Relevant bibliographies by topics / Fusion approaches

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

Academic literature on the topic 'Fusion approaches'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Fusion approaches"

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Michmerhuizen, Nicole L., Jeffery M. Klco, and Charles G. Mullighan. "Mechanistic insights and potential therapeutic approaches for NUP98-rearranged hematologic malignancies." Blood 136, no. 20 (November 12, 2020): 2275–89. http://dx.doi.org/10.1182/blood.2020007093.

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Abstract Nucleoporin 98 (NUP98) fusion oncoproteins are observed in a spectrum of hematologic malignancies, particularly pediatric leukemias with poor patient outcomes. Although wild-type full-length NUP98 is a member of the nuclear pore complex, the chromosomal translocations leading to NUP98 gene fusions involve the intrinsically disordered and N-terminal region of NUP98 with over 30 partner genes. Fusion partners include several genes bearing homeodomains or having known roles in transcriptional or epigenetic regulation. Based on data in both experimental models and patient samples, NUP98 fusion oncoprotein–driven leukemogenesis is mediated by changes in chromatin structure and gene expression. Multiple cofactors associate with NUP98 fusion oncoproteins to mediate transcriptional changes possibly via phase separation, in a manner likely dependent on the fusion partner. NUP98 gene fusions co-occur with a set of additional mutations, including FLT3–internal tandem duplication and other events contributing to increased proliferation. To improve the currently dire outcomes for patients with NUP98-rearranged malignancies, therapeutic strategies have been considered that target transcriptional and epigenetic machinery, cooperating alterations, and signaling or cell-cycle pathways. With the development of more faithful experimental systems and continued study, we anticipate great strides in our understanding of the molecular mechanisms and therapeutic vulnerabilities at play in NUP98-rearranged models. Taken together, these studies should lead to improved clinical outcomes for NUP98-rearranged leukemia.
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Pau, L. "Knowledge Representation Approaches in Sensor Fusion." IFAC Proceedings Volumes 20, no. 5 (July 1987): 323–27. http://dx.doi.org/10.1016/s1474-6670(17)55221-0.

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Katsaggelos, Aggelos K., Sara Bahaadini, and Rafael Molina. "Audiovisual Fusion: Challenges and New Approaches." Proceedings of the IEEE 103, no. 9 (September 2015): 1635–53. http://dx.doi.org/10.1109/jproc.2015.2459017.

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Coulter, Anne H. "Laser Tissue Fusion Approaches Clinical Utility." Journal of Clinical Laser Medicine & Surgery 10, no. 3 (June 1992): 229–33. http://dx.doi.org/10.1089/clm.1992.10.229.

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Konieczny, Sébastien, and Éric Grégoire. "Logic-based approaches to information fusion." Information Fusion 7, no. 1 (March 2006): 2–3. http://dx.doi.org/10.1016/j.inffus.2005.07.002.

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Grégoire, Eric, and Sébastien Konieczny. "Logic-based approaches to information fusion." Information Fusion 7, no. 1 (March 2006): 4–18. http://dx.doi.org/10.1016/j.inffus.2005.08.001.

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Pau, L. "Knowledge representation approaches in sensor fusion." Automatica 25, no. 2 (March 1989): 207–14. http://dx.doi.org/10.1016/0005-1098(89)90073-3.

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Saraç, Fatma, Sevgi Sarsu Büyükbeşe, Mehmet Toptaş, Ayşe Saygılı, and Kamil Şahin. "Approaches to the Treatment of Labial Fusion." Haseki Tıp Bülteni 54, no. 2 (June 27, 2016): 67–69. http://dx.doi.org/10.4274/haseki.2728.

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Clery, Daniel. "Laser fusion reactor approaches ‘burning plasma’ milestone." Science 370, no. 6520 (November 26, 2020): 1019–20. http://dx.doi.org/10.1126/science.370.6520.1019.

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Hammer, J. "Alternative Approaches to High Energy Density Fusion." Journal of Physics: Conference Series 688 (March 2016): 012025. http://dx.doi.org/10.1088/1742-6596/688/1/012025.

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Dissertations / Theses on the topic "Fusion approaches"

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Michael, Andrew M. "Imaging schizophrenia : data fusion approaches to characterize and classify /." Online version of thesis, 2009. http://hdl.handle.net/1850/9673.

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Andrade, Valente da Silva Michelle. "SLAM and data fusion for autonomous vehicles : from classical approaches to deep learning methods." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEM079.

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L'arrivée des voitures autonomes va provoquer une transformation très importante de la mobilité urbaine telle que nous la connaissons, avec un impact significatif sur notre vie quotidienne. En effet, elles proposent un nouveau système de déplacement plus efficace, plus facilement accessible et avec une meilleure sécurité routière. Pour atteindre cet objectif, les véhicules autonomes doivent effectuer en toute sécurité et de manière autonome trois tâches principales: la perception, la planification et le contrôle. La perception est une tâche particulièrement difficile en milieu urbain, car elle se doit d'être suffisamment précise pour assurer à la fois la sécurité du conducteur et celle des autres. Il est décisif d’avoir une bonne compréhension de l’environnement et de ses obstacles, ainsi qu’une localisation précise, afin que les autres tâches puissent être performantes. L'objectif de cette thèse est d'explorer différentes techniques pour la cartographie et la localisation des voitures autonomes en milieu urbain, en partant des approches classiques jusqu'aux algorithmes d'apprentissage profond. On s'intéresse plus spécifiquement aux véhicules équipés de capteurs bon marché avec l'idée de maintenir un prix raisonnable pour les futures voitures autonomes. Dans cette optique, nous utilisons dans les méthodes proposées des capteurs comme des scanner laser 2D, des caméras et des centrales inertielles à bas coût. Dans la première partie, nous introduisons des méthodes classiques utilisant des grilles d'occupation évidentielles. Dans un premier temps, nous présentons une nouvelle approche pour faire de la fusion entre une caméra et un scanner laser 2D pour améliorer la perception de l'environnement. De plus, nous avons ajouté une nouvelle couche dans notre grille d'occupation afin d'affecter un état à chaque objet détecté. Cet état permet de suivre l'objet et de déterminer s'il est statique ou dynamique. Ensuite, nous proposons une méthode de localisation s'appuyant sur cette nouvelle couche ainsi que sur des techniques de superposition d'images pour localiser le véhicule tout en créant une carte de l'environnement. Dans la seconde partie, nous nous intéressons aux algorithmes d'apprentissage profond appliqués à la localisation. D'abord, nous introduisons une méthode d'apprentissage pour l'estimation d'odométrie utilisant seulement des données issues de scanners laser 2D. Cette approche démontre l'intérêt des réseaux de neurones comme un bon moyen pour analyser ce type de données, dans l'optique d'estimer le déplacement du véhicule. Ensuite, nous étendons la méthode précédente en fusionnant le laser scanner 2D avec une caméra dans un système d'apprentissage de bout-en-bout. L'ajout de cette caméra permet d'améliorer la précision de l'estimation d'odométrie et prouve qu'il est possible de faire de la fusion de capteurs avec des réseaux de neurones. Finalement, nous présentons un nouvel algorithme hybride permettant à un véhicule de se localiser dans une région déjà cartographiée. Cet algorithme s'appuie à la fois sur une grille évidentielle prenant en compte les objets dynamiques et sur la capacité des réseaux de neurones à analyser des images. Les résultats obtenus lors de cette thèse nous ont permis de mieux comprendre les problématiques liées à l'utilisation de capteurs bon marché dans un environnement dynamique. En adaptant nos méthodes à ces capteurs et en introduisant une fusion de leur information, nous avons amélioré la perception générale de l'environnement ainsi que la localisation du véhicule. De plus, notre approche a permis d'identifier les avantages et inconvénients entre les différentes méthodes classiques et d'apprentissage. Ainsi, nous proposons une manière de combiner ces deux types d'approches dans un système hybride afin d'obtenir une localisation plus précise et plus robuste
Self-driving cars have the potential to provoke a mobility transformation that will impact our everyday lives. They offer a novel mobility system that could provide more road safety, efficiency and accessibility to the users. In order to reach this goal, the vehicles need to perform autonomously three main tasks: perception, planning and control. When it comes to urban environments, perception becomes a challenging task that needs to be reliable for the safety of the driver and the others. It is extremely important to have a good understanding of the environment and its obstacles, along with a precise localization, so that the other tasks are well performed. This thesis explores from classical approaches to Deep Learning techniques to perform mapping and localization for autonomous vehicles in urban environments. We focus on vehicles equipped with low-cost sensors with the goal to maintain a reasonable price for the future autonomous vehicles. Considering this, we use in the proposed methods sensors such as 2D laser scanners, cameras and standard IMUs. In the first part, we introduce model-based methods using evidential occupancy grid maps. First, we present an approach to perform sensor fusion between a stereo camera and a 2D laser scanner to improve the perception of the environment. Moreover, we add an extra layer to the grid maps to set states to the detected obstacles. This state allows to track an obstacle overtime and to determine if it is static or dynamic. Sequentially, we propose a localization system that uses this new layer along with classic image registration techniques to localize the vehicle while simultaneously creating the map of the environment. In the second part, we focus on the use of Deep Learning techniques for the localization problem. First, we introduce a learning-based algorithm to provide odometry estimation using only 2D laser scanner data. This method shows the potential of neural networks to analyse this type of data for the estimation of the vehicle's displacement. Sequentially, we extend the previous method by fusing the 2D laser scanner with a camera in an end-to-end learning system. The addition of camera images increases the accuracy of the odometry estimation and proves that we can perform sensor fusion without any sensor modelling using neural networks. Finally, we present a new hybrid algorithm to perform the localization of a vehicle inside a previous mapped region. This algorithm takes the advantages of the use of evidential maps in dynamic environments along with the ability of neural networks to process images. The results obtained in this thesis allowed us to better understand the challenges of vehicles equipped with low-cost sensors in dynamic environments. By adapting our methods for these sensors and performing the fusion of their information, we improved the general perception of the environment along with the localization of the vehicle. Moreover, our approaches allowed a possible comparison between the advantages and disadvantages of learning-based techniques compared to model-based ones. Finally, we proposed a form of combining these two types of approaches in a hybrid system that led to a more robust solution
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Pakhotin, Ivan. "Fusion of first principles driven and system science approaches to advance radiation belt forecasting." Thesis, University of Sheffield, 2014. http://etheses.whiterose.ac.uk/7260/.

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Modern radiation belt models can be broadly split into either physics-driven diffusion based algorithms, or data science techniques that utilise the continuous data coverage from satellites at geostationary orbit and the Lagrange point L1 to apply statistical data analysis methods to predict electron fluxes at geostationary orbit. The first kind, while posessing generality due to their physical nature, lack accuracy compared to their data-driven counterparts. This is because the magnetosphere is a highly complex system that is not easy to model and its dynamics are not yet fully understood. Meanwhile, data-driven methods possess statistical accuracy, but cannot predict outside their operating parameters, and so on their own provide no information about what happens in the wider radiation belt region. This thesis is devoted to the development of a model that combines the two approaches into one unified system, attempting to combine the predictive range of physical modelling with the accuracy of the data-driven approach. This model uses geosynchronous orbit fluxes predicted using an advanced data science technique as an input to drive a physics-based radiation belt modelling code. The model has been developed and tested for a range of energy channels, magnetospheric conditions, and with various modifications. It was validated using data from NASA's recent Van Allen probes mission and with NOAA's GOES-13 geostationary satellite. The model results are in good agreement with observations, with the sources of inaccuracies explored in the manuscript. This work is a first attempt to create such a model, and potential improvements are outlined that should further increase accuracy. A further modification of the model is explored that is found to provide superior performance at geostationary orbit at the cost of degraded performance elsewhere. It is proposed to use this modification in tandem with the main model, where accurate information about geostationary orbit is required. The modification has been tested on long-duration time periods and was found to generate good predictions for high-energy electron fluxes. The role of electromagnetic ion cyclotron (EMIC) waves is explored using wave vector analysis and calculation of minimum resonant energies. The aim is to identify what effect EMIC waves have on electron dynamics at energies below 1 MeV. The conclusions are that EMIC waves, under certain conditions, do affect these electron populations in the magnetosphere, and their effect should be included in a representative radiation belt model. This is suggested as a further improvement to the simulation.
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江卓庭 and Cheuk-ting Kong. "Understanding the function of the Mll-een leukaemic fusion gene by embryonic stem cell approaches." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31244312.

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Stone, David L. "The Application of Index Based, Region Segmentation, and Deep Learning Approaches to Sensor Fusion for Vegetation Detection." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5708.

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This thesis investigates the application of index based, region segmentation, and deep learning methods to the sensor fusion of omnidirectional (O-D) Infrared (IR) sensors, Kinnect sensors, and O-D vision sensors to increase the level of intelligent perception for unmanned robotic platforms. The goals of this work is first to provide a more robust calibration approach and improve the calibration of low resolution and noisy IR O-D cameras. Then our goal was to explore the best approach to sensor fusion for vegetation detection. We looked at index based, region segmentation, and deep learning methods and compared them with a goal of significant reduction in false positives while maintaining reasonable vegetation detection. The results are as follows: Direct Spherical Calibration of the IR camera provided a more consistent and robust calibration board capture and resulted in the best overall calibration results with sub-pixel accuracy The best approach for sensor fusion for vegetation detection was the deep learning approach, the three methods are detailed in the following chapters with the results summarized here. Modified Normalized Difference Vegetation Index approach achieved 86.74% recognition and 32.5% false positive, with peaks to 80% Thermal Region Fusion (TRF) achieved a lower recognition rate at 75.16% but reduced false positives to 11.75% (a 64% reduction) Our Deep Learning Fusion Network (DeepFuseNet) results demonstrated that deep learning approach showed the best results with a significant (92%) reduction in false positives when compared to our modified normalized difference vegetation index approach. The recognition was 95.6% with 2% false positive. Current approaches are primarily focused on O-D color vision for localization, mapping, and tracking and do not adequately address the application of these sensors to vegetation detection. We will demonstrate the contradiction between current approaches and our deep sensor fusion (DeepFuseNet) for vegetation detection. The combination of O-D IR and O-D color vision coupled with deep learning for the extraction of vegetation material type, has great potential for robot perception. This thesis will look at two architectures: 1) the application of Autoencoders Feature Extractors feeding a deep Convolution Neural Network (CNN) fusion network (DeepFuseNet), and 2) Bottleneck CNN feature extractors feeding a deep CNN fusion network (DeepFuseNet) for the fusion of O-D IR and O-D visual sensors. We show that the vegetation recognition rate and the number of false detects inherent in the classical indices based spectral decomposition are greatly improved using our DeepFuseNet architecture. We first investigate the calibration of omnidirectional infrared (IR) camera for intelligent perception applications. The low resolution omnidirectional (O-D) IR image edge boundaries are not as sharp as with color vision cameras, and as a result, the standard calibration methods were harder to use and less accurate with the low definition of the omnidirectional IR camera. In order to more fully address omnidirectional IR camera calibration, we propose a new calibration grid center coordinates control point discovery methodology and a Direct Spherical Calibration (DSC) approach for a more robust and accurate method of calibration. DSC addresses the limitations of the existing methods by using the spherical coordinates of the centroid of the calibration board to directly triangulate the location of the camera center and iteratively solve for the camera parameters. We compare DSC to three Baseline visual calibration methodologies and augment them with additional output of the spherical results for comparison. We also look at the optimum number of calibration boards using an evolutionary algorithm and Pareto optimization to find the best method and combination of accuracy, methodology and number of calibration boards. The benefits of DSC are more efficient calibration board geometry selection, and better accuracy than the three Baseline visual calibration methodologies. In the context of vegetation detection, the fusion of omnidirectional (O-D) Infrared (IR) and color vision sensors may increase the level of vegetation perception for unmanned robotic platforms. A literature search found no significant research in our area of interest. The fusion of O-D IR and O-D color vision sensors for the extraction of feature material type has not been adequately addressed. We will look at augmenting indices based spectral decomposition with IR region based spectral decomposition to address the number of false detects inherent in indices based spectral decomposition alone. Our work shows that the fusion of the Normalized Difference Vegetation Index (NDVI) from the O-D color camera fused with the IR thresholded signature region associated with the vegetation region, minimizes the number of false detects seen with NDVI alone. The contribution of this work is the demonstration of two new techniques, Thresholded Region Fusion (TRF) technique for the fusion of O-D IR and O-D Color. We also look at the Kinect vision sensor fused with the O-D IR camera. Our experimental validation demonstrates a 64% reduction in false detects in our method compared to classical indices based detection. We finally compare our DeepFuseNet results with our previous work with Normalized Difference Vegetation index (NDVI) and IR region based spectral fusion. This current work shows that the fusion of the O-D IR and O-D visual streams utilizing our DeepFuseNet deep learning approach out performs the previous NVDI fused with far infrared region segmentation. Our experimental validation demonstrates an 92% reduction in false detects in our method compared to classical indices based detection. This work contributes a new technique for the fusion of O-D vision and O-D IR sensors using two deep CNN feature extractors feeding into a fully connected CNN Network (DeepFuseNet).
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Lingenfelser, Florian [Verfasser], and Elisabeth [Akademischer Betreuer] André. "From Synchronous to Asynchronous Event-driven Fusion Approaches in Multi-modal Affect Recognition / Florian Lingenfelser ; Betreuer: Elisabeth André." Augsburg : Universität Augsburg, 2018. http://d-nb.info/1168591031/34.

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Ghattas, Andrew Emile. "Medical imaging segmentation assessment via Bayesian approaches to fusion, accuracy and variability estimation with application to head and neck cancer." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5759.

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With the advancement of technology, medical imaging has become a fast growing area of research. Some imaging questions require little physician analysis, such as diagnosing a broken bone, using a 2-D X-ray image. More complicated questions, using 3-D scans, such as computerized tomography (CT), can be much more difficult to answer. For example, estimating tumor growth to evaluate malignancy; which informs whether intervention is necessary. This requires careful delineation of different structures in the image. For example, what is the tumor versus what is normal tissue; this is referred to as segmentation. Currently, the gold standard of segmentation is for a radiologist to manually trace structure edges in the 3-D image, however, this can be extremely time consuming. Additionally, manual segmentation results can differ drastically between and even within radiologists. A more reproducible, less variable, and more time efficient segmentation approach would drastically improve medical treatment. This potential, as well as the continued increase in computing power, has led to computationally intensive semiautomated segmentation algorithms. Segmentation algorithms' widespread use is limited due to difficulty in validating their performance. Fusion models, such as STAPLE, have been proposed as a way to combine multiple segmentations into a consensus ground truth; this allows for evaluation of both manual and semiautomated segmentation in relation to the consensus ground truth. Once a consensus ground truth is obtained, a multitude of approaches have been proposed for evaluating different aspects of segmentation performance; segmentation accuracy, between- and within -reader variability. The focus of this dissertation is threefold. First, a simulation based tool is introduced to allow for the validation of fusion models. The simulation properties closely follow a real dataset, in order to ensure that they mimic reality. Second, a statistical hierarchical Bayesian fusion model is proposed, in order to estimate a consensus ground truth within a robust statistical framework. The model is validated using the simulation tool and compared to other fusion models, including STAPLE. Additionally, the model is applied to real datasets and the consensus ground truth estimates are compared across different fusion models. Third, a statistical hierarchical Bayesian performance model is proposed in order to estimate segmentation method specific accuracy, between- and within -reader variability. An extensive simulation study is performed to validate the model’s parameter estimation and coverage properties. Additionally, the model is fit to a real data source and performance estimates are summarized.
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Dalvi, Rupin. "Novel approaches for multi-modal imaging and fusion in orthopaedic research for analysis of bone and joint anatomy and motion." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/15857.

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Faced with an increasingly aging and overweight population, our modern societies, particularly in the west, are set to witness a steep rise in various orthopaedic health problems in the coming decades, especially joint diseases such as arthritis. Better understanding of the way bones of the joints work is thus imperative for studying the nature and effects of these diseases and for finding cures. The data obtained from conventional sources such as skin markers and x-ray/fluoroscopy scans are generally useful but quite limited in terms of accuracy, quantification abilities and three-dimensional visualization potential. The continuous increase in the quality and versatility of various modern imaging modalities is enabling many new means for enhanced visualization and analysis of motion data of the joints. In this thesis we make use of ultrasound (US) and magnetic resonance (MR) imaging to facilitate robust, accurate and efficient analysis of the bones of joints in motion. We achieve this by obtaining motion data using 3D US with high temporal resolution which is then fused with a high spatial resolution, but static MRI volume of the same region (we mostly focus on the knee joint area). Our contributions include novel ways for improved segmentation and localization of the bones from image data. In particular, a highly effective method for improving bone segmentation in MRI volumes by enhancing the contrast at the bone-cartilage interface is proposed. Our contribution also focuses on robust and accurate registration of image data. To achieve this, a new method for stitching US bone volumes is proposed for generating larger fields of view. Further, we also present a novel method for US-MRI bone surface registration. The tools developed during the course of this thesis facilitate orthopaedic research efforts aiming to improving our understanding of the workings of the joints. The tools and methodologies proposed are versatile and expected to be applicable to other applications.
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Jensfelt, Patric. "Approaches to Mobile Robot Localization in Indoor Environments." Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3194.

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Jiao, Lianmeng. "Classification of uncertain data in the framework of belief functions : nearest-neighbor-based and rule-based approaches." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2222/document.

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Dans de nombreux problèmes de classification, les données sont intrinsèquement incertaines. Les données d’apprentissage disponibles peuvent être imprécises, incomplètes, ou même peu fiables. En outre, des connaissances spécialisées partielles qui caractérisent le problème de classification peuvent également être disponibles. Ces différents types d’incertitude posent de grands défis pour la conception de classifieurs. La théorie des fonctions de croyance fournit un cadre rigoureux et élégant pour la représentation et la combinaison d’une grande variété d’informations incertaines. Dans cette thèse, nous utilisons cette théorie pour résoudre les problèmes de classification des données incertaines sur la base de deux approches courantes, à savoir, la méthode des k plus proches voisins (kNN) et la méthode à base de règles.Pour la méthode kNN, une préoccupation est que les données d’apprentissage imprécises dans les régions où les classes de chevauchent peuvent affecter ses performances de manière importante. Une méthode d’édition a été développée dans le cadre de la théorie des fonctions de croyance pour modéliser l’information imprécise apportée par les échantillons dans les régions qui se chevauchent. Une autre considération est que, parfois, seul un ensemble de données d’apprentissage incomplet est disponible, auquel cas les performances de la méthode kNN se dégradent considérablement. Motivé par ce problème, nous avons développé une méthode de fusion efficace pour combiner un ensemble de classifieurs kNN couplés utilisant des métriques couplées apprises localement. Pour la méthode à base de règles, afin d’améliorer sa performance dans les applications complexes, nous étendons la méthode traditionnelle dans le cadre des fonctions de croyance. Nous développons un système de classification fondé sur des règles de croyance pour traiter des informations incertains dans les problèmes de classification complexes. En outre, dans certaines applications, en plus de données d’apprentissage, des connaissances expertes peuvent également être disponibles. Nous avons donc développé un système de classification hybride fondé sur des règles de croyance permettant d’utiliser ces deux types d’information pour la classification
In many classification problems, data are inherently uncertain. The available training data might be imprecise, incomplete, even unreliable. Besides, partial expert knowledge characterizing the classification problem may also be available. These different types of uncertainty bring great challenges to classifier design. The theory of belief functions provides a well-founded and elegant framework to represent and combine a large variety of uncertain information. In this thesis, we use this theory to address the uncertain data classification problems based on two popular approaches, i.e., the k-nearest neighbor rule (kNN) andrule-based classification systems. For the kNN rule, one concern is that the imprecise training data in class over lapping regions may greatly affect its performance. An evidential editing version of the kNNrule was developed based on the theory of belief functions in order to well model the imprecise information for those samples in over lapping regions. Another consideration is that, sometimes, only an incomplete training data set is available, in which case the ideal behaviors of the kNN rule degrade dramatically. Motivated by this problem, we designedan evidential fusion scheme for combining a group of pairwise kNN classifiers developed based on locally learned pairwise distance metrics.For rule-based classification systems, in order to improving their performance in complex applications, we extended the traditional fuzzy rule-based classification system in the framework of belief functions and develop a belief rule-based classification system to address uncertain information in complex classification problems. Further, considering that in some applications, apart from training data collected by sensors, partial expert knowledge can also be available, a hybrid belief rule-based classification system was developed to make use of these two types of information jointly for classification
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Books on the topic "Fusion approaches"

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From multiculturalism to hybridity: New approaches to teaching modern Switzerland. Newcastle upon Tyne, UK: Cambridge Scholars Publishing, 2010.

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The theory of fusion systems: An algebraic approach. Cambridge: Cambridge University Press, 2011.

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Hager, Gregory D. Task-directed sensor fusion and planning: A computational approach. Boston: Kluwer Academic Publishers, 1990.

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Hager, Gregory D. Task-Directed Sensor Fusion and Planning: A Computational Approach. Boston, MA: Springer US, 1990.

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Wilhide, Elizabeth. Fusion style decorating: A new approach to interior design. New York: Abbeville Press, 1999.

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Ghosh, Ranjan. Romancing theory, riding interpretation: (in)fusion approach and Salman Rushdie. New York: Peter Lang, 2012.

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Manyika, J. Data fusion and sensor management: A decentralized information-theoretic approach. New York: Ellis Horwood, 1994.

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Castellanos, José A. Mobile Robot Localization and Map Building: A Multisensor Fusion Approach. Boston, MA: Springer US, 1999.

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Romancing theory, riding interpretation: (in)fusion approach and Salman Rushdie. New York: Peter Lang, 2012.

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Harris, Chris. Adaptive Modelling, Estimation and Fusion from Data: A Neurofuzzy Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.

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Book chapters on the topic "Fusion approaches"

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Holman, Paul J., Blake Staub, and Matthew McLaurin. "Spinal Fusion." In Emergency Approaches to Neurosurgical Conditions, 175–80. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10693-9_16.

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Cotler, Howard B., and Michael G. Kaldis. "Anatomy and Surgical Approaches of the Spine." In Spinal Fusion, 89–124. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3272-8_7.

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Varshney, Pramod K. "Multisensor Data Fusion." In Intelligent Problem Solving. Methodologies and Approaches, 1–3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45049-1_1.

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Taubert, Jan, and Jacob Köhler. "Molecular Information Fusion in Ondex." In Approaches in Integrative Bioinformatics, 131–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41281-3_5.

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Blasch, Erik, Chun Yang, Jesús García, Lauro Snidaro, and James Llinas. "Contextual Tracking Approaches in Information Fusion." In Context-Enhanced Information Fusion, 73–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28971-7_4.

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Simpson, Andrew K., Peter G. Whang, and Jonathan N. Grauer. "Biological Approaches to Spinal Fusion." In Musculoskeletal Tissue Regeneration, 247–58. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-239-7_12.

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Reinders, L. J. "Non-mainstream Approaches to Fusion." In The Fairy Tale of Nuclear Fusion, 371–403. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64344-7_14.

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Olaru, Cristina, and Louis Wehenkel. "On neurofuzzy and fuzzy decision tree approaches." In Information, Uncertainty and Fusion, 131–45. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5209-3_10.

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Sumeet and K. G. Mukerji. "Protoplast Fusion in Disease Control." In Biotechnological Approaches in Biocontrol of Plant Pathogens, 177–96. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4745-7_9.

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Yim, J., S. S. Udpa, L. Udpa, M. Mina, and W. Lord. "Neural Network Approaches to Data Fusion." In Review of Progress in Quantitative Nondestructive Evaluation, 819–26. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1987-4_102.

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Conference papers on the topic "Fusion approaches"

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"SURVEY OF ESTIMATE FUSION APPROACHES." In 7th International Conference on Informatics in Control, Automation and Robotics. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002947201910196.

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Carthel, Craig, Jordan LeNoach, Stefano Coraluppi, Alan Willsky, and Brandon Bale. "Analysis of MHT and GBT Approaches to Disparate-Sensor Fusion." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190256.

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Dahire, Sonam, Yongming Liu, and Yang Jiao. "Probabilistic pipe strength and toughness estimation through information fusion with Bayesian updating." In 19th AIAA Non-Deterministic Approaches Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0592.

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Thormann, Kolja, Shishan Yang, and Marcus Baum. "A Comparison of Kalman Filter-based Approaches for Elliptic Extended Object Tracking." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190375.

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Zhu, Yifei, Alanna C. Green, Lingzhong Guo, Holly R. Evans, and Lyudmila Mihaylova. "Machine Learning Approaches for Cancer Bone Segmentation from Micro Computed Tomography Images." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190495.

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Lohan, Elena Simona, Jukka Talvitie, and Gonzalo-Seco Granados. "Data fusion approaches for WiFi fingerprinting." In 2016 International Conference on Localization and GNSS (ICL-GNSS). IEEE, 2016. http://dx.doi.org/10.1109/icl-gnss.2016.7533847.

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Marfella, Luca, Emanuela Marasco, and Carlo Sansone. "Liveness-based fusion approaches in multibiometrics." In 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS). IEEE, 2012. http://dx.doi.org/10.1109/bioms.2012.6345779.

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Ding, Jiankun, Deqiang Han, and Yi Yang. "Novel instant-runoff ranking fusion approaches." In 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2015. http://dx.doi.org/10.1109/mfi.2015.7295813.

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Dinklage, A. "Integrated Approaches in Fusion Data Analysis." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835196.

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Ulicny, B., C. J. Matheus, G. M. Powell, and M. M. Kokar. "Current approaches to automated information evaluation and their applicability to Priority Intelligence Requirement answering." In 2010 13th International Conference on Information Fusion (FUSION 2010). IEEE, 2010. http://dx.doi.org/10.1109/icif.2010.5711861.

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Reports on the topic "Fusion approaches"

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Bourque, R. F., and K. R. Schultz. Innovative approaches to inertial confinement fusion reactors: Final report. Office of Scientific and Technical Information (OSTI), November 1986. http://dx.doi.org/10.2172/6574904.

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Finley, Patrick D., Drew Levin, Tatiana Paz Flanagan, Walter E. Beyeler, Michael David Mitchell, Jaideep Ray, Melanie Moses, and Stephanie Forrest. Biologically inspired approaches for biosurveillance anomaly detection and data fusion. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1489542.

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Hayes, Daniel. Model-Data Fusion Approaches for Retrospective and Predictive Assessment of the Pan-Arctic Scale Permafrost Carbon Feedback to Global Climate. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1494028.

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Keith Rule, Erik Perry, Jim Chrzanowski, Mike Viola, and Ron Strykowsky. The Innovations, Technology and Waste Management Approaches to Safely Package and Transport the World's First Radioactive Fusion Research Reactor for Burial. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/815096.

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Bright, Gerald D., Robert S. Mandry, and Mark D. Barnell. Innovative Approach to Fusion Testbed to Integrate Multiple Sensor Data. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada299800.

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Allen, Doug, Bart Smith, Norman Morris, Charles Bjork, and John Rushing. Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada357702.

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Bleasdale, S. A., T. L. Burr, J. C. Scovel, and R. B. Strittmatter. Knowledge fusion: An approach to time series model selection followed by pattern recognition. Office of Scientific and Technical Information (OSTI), March 1996. http://dx.doi.org/10.2172/219426.

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Odette, G. R. An integrated approach to assessing the fracture safe margins of fusion reactor structures. Office of Scientific and Technical Information (OSTI), October 1996. http://dx.doi.org/10.2172/414889.

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Miller, Eric L., and Alan S. Willsky. A Multiscale Approach to Sensor Fusion and the Solution of Linear Inverse Problems. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada458527.

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Whitney, Paul D., Christian Posse, and Xingye C. Lei. Towards a Unified Approach to Information Integration - A review paper on data/information fusion. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/881949.

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