Bibliographies thématiques / Multi-Objective Estimation

Littérature scientifique sur le sujet « Multi-Objective Estimation »

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Publié le 10 mars 2023

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Articles de revues sur le sujet "Multi-Objective Estimation"

R, Rakesh. « Nonlinear Analysis for Parameter Estimation by Multi Objective Single Variable Inverse Analysis ». Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (25 juillet 2020) : 529–38. http://dx.doi.org/10.5373/jardcs/v12sp7/20202136.

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Mitra, Amitava, et Jayprakash G. Patankar. « A multi-objective model for warranty estimation ». European Journal of Operational Research 45, n^o 2-3 (avril 1990) : 347–55. http://dx.doi.org/10.1016/0377-2217(90)90198-k.

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Alberdi, Iciar, Roberto Vallejo, Juan G. Álvarez-González, Sonia Condés, Eduardo González-Ferreiro, Silvia Guerrero, Laura Hernández et al. « The multi-objective Spanish National Forest Inventory ». Forest Systems 26, n^o 2 (3 août 2017) : e04S. http://dx.doi.org/10.5424/fs/2017262-10577.

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Aim of study: To present the evolution of the current multi-objective Spanish National Forest Inventory (SNFI) through the assessment of different key indicators on challenging areas of the forestry sector.Area of study: Using information from the Second, Third and Fourth SNFI, this work provides case studies in Navarra, La Rioja, Galicia and Balearic Island regions and at national Spanish scale.Material and methods: These case studies present an estimation of reference values for dead wood by forest types, diameter-age modeling for Populus alba and Populus nigra in riparian forest, the invasiveness of alien species and the invasibility of forest types, herbivore preferences and effects on trees and shrub species, the methodology for estimating cork production , and the combination of SNFI4 information and Airborne Laser Scanning datasets with the aim of updating forest-fire behavior assessment information with a high degree of accuracy.Main results: The results show the suitability and feasibility of the proposed methodologies to estimate the indicators using SNFI data with the exception of the estimation of cork production. In this case, additional field variables were suggested in order to obtain robust estimates.Research highlights: By broadening the variables recorded, the SNFI has become an even more important source of forest information for the development of support tools for decision-making and assessment in diverse strategic fields such as those analyzed in this study.

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Silva, Cláudia M., et Evaristo C. Biscaia. « Multi-Objective parameter estimation problems : an improved strategy ». Inverse Problems in Science and Engineering 12, n^o 3 (juin 2004) : 297–316. http://dx.doi.org/10.1080/10682760310001598715.

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Barroso, Márcio F. S., Ricardo H. C. Takahashi et Luis A. Aguirre. « Multi-objective parameter estimation via minimal correlation criterion ». Journal of Process Control 17, n^o 4 (avril 2007) : 321–32. http://dx.doi.org/10.1016/j.jprocont.2006.10.005.

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van Brummelen, E. H., S. Zhuk et G. J. van Zwieten. « Worst-case multi-objective error estimation and adaptivity ». Computer Methods in Applied Mechanics and Engineering 313 (janvier 2017) : 723–43. http://dx.doi.org/10.1016/j.cma.2016.10.007.

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Papaioannidis, Christos, et Ioannis Pitas. « 3D Object Pose Estimation Using Multi-Objective Quaternion Learning ». IEEE Transactions on Circuits and Systems for Video Technology 30, n^o 8 (août 2020) : 2683–93. http://dx.doi.org/10.1109/tcsvt.2019.2929600.

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Emami Niri, Mohammad, et David E. Lumley. « Estimation of subsurface geomodels by multi-objective stochastic optimization ». Journal of Applied Geophysics 129 (juin 2016) : 187–99. http://dx.doi.org/10.1016/j.jappgeo.2016.03.031.

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Martins, Marcella S. R., Myriam R. B. S. Delgado, Ricardo Lüders, Roberto Santana, Richard A. Gonçalves et Carolina P. de Almeida. « Hybrid multi-objective Bayesian estimation of distribution algorithm : a comparative analysis for the multi-objective knapsack problem ». Journal of Heuristics 24, n^o 1 (2 septembre 2017) : 25–47. http://dx.doi.org/10.1007/s10732-017-9356-7.

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Dörgő, Gyula, et János Abonyi. « Group Contribution Method-based Multi-objective Evolutionary Molecular Design ». Hungarian Journal of Industry and Chemistry 44, n^o 1 (1 octobre 2016) : 39–50. http://dx.doi.org/10.1515/hjic-2016-0005.

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Abstract The search for compounds exhibiting desired physical and chemical properties is an essential, yet complex problem in the chemical, petrochemical, and pharmaceutical industries. During the formulation of this optimization-based design problem two tasks must be taken into consideration: the automated generation of feasible molecular structures and the estimation of macroscopic properties based on the resultant structures. For this structural characteristic-based property prediction task numerous methods are available. However, the inverse problem, the design of a chemical compound exhibiting a set of desired properties from a given set of fragments is not so well studied. Since in general design problems molecular structures exhibiting several and sometimes conflicting properties should be optimized, we proposed a methodology based on the modification of the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The originally huge chemical search space is conveniently described by the Joback estimation method. The efficiency of the algorithm was enhanced by soft and hard structural constraints, which expedite the search for feasible molecules. These constraints are related to the number of available groups (fragments), the octet rule and the validity of the branches in the molecule. These constraints are also used to introduce a special genetic operator that improves the individuals of the populations to ensure the estimation of the properties is based on only reliable structures. The applicability of the proposed method is tested on several benchmark problems.

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Thèses sur le sujet "Multi-Objective Estimation"

Martins, Marcella Scoczynski Ribeiro. « A hybrid multi-objective bayesian estimation of distribution algorithm ». Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2806.

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Atualmente, diversas metaheurísticas têm sido desenvolvidas para tratarem problemas de otimização multiobjetivo. Os Algoritmos de Estimação de Distribuição são uma classe específica de metaheurísticas que exploram o espaço de variáveis de decisão para construir modelos de distribuição de probabilidade a partir das soluções promissoras. O modelo probabilístico destes algoritmos captura estatísticas das variáveis de decisão e suas interdependências com o problema de otimização. Além do modelo probabilístico, a incorporação de métodos de busca local em Algoritmos Evolutivos Multiobjetivo pode melhorar consideravelmente os resultados. Estas duas técnicas têm sido aplicadas em conjunto na resolução de problemas de otimização multiobjetivo. Nesta tese, um algoritmo de estimação de distribuição híbrido, denominado HMOBEDA (Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm ), o qual é baseado em redes bayesianas e busca local é proposto no contexto de otimização multi e com muitos objetivos a fim de estruturar, no mesmo modelo probabilístico, as variáveis, objetivos e as configurações dos parâmetros da busca local. Diferentes versões do HMOBEDA foram testadas utilizando instâncias do problema da mochila multiobjetivo com dois a cinco e oito objetivos. O HMOBEDA também é comparado com outros cinco métodos evolucionários (incluindo uma versão modificada do NSGA-III, adaptada para otimização combinatória) nas mesmas instâncias do problema da mochila, bem como, em um conjunto de instâncias do modelo MNK-landscape para dois, três, cinco e oito objetivos. As fronteiras de Pareto aproximadas também foram avaliadas utilizando as probabilidades estimadas pelas estruturas das redes resultantes, bem como, foram analisadas as interações entre variáveis, objetivos e parâmetros de busca local a partir da representação da rede bayesiana. Os resultados mostram que a melhor versão do HMOBEDA apresenta um desempenho superior em relação às abordagens comparadas. O algoritmo não só fornece os melhores valores para os indicadores de hipervolume, capacidade e distância invertida geracional, como também apresenta um conjunto de soluções com alta diversidade próximo à fronteira de Pareto estimada.
Nowadays, a number of metaheuristics have been developed for dealing with multiobjective optimization problems. Estimation of distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising solutions. The probabilistic model used in EDA captures statistics of decision variables and their interdependencies with the optimization problem. Moreover, the aggregation of local search methods can notably improve the results of multi-objective evolutionary algorithms. Therefore, these hybrid approaches have been jointly applied to multi-objective problems. In this work, a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), which is based on a Bayesian network, is proposed to multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and configuration parameters of an embedded local search (LS). We tested different versions of HMOBEDA using instances of the multi-objective knapsack problem for two to five and eight objectives. HMOBEDA is also compared with five cutting edge evolutionary algorithms (including a modified version of NSGA-III, for combinatorial optimization) applied to the same knapsack instances, as well to a set of MNK-landscape instances for two, three, five and eight objectives. An analysis of the resulting Bayesian network structures and parameters has also been carried to evaluate the approximated Pareto front from a probabilistic point of view, and also to evaluate how the interactions among variables, objectives and local search parameters are captured by the Bayesian networks. Results show that HMOBEDA outperforms the other approaches. It not only provides the best values for hypervolume, capacity and inverted generational distance indicators in most of the experiments, but it also presents a high diversity solution set close to the estimated Pareto front.

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Morcos, Karim M. « Genetic network parameter estimation using single and multi-objective particle swarm optimization ». Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/9207.

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Master of Science
Department of Electrical and Computer Engineering
Sanjoy Das
Stephen M. Welch
Multi-objective optimization problems deal with finding a set of candidate optimal solutions to be presented to the decision maker. In industry, this could be the problem of finding alternative car designs given the usually conflicting objectives of performance, safety, environmental friendliness, ease of maintenance, price among others. Despite the significance of this problem, most of the non-evolutionary algorithms which are widely used cannot find a set of diverse and nearly optimal solutions due to the huge size of the search space. At the same time, the solution set produced by most of the currently used evolutionary algorithms lacks diversity. The present study investigates a new optimization method to solve multi-objective problems based on the widely used swarm-intelligence approach, Particle Swarm Optimization (PSO). Compared to other approaches, the proposed algorithm converges relatively fast while maintaining a diverse set of solutions. The investigated algorithm, Partially Informed Fuzzy-Dominance (PIFD) based PSO uses a dynamic network topology and fuzzy dominance to guide the swarm of dominated solutions. The proposed algorithm in this study has been tested on four benchmark problems and other real-world applications to ensure proper functionality and assess overall performance. The multi-objective gene regulatory network (GRN) problem entails the minimization of the coefficient of variation of modified photothermal units (MPTUs) across multiple sites along with the total sum of similarity background between ecotypes. The results throughout the current research study show that the investigated algorithm attains outstanding performance regarding optimization aspects, and exhibits rapid convergence and diversity.

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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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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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Petrlík, Jiří. « Multikriteriální genetické algoritmy v predikci dopravy ». Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-412573.

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Porozumění chování silniční dopravy je klíčem pro její efektivní řízení a organizaci. Tato úloha se stává čím dál více důležitou s rostoucími požadavky na dopravu a počtem registrovaných vozidel. Informace o dopravní situaci je důležitá pro řidiče a osoby zodpovědné za její řízení. Naštěstí v posledních několika dekádách došlo k značnému rozvoji technologií pro monitorování dopravní situace. Stacionární senzory, jako jsou indukční smyčky, radary, kamery a infračervené senzory, mohou být nainstalovány na důležitých místech. Zde jsou schopny měřit různé mikroskopické a makroskopické dopravní veličiny. Bohužel mnohá měření obsahují nekorektní data, která není možné použít při dalším zpracování, například pro predikci dopravy a její inteligentní řízení. Tato nekorektní data mohou být způsobena poruchou zařízení nebo problémy při přenosu dat. Z tohoto důvodu je důležité navrhnout obecný framework, který je schopný doplnit chybějící data. Navíc by tento framework měl být také schopen poskytovat krátkodobou predikci budoucího stavu dopravy. Tato práce se především zabývá vybranými problémy v oblasti doplnění chybějících dopravních dat, predikcí dopravy v krátkém časovém horizontu a predikcí dojezdových dob. Navrhovaná řešení jsou založena na kombinaci současných metod strojového učení, například Support vector regression (SVR) a multikriteriálních evolučních algoritmů. SVR má mnoho meta-parametrů, které je nutné dobře nastavit tak, aby byla dosažena co nejkvalitnější predikce. Kvalita predikce SVR dále silně závisí na výběru vhodné množiny vstupních proměnných. V této práci používáme multiktriteriální optimalizaci pro optimalizaci SVR meta-parametrů a množiny vstupních proměnných. Multikriteriální optimalizace nám umožňuje získat mnoho Pareto nedominovaných řešení. Mezi těmito řešeními je možné dynamicky přepínat dle toho, jaká data jsou aktuálně k dispozici tak, aby bylo dosaženo maximální kvality predikce. Metody navržené v této práci jsou především vhodné pro prostředí s velkým množstvím chybějících hodnot v dopravních datech. Tyto metody jsme ověřili na reálných datech a porovnali jejich výsledky s metodami, které jsou v současné době používány. Navržené metody poskytují lepší výsledky než stávající metody, a to především ve scénářích, kde se vyskytuje mnoho chybějících hodnot v dopravních datech.

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Xu, Weili. « An Energy and Cost Performance Optimization Platform for Commercial Building System Design ». Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/956.

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Energy and cost performance optimization for commercial building system design is growing in popularity, but it is often criticized for its time consuming process. Moreover, the current process lacks integration, which not only affects time performance, but also investors’ confidence in the predicted performance of the generated design. Such barriers keep building owners and design teams from embracing life cycle cost consideration. This thesis proposes a computationally efficient design optimization platform to improve the time performance and to streamline the workflow in an integrated multi-objective building system design optimization process. First, building system cost estimation is typically completed through a building information model based quantity take-off process, which does not provide sufficient design decision support features in the design process. To remedy this issue, an automatic cost estimation framework that integrates EnergyPlus with an external database to perform building systems’ capital and operation costs is proposed. Optimization, typically used for building system design selection, requires a large amount of computational time. The optimization process evaluates building envelope, electrical and HVAC systems in an integrated system not only to explore the cost-saving potential from a single high performance system, but also the interrelated effects among different systems. An innovative optimization strategy that integrates machine learning techniques with a conventional evolutionary algorithm is proposed. This strategy can reduce run time and improve the quality of the solutions. Lastly, developing baseline energy models typically takes days or weeks depending on the scale of the design. An automated system for generating baseline energy model according to ANSI/ASHRAE/IESNA Standard 90.1 performance rating method is thus proposed to provide a quick appraisal of optimal designs in comparison with the baseline energy requirements. The main contribution of this thesis is the development of a new design optimization platform to expedite the conventional decision making process. The platform integrates three systems: (1) cost estimation, (2) optimization and (3) benchmark comparison for minimizing the first cost and energy operation costs. This allows designers to confidently select an optimal design with high performance building systems by making a comparison with the minimum energy baseline set by standards in the building industry. Two commercial buildings are selected as case studies to demonstrate the effectiveness of this platform. One building is the Center for Sustainable Landscapes in Pittsburgh, PA. This case study is used as a new construction project. With 54 million possible design solutions, the platform is able to identify optimal designs in four hours. Some of the design solutions not only save the operation costs by up to 23% compared to the ASHRAE baseline design, but also reduce the capital cost ranging from 5% to 23%. Also, compared with the ASHRAE baseline design, one design solution demonstrates that the high investment of a product, building integrative photovoltaic (BiPV) system, can be justified through the integrative design optimization approach by the lower operation costs (20%) as well as the lower capital cost (12%). The second building is the One Montgomery Plaza, a large office building in Norristown, PA. This case study focuses on using the platform for a retrofit project. The calibrated energy model requires one hour to complete the simulation. There are 4000 possible design solutions proposed and the platform is able to find the optimal design solution in around 50 hours. Similarly, the results indicate that up to 25% capital cost can be saved with $1.7 million less operation costs in 25 years, compare to the ASHRAE baseline design.

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Hartikka, Alice, et Simon Nordenhög. « Emission Calculation Model for Vehicle Routing Planning : Estimation of emissions from heavy transports and optimization with carbon dioxide equivalents for a route planning software ». Thesis, Linköpings universitet, Energisystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178065.

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The transport sector is a major cause of emissions both in Sweden and globally. This master thesis aims to develop a model for estimating emissions from heavy transport on a specific route. The emissions can be used in a route planning software and help the driver choose a route that contributes to reduced emissions. The methodology was to investigate attributes, like vehicle-related attributes and topography, and their impact on transport emissions. The carbon dioxide, methane and nitrous oxide emissions were converted into carbon dioxide equivalents, which were incorporated as one cost together with a precalculated driving time as a second cost in a multi objective function used for route planning. Different tests were conducted to investigate the accuracy and the usability of the model. First, a validation test was performed, where the optimized routes were analyzed. The test showed that the model was more likely to choose a shorter route in general. The fuel consumption values largely met expectations towards generic values and measurements, that were gathered from research. A second test of the model made use of the driving time combined with the emissions in a multi objective function. In this test, a weighting coefficient was varied and analyzed to understand the possibility to find a value of the coefficient for the best trade-off. The result showed that the model generates different solutions for different coefficients and that it is possible to find a suitable trade-off between the driving time and emissions. Therefore, this study shows that there is a possibility to combine emission with other objectives such as driving time for route optimization. Finally, a sensitivity analysis was performed, where attribute factors and assumptions were varied to see how sensitive they were and, in turn, how much a change would impact the calculated emissions. The result from the sensitivity analysis showed that the changes in topography-attributes had less impact than changes on vehicle-related attributes. In conclusion, this thesis has built a foundation for route planning, based on the environmental aspect, for heavy transports.

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Skolpadungket, Prisadarng. « Portfolio management using computational intelligence approaches : forecasting and optimising the stock returns and stock volatilities with fuzzy logic, neural network and evolutionary algorithms ». Thesis, University of Bradford, 2013. http://hdl.handle.net/10454/6306.

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Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN's initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

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Thenon, Arthur. « Utilisation de méta-modèles multi-fidélité pour l'optimisation de la production des réservoirs ». Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066100/document.

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Les simulations d'écoulement sur des modèles représentatifs d'un gisement pétrolier sont généralement coûteuses en temps de calcul. Une pratique courante en ingénierie de réservoir consiste à remplacer ces simulations par une approximation mathématique, un méta-modèle. La méta-modélisation peut fortement réduire le nombre de simulations nécessaires à l'analyse de sensibilité, le calibrage du modèle, l'estimation de la production, puis son optimisation. Cette thèse porte sur l'étude de méta-modèles utilisant des simulations réalisées à différents niveaux de précision, par exemple pour des modèles de réservoir avec des maillages de résolutions différentes. L'objectif est d'accélérer la construction d'un méta-modèle prédictif en combinant des simulations coûteuses avec des simulations rapides mais moins précises. Ces méta-modèles multi-fidélité, basés sur le co-krigeage, sont comparés au krigeage pour l'approximation de sorties de la simulation d'écoulement. Une analyse en composantes principales peut être considérée afin de réduire le nombre de modèles de krigeage pour la méta-modélisation de réponses dynamiques et de cartes de propriétés. Cette méthode peut aussi être utilisée pour améliorer la méta-modélisation de la fonction objectif dans le cadre du calage d'historique. Des algorithmes de planification séquentielle d'expériences sont finalement proposés pour accélérer la méta-modélisation et tirer profit d'une approche multi-fidélité. Les différentes méthodes introduites sont testées sur deux cas synthétiques inspirés des benchmarks PUNQ-S3 et Brugge
Performing flow simulations on numerical models representative of oil deposits is usually a time consuming task in reservoir engineering. The substitution of a meta-model, a mathematical approximation, for the flow simulator is thus a common practice to reduce the number of calls to the flow simulator. It permits to consider applications such as sensitivity analysis, history-matching, production estimation and optimization. This thesis is about the study of meta-models able to integrate simulations performed at different levels of accuracy, for instance on reservoir models with various grid resolutions. The goal is to speed up the building of a predictive meta-model by balancing few expensive but accurate simulations, with numerous cheap but approximated ones. Multi-fidelity meta-models, based on co-kriging, are thus compared to kriging meta-models for approximating different flow simulation outputs. To deal with vectorial outputs without building a meta-model for each component of the vector, the outputs can be split on a reduced basis using principal component analysis. Only a few meta-models are then needed to approximate the main coefficients in the new basis. An extension of this approach to the multi-fidelity context is proposed. In addition, it can provide an efficient meta-modelling of the objective function when used to approximate each production response involved in the objective function definition. The proposed methods are tested on two synthetic cases derived from the PUNQ-S3 and Brugge benchmark cases. Finally, sequential design algorithms are introduced to speed-up the meta-modeling process and exploit the multi-fidelity approach

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Song, Yingying. « Amélioration de la résolution spatiale d’une image hyperspectrale par déconvolution et séparation-déconvolution conjointes ». Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0207/document.

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Une image hyperspectrale est un cube de données 3D dont chaque pixel fournit des informations spectrales locales sur un grand nombre de bandes contiguës sur une scène d'intérêt. Les images observées peuvent subir une dégradation due à l'instrument de mesure, avec pour conséquence l'apparition d'un flou sur les images qui se modélise par une opération de convolution. La déconvolution d'image hyperspectrale (HID) consiste à enlever le flou pour améliorer au mieux la résolution spatiale des images. Un critère de HID du type Tikhonov avec contrainte de non-négativité est proposé dans la thèse de Simon Henrot. Cette méthode considère les termes de régularisations spatiale et spectrale dont la force est contrôlée par deux paramètres de régularisation. La première partie de cette thèse propose le critère de courbure maximale MCC et le critère de distance minimum MDC pour estimer automatiquement ces paramètres de régularisation en formulant le problème de déconvolution comme un problème d'optimisation multi-objectif. La seconde partie de cette thèse propose l'algorithme de LMS avec un bloc lisant régularisé (SBR-LMS) pour la déconvolution en ligne des images hyperspectrales fournies par les systèmes de whiskbroom et pushbroom. L'algorithme proposé prend en compte la non-causalité du noyau de convolution et inclut des termes de régularisation non quadratiques tout en maintenant une complexité linéaire compatible avec le traitement en temps réel dans les applications industrielles. La troisième partie de cette thèse propose des méthodes de séparation-déconvolution conjointes basés sur le critère de Tikhonov en contextes hors-ligne ou en-ligne. L'ajout d'une contrainte de non-négativité permet d’améliorer leurs performances
A hyperspectral image is a 3D data cube in which every pixel provides local spectral information about a scene of interest across a large number of contiguous bands. The observed images may suffer from degradation due to the measuring device, resulting in a convolution or blurring of the images. Hyperspectral image deconvolution (HID) consists in removing the blurring to improve the spatial resolution of images at best. A Tikhonov-like HID criterion with non-negativity constraint is considered here. This method considers separable spatial and spectral regularization terms whose strength are controlled by two regularization parameters. First part of this thesis proposes the maximum curvature criterion MCC and the minimum distance criterion MDC to automatically estimate these regularization parameters by formulating the deconvolution problem as a multi-objective optimization problem. The second part of this thesis proposes the sliding block regularized (SBR-LMS) algorithm for the online deconvolution of hypserspectral images as provided by whiskbroom and pushbroom scanning systems. The proposed algorithm accounts for the convolution kernel non-causality and including non-quadratic regularization terms while maintaining a linear complexity compatible with real-time processing in industrial applications. The third part of this thesis proposes joint unmixing-deconvolution methods based on the Tikhonov criterion in both offline and online contexts. The non-negativity constraint is added to improve their performances

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Chapitres de livres sur le sujet "Multi-Objective Estimation"

Peng, Yiming, et Hisao Ishibuchi. « Niching Diversity Estimation for Multi-modal Multi-objective Optimization ». Dans Lecture Notes in Computer Science, 323–34. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72062-9_26.

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Punnapala, Sameer, Francisco M. Vargas et Ali Elkamel. « Parameter Estimation in Phase Equilibria Calculations Using Multi-Objective Evolutionary Algorithms ». Dans Multi-Objective Optimization in Chemical Engineering, 247–65. Oxford, UK : John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.ch9.

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Periasamy, Karthik Raja, et S. Lakshminarayanan. « Estimation of Crystal Size Distribution : Image Thresholding Based on Multi-Objective Optimization ». Dans Multi-Objective Optimization in Chemical Engineering, 399–422. Oxford, UK : John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118341704.ch14.

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Rao, G. Sivanageswara, Ch V. Phani Krishna et K. Rajasekhara Rao. « Multi Objective Particle Swarm Optimization for Software Cost Estimation ». Dans ICT and Critical Infrastructure : Proceedings of the 48th Annual Convention of Computer Society of India- Vol I, 125–32. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03107-1_15.

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Whigham, Peter A., et Caitlin Owen. « Multi-objective Optimisation, Software Effort Estimation and Linear Models ». Dans Lecture Notes in Computer Science, 263–73. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13563-2_23.

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Palar, Pramudita Satria, et Koji Shimoyama. « Multiple Metamodels for Robustness Estimation in Multi-objective Robust Optimization ». Dans Lecture Notes in Computer Science, 469–83. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54157-0_32.

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Hao, Xinchang, Lu Sun et Mitsuo Gen. « Multi-objective Job Shop Rescheduling with Estimation of Distribution Algorithm ». Dans Proceedings of the Eleventh International Conference on Management Science and Engineering Management, 35–46. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59280-0_3.

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Arya, Anoop, Yogendra Kumar, Manisha Dubey et Radharaman Gupta. « Multi-Objective Fault Section Estimation in Distribution Systems Using Elitist NSGA ». Dans Advances in Intelligent Systems and Computing, 211–19. India : Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-1041-2_18.

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Monzón, Hugo, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel et Kiyoshi Tanaka. « Dynamic Compartmental Models for Large Multi-objective Landscapes and Performance Estimation ». Dans Evolutionary Computation in Combinatorial Optimization, 99–113. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43680-3_7.

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Vander Biest, Alexis, Alienor Richard, Dragomir Milojevic et Frederic Robert. « A Multi-objective and Hierarchical Exploration Tool for SoC Performance Estimation ». Dans Lecture Notes in Computer Science, 85–95. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-70550-5_10.

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Actes de conférences sur le sujet "Multi-Objective Estimation"

Sarro, Federica, Alessio Petrozziello et Mark Harman. « Multi-objective software effort estimation ». Dans ICSE '16 : 38th International Conference on Software Engineering. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2884781.2884830.

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Zangl, H., et G. Steiner. « Optimal design of multi-objective multi-sensor systems ». Dans Proceedings of the 2005 IEEE International Workshop on Advanced Methods for Uncertainty Estimation in Measurement. IEEE, 2005. http://dx.doi.org/10.1109/amuem.2005.1594616.

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Ascher, Dominik, et Georg Hackenberg. « Early estimation of multi-objective traffic flow ». Dans 2014 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, 2014. http://dx.doi.org/10.1109/iccve.2014.7297511.

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Takagi, Tomoaki, Keiki Takadama et Hiroyuki Sato. « Supervised Multi-Objective Optimization Algorithm Using Estimation ». Dans 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022. http://dx.doi.org/10.1109/cec55065.2022.9870375.

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Mensah, Solomon, Jacky Keung, Kwabena Ebo Bennin et Michael Franklin Bosu. « Multi-Objective Optimization for Software Testing Effort Estimation ». Dans The 28th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2016. http://dx.doi.org/10.18293/seke2016-163.

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Mendoza-Gonzalez, Alfredo, Eunice Ponce-de-Leon et Elva Diaz-Diaz. « Classification scheme of multi-objective Estimation of Distribution Algorithms ». Dans 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557941.

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Di Fina, Dario, Svebor Karaman, Andrew D. Bagdanov et Alberto Del Bimbo. « MORF : Multi-Objective Random Forests for face characteristic estimation ». Dans 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2015. http://dx.doi.org/10.1109/avss.2015.7301793.

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Zhong, Xiaoping, et Weiji Li. « A Decision-Tree-Based Multi-objective Estimation of Distribution Algorithm ». Dans 2007 International Conference on Computational Intelligence and Security (CIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/cis.2007.136.

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Nayeem, Muhammad Ali, Md Shamsuzzoha Bayzid, Sakshar Chakravarty, Mohammad Saifur Rahman et M. Sohel Rahman. « A Multi-objective Metaheuristic Approach for Accurate Species Tree Estimation ». Dans 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2020. http://dx.doi.org/10.1109/bibe50027.2020.00021.

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Agrawal, Gautam, Sumeet Parashar et Christina Bloebaum. « Estimation of Multi-Objective Pareto Frontier using Hyperspace Diagonal Counting ». Dans 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-6959.

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Rapports d'organisations sur le sujet "Multi-Objective Estimation"

Waddell, Lucas, John Gauthier, Matthew Hoffman, Denise Padilla, Stephen Henry, Alexander Dessanti et Adam Pierson. Estimating the Adequacy of a Multi-Objective Optimization . Office of Scientific and Technical Information (OSTI), novembre 2021. http://dx.doi.org/10.2172/1833178.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak et Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, juillet 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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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor et Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, janvier 2016. http://dx.doi.org/10.32747/2016.7600047.bard.

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Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.

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