposée
This Ph. D. work addresses the vulnerability analysis of safety-critical systems (e.g., nuclear power plants) within a framework that combines the disciplines of risk analysis and multi-criteria decision-making. The scientific contribution follows four directions: (i) a quantitative hierarchical model is developed to characterize the susceptibility of safety-critical systems to multiple types of hazard, within the needed `all-hazard' view of the problem currently emerging in the risk analysis field; (ii) the quantitative assessment of vulnerability is tackled by an empirical classification framework: to this aim, a model, relying on the Majority Rule Sorting (MR-Sort) Method, typically used in the decision analysis field, is built on the basis of a (limited-size) set of data representing (a priori-known) vulnerability classification examples; (iii) three different approaches (namely, a model-retrieval-based method, the Bootstrap method and the leave-one-out cross-validation technique) are developed and applied to provide a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments), accounting for the uncertainty introduced into the analysis by the empirical construction of the vulnerability model; (iv) on the basis of the models developed, an inverse classification problem is solved to identify a set of protective actions which effectively reduce the level of vulnerability of the critical system under consideration. Two approaches are developed to this aim: the former is based on a novel sensitivity indicator, the latter on optimization.Applications on fictitious and real case studies in the nuclear power plant risk field demonstrate the effectiveness of the proposed methodology
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Monteagudo, Maykel Cruz. "Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe): A Computer-Aided Methodology for Multi-Criteria Drug Discovery." Tese, Faculdade de Farmácia da Universidade do Porto, 2009. http://hdl.handle.net/10216/63799.

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Monteagudo, Maykel Cruz. "Multi-Objective Optimization Based on Desirability Estimation of Several Interrelated Responses (MOOp-DESIRe): A Computer-Aided Methodology for Multi-Criteria Drug Discovery." Doctoral thesis, Faculdade de Farmácia da Universidade do Porto, 2009. http://hdl.handle.net/10216/63799.

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Catalina, Tiberiu. "Estimation of residential buildings energy consumptions and analysis of renewable energy systems using a multi-criteria decision methodology." Lyon, INSA, 2009. http://theses.insa-lyon.fr/publication/2009ISAL0058/these.pdf.

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L'Union européenne (UE) a établi la prospective politique pour atteindre ses objectifs énergétiques fondamentaux pour le développent durable, la compétitivité et la sécurité énergétique, en réduisant les émissions de gaz à effet de serre par le biais d'une augmentation de la part des énergies renouvelables dans la consommation d'énergie et par l'amélioration de l'efficacité énergétique. Les principales questions sur l'utilisation à grande échelle des EnR sont liées au dimensionnement des systèmes, le choix parmi une grande variété de solutions vis-à-vis de plusieurs critères, et enfin le contrôle de ces sources. Dans un proche avenir, de plus en plus les EnR vont cohabiter avec les sources d'énergie fossiles et la recherche doit être orientée vers des solutions qui sont efficaces du point de vue énergétique, économiquement viable et respectueuses de l'environnement. Dans cette thèse, les travaux de recherche établissent une démarche en vue de proposer des solutions qui pourraient être les réponses aux deux premières problématiques que sont le dimensionnement mais surtout la seconde, qui est le choix des systèmes énergétiques les mieux adaptés par rapport a un nombre donné de critères. La première partie de la thèse traite des questions liées à l'estimation des besoins pour le chauffage, l'eau chaude sanitaire et l'énergie électrique. Cette estimation a été abordée par le développement de modèles polynomiaux de régression. La deuxième partie de la thèse examine les techniques de modélisation des systèmes afin d'obtenir les profils de l'approvisionnement en énergie renouvelable : ces quantités d'énergie de l'offre sont nécessaires dans d'analyse décisionnelle. Dans la troisième partie, une méthode d'aide à la décision multicritères (ELECTRE III) est décrite, puis appliquée à un exemple. La technique consiste à utiliser les règles obtenues à partir des modèles précédents et à réaliser un classement de solutions possibles envisagées. L'analyse multicritères fournit ainsi une technique intéressante d'aide dans le processus de décision et est capable de justifier les choix et de surclasser les alternatives dans le secteur des énergies renouvelables. Enfin, les théories, les algorithmes et les modèles qui ont été décrits dans ce travail ont été intégrées dans un outil d'aide à la décision que nous avons développé visant spécifiquement l'intégration des technologies à énergie renouvelable par une analyse multicritères et de faire rapidement des études paramétriques sur les consommations d'énergie des bâtiments. Dans la dernière partie de la thèse une analyse complète d'une étude de cas (maison Mozart) est réalisée avec la mise en application des méthodes proposées
European Union (EU) has agreed a forward-looking political agenda to achieve its core energy objectives of sustainability, competitiveness and security of supply, by reducing greenhouse gas emissions through an increase of the share of renewable in the energy consumption and by improving energy efficiency. The main issues of renewable energy sources large scale use are related to the sizing of the systems, the choice among a large variety of alternatives face to a certain number of criteria, and finally the control of these sources. In the near future, more and more the RES will cohabit with fossil energy source systems and research has to be pointed towards solutions that are energy efficiently, economical viable and environmental friendly. In this thesis, the research work is focus on finding and proposing solutions that could be the answers for the first two main issues presented previous, especially on the second issue which is the choice of systems face to several criteria. The first part treats the issues related to the heating, domestic hot water and electricity energy demand assessment, from the estimation to the impact factors. At this level it is described a new methodology developed to estimate the heating demand of residential buildings in temperate climate by using polynomial regression models obtained from a database of values from dynamic simulations. The second part examines the modeling techniques to obtain the renewable energy supply profiles which are further used in the multicriteria decision analysis. In the third part, a multicriteria decision support methodology concept is de-scribed (ELECTRE III) and then applied for an example. The decision support algorithm has its bases on the developed models and realizes the outranking of the possible. Finally, the theories, algorithms and models that have described in the thesis have been encapsulated in the development of a decision support-tool specifically aimed to aid strategic decisions regarding renewable technology integration and making quick parametric studies on the building energy consumptions. In the last part of the thesis a complete analysis of a study case (Mozart dwelling) is realized with application of the proposed methodologies
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Jiang, Boyi. "GIS-based Multi-criteriaAnalysis Used in Forest Fire Estimation: A Case Study of Northernmost Gävleborg County in Sweden." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9626.

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Fire plays an important role in forest ecosystem management depending on the dual character of it. It should be managed and supervised effectively. In this particular study, the study area was located in the north part of Gävleborg County in Sweden, which is in a high- latitude region. Seven factors, divided into natural factors and human caused factors, were extracted from digital elevation model (DEM), classified land use map and feature shape files provided by National Land Survey of Sweden (Lantmäteriet). Two different weighting schemes for the factors were determined by the Analytic Hierarchy Process (AHP) method. With the help of ArcGIS 9.3 and Erdas 9.3, two classified result maps were obtained, where forest fire risk ranks were shown as five classes, very low, low, moderate, high and very high. The 43 fire incidents in the year 2007 and 2008 recorded by Global Fire Management System were used to evaluate the results. The results show that the higher rank the region is, the larger is the probability for forest fire risk and higher the risk to spread the fire. Furthermore, according to the occurrence time of the fire incidents, the period of time from end of May to beginning of June was generalized as a dangerous period for forest fire risk in this study area. After analyzing and discussing, even if there might be some uncertainties caused by variable selection, resolution problem and weighting schemes, the results were generally reliable.
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Филатова, Анна Евгеньевна. "Метод структурной идентификации биомедицинских изображений с локально сосредоточенными признаками." Thesis, Политехпериодика, 2014. http://repository.kpi.kharkov.ua/handle/KhPI-Press/46308.

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Рассматривается научно-техническая проблема синтеза интеллектуальных систем поддержки принятия решений при обработке биомедицинских сигналов и изображений. В работе используются методы цифровой обработки сигналов и изображений, многокритериальные оценки, методы выделения новых знаний с целью повышения качества принятия решений в условиях априорной неопределенности.
Scientific and technical problem of synthesis of intelligent decision support systems in biomedical signals and images processing is considered. Methods of digital signal and image processing, multi-criteria estimation, methods for detection of new knowledge to improve the quality of decision making under prior uncertainty are used in the work.
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Sargent, Gabriel. "Estimation de la structure de morceaux de musique par analyse multi-critères et contrainte de régularité." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00853737.

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Les récentes évolutions des technologies de l'information et de la communication font qu'il est aujourd'hui facile de consulter des catalogues de morceaux de musique conséquents. De nouvelles représentations et de nouveaux algorithmes doivent de ce fait être développés afin de disposer d'une vision représentative de ces catalogues et de naviguer avec agilité dans leurs contenus. Ceci nécessite une caractérisation efficace des morceaux de musique par l'intermédiaire de descriptions macroscopiques pertinentes. Dans cette thèse, nous nous focalisons sur l'estimation de la structure des morceaux de musique : il s'agit de produire pour chaque morceau une description de son organisation par une séquence de quelques dizaines de segments structurels, définis par leurs frontières (un instant de début et un instant de fin) et par une étiquette représentant leur contenu sonore.La notion de structure musicale peut correspondre à de multiples acceptions selon les propriétés musicales choisies et l'échelle temporelle considérée. Nous introduisons le concept de structure "sémiotique" qui permet de définir une méthodologie d'annotation couvrant un vaste ensemble de styles musicaux. La détermination des segments structurels est fondée sur l'analyse des similarités entre segments au sein du morceau, sur la cohérence de leur organisation interne (modèle "système-contraste") et sur les relations contextuelles qu'ils entretiennent les uns avec les autres. Un corpus de 383 morceaux a été annoté selon cette méthodologie et mis à disposition de la communauté scientifique.En termes de contributions algorithmiques, cette thèse se concentre en premier lieu sur l'estimation des frontières structurelles, en formulant le processus de segmentation comme l'optimisation d'un coût composé de deux termes~: le premier correspond à la caractérisation des segments structurels par des critères audio et le second reflète la régularité de la structure obtenue en référence à une "pulsation structurelle". Dans le cadre de cette formulation, nous comparons plusieurs contraintes de régularité et nous étudions la combinaison de critères audio par fusion. L'estimation des étiquettes structurelles est pour sa part abordée sous l'angle d'un processus de sélection d'automates à états finis : nous proposons un critère auto-adaptatif de sélection de modèles probabilistes que nous appliquons à une description du contenu tonal. Nous présentons également une méthode d'étiquetage des segments dérivée du modèle système-contraste.Nous évaluons différents systèmes d'estimation automatique de structure musicale basés sur ces approches dans le cadre de campagnes d'évaluation nationales et internationales (Quaero, MIREX), et nous complétons cette étude par quelques éléments de diagnostic additionnels.
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Book chapters on the topic "Multi-criteria estimation"

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Fellir, Fadoua, Khalid Nafil, Rajaa Touahni, and Lawrence Chung. "Improving Case Based Software Effort Estimation Using a Multi-criteria Decision Technique." In Advances in Intelligent Systems and Computing, 438–51. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91186-1_46.

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Zaglauer, Susanne, and Michael Deflorian. "Multi-criteria Optimization for Parameter Estimation of Physical Models in Combustion Engine Calibration." In Lecture Notes in Computer Science, 628–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37140-0_47.

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Kumari, Sweta, and Shashank Pushkar. "A Genetic Algorithm Approach for Multi-criteria Project Selection for Analogy-Based Software Cost Estimation." In Computational Intelligence in Data Mining - Volume 3, 13–24. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2202-6_2.

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De Leone, Renato, and Valentina Minnetti. "The Estimation of the Parameters in Multi-Criteria Classification Problem: The Case of the Electre Tri Method." In Studies in Classification, Data Analysis, and Knowledge Organization, 93–101. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06692-9_11.

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Piccinno, Matteo, Adrienn Caronte-Veisz, and Fabio Recanatesi. "Land Use, Phosphorus Pollution and Risk Assessment for the Bolsena Lake (Italy). An Estimation Using Remote Sensing and Multi Criteria Analysis." In New Metropolitan Perspectives, 1618–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48279-4_152.

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Navitskaya, Katsiaryna. "Multi-Criteria Fuzzy Analysis of Competitiveness." In Fuzzy Optimization and Multi-Criteria Decision Making in Digital Marketing, 308–25. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8808-7.ch014.

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The article presents the possibility of using multi-criteria fuzzy analysis for assessing the regional competitiveness. This estimation can be used for place marketing strategy development and based on results of socio-economic development. The proposed approach is characterized by comparative estimation, when the level of development of one region is determined by the development of other areas. The final evaluation is the level of the cluster which the object being analyzed belongs. This allows ignoring minor fluctuations in total indexes. The results of robust and fuzzy groups of regions are analyzed. This grouping is characterized by similar levels of development and helps to define the directions of further development of the regions.
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"Multicriteria Assessment of Projects and Scenarios." In Multi-Criteria Decision Making for the Management of Complex Systems, 72–99. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2509-7.ch005.

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Chapter 5 deals with formulation of the problem of multicriteria assessment of projects and scenarios. Expert assessment methods are reviewed. Determination of criteria values, multicriteria estimation and data processing methods are proposed. Nested scalar convolutions method is proposed. Project profile approach is considered. Vector optimization of hierarchical structures methods is proposed. Structural optimization, description of structure, parameters of significance and calculation of priority factors methods are considered. Calculation of the nested scalar convolutions and calculation of the hierarchical structure assessment method is proposed. Parametric synthesis and substantial model is considered. Optimal allocation of fewer resources method is proposed. System optimization approach is given.
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Smiri, Kamel, and Nourhen Fourati. "Co-Design Flow for Embedded Systems (MPSoC)." In Advances in Computer and Electrical Engineering, 44–62. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3531-7.ch003.

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In this chapter, the authors explore the estimation of the performances in an earlier stage of (multi-processors system on chip) MPSoC design in which it is necessary to drive design space exploration and support important design decisions. Therefore, they address the co-design hardware/software with estimating performances in order to find an adequate solution, which consists in mapping the application on the components of architecture with respect the criteria of performance of the system defined from the beginning. The chapter provides a hybrid model for estimating performance in which cohabited simulation and analytical techniques are carried out via a link layer in order to reach an optimal architectural solution quickly. Thus, it allows faster performance estimation with better accuracy at different levels of abstraction.
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Sangaiah, Arun Kumar, and Vipul Jain. "Fusion of Fuzzy Multi-Criteria Decision Making Approaches for Discriminating Risk with Relate to Software Project Performance." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, 346–73. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch016.

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The prediction and estimation software risks ahead have been key predictor for evaluating project performance. Discriminating risk is vital in software project management phase, where risk and performance has been closely inter-related to each other. This chapter aims at hybridization of fuzzy multi-criteria decision making approaches for building an assessment framework that can be used to evaluate risk in the context of software project performance in following dimensions: 1) user, 2) requirements, 3) project complexity, 4) planning and control, 5) team, and 6) organizational environment. For measuring the risk for effectiveness of project performance, we have integrated Fuzzy Multi-Criteria Decision Making (FMCDM) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches. Moreover the fusion of FMCDM and TOPSIS has not been adequately investigated in the exiting studies.
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Sangaiah, Arun Kumar, and Vipul Jain. "Fusion of Fuzzy Multi-Criteria Decision Making Approaches for Discriminating Risk with Relate to Software Project Performance." In Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making, 38–64. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1008-6.ch003.

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The prediction and estimation software risks ahead have been key predictor for evaluating project performance. Discriminating risk is vital in software project management phase, where risk and performance has been closely inter-related to each other. This chapter aims at hybridization of fuzzy multi-criteria decision making approaches for building an assessment framework that can be used to evaluate risk in the context of software project performance in following dimensions: 1) user, 2) requirements, 3) project complexity, 4) planning and control, 5) team, and 6) organizational environment. For measuring the risk for effectiveness of project performance, we have integrated Fuzzy Multi-Criteria Decision Making (FMCDM) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches. Moreover the fusion of FMCDM and TOPSIS has not been adequately investigated in the exiting studies.
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Conference papers on the topic "Multi-criteria estimation"

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Ivanov, D. A., B. V. Sokolov, E. M. Zaychik, and D. N. Verzilin. "Simulation-Based Multi-Criteria Estimation Of Plans Stability." In 21st Conference on Modelling and Simulation. ECMS, 2007. http://dx.doi.org/10.7148/2007-0209.

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Lootsma, F. A. "Ratio and Difference Estimation in Multi-Criteria Decision Analysis." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0416.

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Abstract Since decisions are invariably made within a given context, we model relative preferences as ratios of increments or decrements in an interval on the axis of desirability. Next, we sort the ratio magnitudes into a small number of categories, represented by numerical values on a geometric scale. We explain why the Analytic Hierarchy Process (AHP), typically based on a pairwise-comparison method, is concerned with category judgement of ratio magnitudes, whereas the Simple Multi-Attribute Rating Technique (SMART) essentially uses the orders of magnitude of these ratios. This provides a common basis for the analysis of the methods in question and for a cross-validation of their results. The present paper summarizes the basic assumptions and the relationship between the two methods.
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Karelina, M. Yu, A. V. Terentyev, V. V. Moiseev, and V. V. Stroev. "Method of Multi-Criteria Estimation of Agro-Industrial Complex Vehicles." In Proceedings of the International Conference "Topical Problems of Philology and Didactics: Interdisciplinary Approach in Humanities and Social Sciences" (TPHD 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/tphd-18.2019.56.

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Karelina, M. Yu, A. V. Terentyev, V. V. Moiseev, and V. V. Stroev. "Enhancement of economic efficiency of transport performance using multi-criteria estimation." In Proceedings of the International conference "Economy in the modern world" (ICEMW 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icemw-18.2018.31.

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Li, Jingzhou, and Guenther Ruhe. "Multi-criteria decision analysis for customization of estimation by analogy method AQUA+." In the 4th international workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1370788.1370803.

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Tavares, Bruna, Victor Freitas, Vladimiro Miranda, and Antonio Simoes Costa. "Merging conventional and phasor measurements in state estimation: A multi-criteria perspective." In 2017 19th International Conference on Intelligent System Application to Power Systems (ISAP). IEEE, 2017. http://dx.doi.org/10.1109/isap.2017.8071423.

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Richard, Alienor, Cedric Hernalsteens, and Frederic Robert. "Development and validation of Nessie: a multi-criteria performance estimation tool for SoC." In 2009 Ph.D. Research in Microelectronics and Electronics (PRIME). IEEE, 2009. http://dx.doi.org/10.1109/rme.2009.5201349.

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Kerdprasop, Nittaya, Kittisak Kerdorasop, and Paradee Chuaybamroong. "A Multi-criteria Scheme to Build Model Ensemble for Dengue Infection Case Estimation." In 2020 International Conference on Decision Aid Sciences and Application (DASA). IEEE, 2020. http://dx.doi.org/10.1109/dasa51403.2020.9317204.

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Franco, Camilo, Jens L. Hougaard, and Kurt Nielsen. "An axiomatic approach to the estimation of interval-valued preferences in multi-criteria decision modeling." In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, 2017. http://dx.doi.org/10.1109/ifsa-scis.2017.8023275.

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Xiong, Wentao, and Huan Qi. "A Extended TOPSIS Method for the Stochastic Multi-Criteria Decision Making Problem through Interval Estimation." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473307.

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Reports on the topic "Multi-criteria estimation"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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