Academic literature on the topic 'Robust hierarchical ensemble'
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Journal articles on the topic "Robust hierarchical ensemble"
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Myrseth, Inge. "Hierarchical Ensemble Kalman Filter." SPE Journal 15, no. 02 (March 11, 2010): 569–80. http://dx.doi.org/10.2118/125851-pa.
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Summary This paper presents the hierarchical ensemble Kalman filter (HEnKF) as a robust extension of the ensemble Kalman filter (EnKF). The HEnKF is developed to be robust against features like estimation uncertainty and rank deficiency related to covariance estimation in EnKF. The HEnKF imposes a hierarchical model on the state variables and uses prior distributions from the Gauss conjugate family of distributions to obtain more-robust estimates. An empirical study demonstrates that the HEnKF provides more-reliable results than the traditional EnKF approach. Better predictions and more-realistic prediction intervals are provided. The latter is caused by model-parameter uncertainty being an integral part of the HEnKF approach, while this effect is ignored in traditional EnKF. The two versions of the ensemble Kalman filter are also compared on a synthetic-reservoir study. The HEnKF appears as significantly better.
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Li, Shengjie, Shuai Zhao, Bo Cheng, Erhu Zhao, and Junliang Chen. "Robust Visual Tracking via Hierarchical Particle Filter and Ensemble Deep Features." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 1 (January 2020): 179–91. http://dx.doi.org/10.1109/tcsvt.2018.2889457.
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Ding, Weiping, Jiehua Wang, and Jiandong Wang. "A hierarchical-coevolutionary-MapReduce-based knowledge reduction algorithm with robust ensemble Pareto equilibrium." Information Sciences 342 (May 2016): 153–75. http://dx.doi.org/10.1016/j.ins.2016.01.035.
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Sun, Yuqin, Songlei Wang, Dongmei Huang, Yuan Sun, Anduo Hu, and Jinzhong Sun. "A multiple hierarchical clustering ensemble algorithm to recognize clusters arbitrarily shaped." Intelligent Data Analysis 26, no. 5 (September 5, 2022): 1211–28. http://dx.doi.org/10.3233/ida-216112.
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As a research hotspot in ensemble learning, clustering ensemble obtains robust and highly accurate algorithms by integrating multiple basic clustering algorithms. Most of the existing clustering ensemble algorithms take the linear clustering algorithms as the base clusterings. As a typical unsupervised learning technique, clustering algorithms have difficulties properly defining the accuracy of the findings, making it difficult to significantly enhance the performance of the final algorithm. AGglomerative NESting method is used to build base clusters in this article, and an integration strategy for integrating multiple AGglomerative NESting clusterings is proposed. The algorithm has three main steps: evaluating the credibility of labels, producing multiple base clusters, and constructing the relation among clusters. The proposed algorithm builds on the original advantages of AGglomerative NESting and further compensates for the inability to identify arbitrarily shaped clusters. It can establish the proposed algorithm’s superiority in terms of clustering performance by comparing the proposed algorithm’s clustering performance to that of existing clustering algorithms on different datasets.
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Yang, Cheng, Minjie Shi, Xuefeng Song, Xiaofeng Zhao, Liping Zhao, Jing Liu, Peng Zhang, and Lian Gao. "A robust hierarchical microcapsule for efficient supercapacitors exhibiting an ultrahigh current density of 300 A g−1." Journal of Materials Chemistry A 6, no. 14 (2018): 5724–32. http://dx.doi.org/10.1039/c8ta00255j.
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A unique three dimensional (3D) hierarchical microcapsule structure (NiSx@NCV) has been put forward, which is realized by the ensemble of N-doped carbon vesicles encapsulating dual-NiSx (α-NiS/NiS2) nanoparticles via an in situ nanospace-confined pyrolysis strategy.
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Selicato, Laura, Flavia Esposito, Grazia Gargano, Maria Carmela Vegliante, Giuseppina Opinto, Gian Maria Zaccaria, Sabino Ciavarella, Attilio Guarini, and Nicoletta Del Buono. "A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices." Mathematics 9, no. 8 (April 16, 2021): 882. http://dx.doi.org/10.3390/math9080882.
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One of the main problems in the analysis of real data is often related to the presence of anomalies. Namely, anomalous cases can both spoil the resulting analysis and contain valuable information at the same time. In both cases, the ability to detect these occurrences is very important. In the biomedical field, a correct identification of outliers could allow the development of new biological hypotheses that are not considered when looking at experimental biological data. In this work, we address the problem of detecting outliers in gene expression data, focusing on microarray analysis. We propose an ensemble approach for detecting anomalies in gene expression matrices based on the use of Hierarchical Clustering and Robust Principal Component Analysis, which allows us to derive a novel pseudo-mathematical classification of anomalies.
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Jo, Honggeun, Javier E. Santos, and Michael J. Pyrcz. "Conditioning well data to rule-based lobe model by machine learning with a generative adversarial network." Energy Exploration & Exploitation 38, no. 6 (July 14, 2020): 2558–78. http://dx.doi.org/10.1177/0144598720937524.
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Rule-based reservoir modeling methods integrate geological depositional process concepts to generate reservoir models that capture realistic geologic features for improved subsurface predictions and uncertainty models to support development decision making. However, the robust and direct conditioning of these models to subsurface data, such as well logs, core descriptions, and seismic inversions and interpretations, remains as an obstacle for the broad application as a standard subsurface modeling technology. We implement a machine learning-based method for fast and flexible data conditioning of rule-based models. This study builds on a rule-based modeling method for deep-water lobe reservoirs. The model has three geological inputs: (1) the depositional element geometry, (2) the compositional exponent for element stacking pattern, and (3) the distribution of petrophysical properties with hierarchical trends conformable to the surfaces. A deep learning-based workflow is proposed for robust and non-iterative data conditioning. First, a generative adversarial network learns salient geometric features from the ensemble of the training rule-based models. Then, a new rule-based model is generated and a mask is applied to remove the model near local data along the well trajectories. Last, semantic image inpainting restores the mask with the optimum generative adversarial network realization that is consistent with both local data and the surrounding model. For the deep-water lobe example, the generative adversarial network learns the primary geological spatial features to generate reservoir realizations that reproduce hierarchical trend as well as the surface geometries and stacking pattern. Moreover, the trained generative adversarial network explores the latent reservoir manifold and identifies the ensemble of models to represent an uncertainty model. Semantic image inpainting determines the optimum replacement for the near-data mask that is consistent with the local data and the rest of the model. This work results in subsurface models that accurately reproduce reservoir heterogeneity, continuity, and spatial distribution of petrophysical parameters while honoring the local well data constraints.
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Chu, Xiao, Xianghua Tan, and Weili Zeng. "A Clustering Ensemble Method of Aircraft Trajectory Based on the Similarity Matrix." Aerospace 9, no. 5 (May 17, 2022): 269. http://dx.doi.org/10.3390/aerospace9050269.
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Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns of aircraft and air traffic flow distribution, which can provide a reference for the revision of standard flight procedures and the optimization of the division of airspace sectors. At present, most trajectory clustering uses a single clustering algorithm. When other processing remains unchanged, it is difficult to improve the clustering effect by using a single clustering method. Therefore, this paper proposes a trajectory clustering ensemble method based on a similarity matrix. Firstly, a stacked autoencoder is used to learn a small number of features that are sufficiently representative of the trajectory and used as the input to the subsequent clustering algorithm. Secondly, each basis cluster is used to cluster the data set, and then a consistent similarity matrix is obtained by using the clustering results of each basis cluster. On this basis, using the deformation of the matrix as the distance matrix between trajectories, the agglomerative hierarchical clustering algorithm is used to ensemble the results of each basis cluster. Taking the Nanjing Lukou Airport terminal area as an example, the experimental results show that integrating multiple basis clusters eliminates the inherent randomness of a single clustering algorithm, and the trajectory clustering results are more robust.
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Mohsin Siraj, M., Paul M. Van den Hof, and Jan-Dirk Jansen. "Handling Geological and Economic Uncertainties in Balancing Short-Term and Long-Term Objectives in Waterflooding Optimization." SPE Journal 22, no. 04 (May 17, 2017): 1313–25. http://dx.doi.org/10.2118/185954-pa.
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Summary Model-based economic optimization of oil production has a significant scope to increase financial life-cycle performance. The net-present-value (NPV) objective in this optimization, because of its nature, focuses on long-term gains, whereas short-term production is not explicitly addressed. At the same time, the achievable NPV is highly uncertain because of strongly varying economic conditions and limited knowledge of the reservoir-model parameters. The prime focus of this work is to develop optimization strategies that balance both long-term and short-term economic objectives and also offer robustness to the long-term NPV. An earlier robust hierarchical optimization method honoring geological uncertainty with robust long-term and short-term NPV objectives serves as a starting base of this work. We address the issue of extending this approach to include economic uncertainty and aim to analyze how the optimal solution reduces the uncertainty in the achieved average NPV. An ensemble of varying oil prices is used to model economic uncertainty with average NPVs as robust objectives in the hierarchical approach. A weighted-sum approach is used with the same objectives to quantify the effect of uncertainty. To reduce uncertainty, a mean-variance-optimization (MVO) objective is then considered to maximize the mean and also minimize the variance. A reduced effect of uncertainty on the long-term NPV is obtained compared with the uncertainty in the mean-optimization (MO) objectives. Last, it is investigated whether, because of the better handling of uncertainty in MVO, a balance between short-term and long-term gains can be naturally obtained by solving a single-objective MVO. Simulation examples show that a faster NPV buildup is naturally achieved by choosing appropriate weighting of the variance term in the MVO objective.
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Mendoza, Pablo A., Andrew W. Wood, Elizabeth Clark, Eric Rothwell, Martyn P. Clark, Bart Nijssen, Levi D. Brekke, and Jeffrey R. Arnold. "An intercomparison of approaches for improving operational seasonal streamflow forecasts." Hydrology and Earth System Sciences 21, no. 7 (July 31, 2017): 3915–35. http://dx.doi.org/10.5194/hess-21-3915-2017.
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Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.
Dissertations / Theses on the topic "Robust hierarchical ensemble"
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Wang, Ye. "Robust Text Mining in Online Social Network Context." Thesis, 2018. https://vuir.vu.edu.au/38645/.
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Text mining is involved in a broad scope of applications in diverse domains that mainly,
but not exclusively, serve political, commercial, medical and academic needs. Along
with the rapid development of the Internet technology in recent thirty years and the advent
of online social media and network in a decade, text data is obliged to entail features
of online social data streams, for example, the explosive growth, the constantly changing
content and the huge volume. As a result, text mining is no longer merely oriented to
textual content itself, but requires consideration of surroundings and combining theories
and techniques of stream processing and social network analysis, which give birth to a
wide range of applications used for understanding thoughts spread over the world , such
as sentiment analysis, mass surveillance and market prediction.
Automatically discovering sequences of words that represent appropriate themes in
a collection of documents, topic detection closely associated with document clustering
and classification. These two tasks play integral roles in revealing deep insight into the
text content in the whole text mining framework. However, most existing detection techniques
cannot adapt to the dynamic social context. This shows bottlenecks of detecting
performance and deficiencies of topic models.
In this thesis, we take aim at text data stream, investigating novel techniques and
solutions for robust text mining to tackle arising challenges associated with the online
social context by incorporating methodologies of stream processing, topic detection and
document clustering and classification. In particular, we have advanced the state-of-theart
by making the following contributions:
1. A Multi-Window based Ensemble Learning (MWEL) framework is proposed for
imbalanced streaming data that comprehensively improves the classification performance.
MWEL ensures that the ensemble classifier is maintained up to date and
adaptive to the evolving data distribution by applying a multi-window monitoring
mechanism and efficient updating strategy.
2. A semi-supervised learning method is proposed to detect latent topics from news
streams and the corresponding social context with a constraint propagation scheme
to adequately exploit the hidden geometrical structure as supervised information
in given data space. A collective learning algorithm is proposed to integrate the
textual content into the social context. A locally weighted scheme is afterwards
proposed to seek an improvement of the algorithm stability.
3. A Robust Hierarchical Ensemble (RHE) framework is introduced to enhance the
robustness of the topic model. It, on the one hand, reduces repercussions caused by outliers and noises, and on the other overcomes inherent defects of text data. RHE
adapts to the changing distribution of text stream by constructing a flexible document
hierarchy which can be dynamically adjusted. A discussion of how to extract
the most valuable social context is conducted with experiments for the purpose of
removing some noises from the surroundings and efficiency of the proposed.
Book chapters on the topic "Robust hierarchical ensemble"
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Spinelli, Stefano. "Optimal Management and Control of Smart Thermal-Energy Grids." In Special Topics in Information Technology, 15–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_2.
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AbstractThis work deals with the development of novel algorithms and methodologies for the optimal management and control of thermal and electrical energy units operating in a networked configuration. The aim of the work is to foster the creation of a smart thermal-energy grid (smart-TEG), by providing supporting tools for the modeling of subsystems and their optimal control and coordination. A hierarchical scheme is proposed to optimally address the management and control issues of the smart-TEG. Different methods are adopted to deal with the features of the specific generation units involved, e.g., multi-rate MPC approaches, or linear parameter-varying strategies for managing subsystem nonlinearity. An advanced scheme based on ensemble model is also conceived for a network of homogeneous units operating in parallel. Moreover, a distributed optimization algorithm for the high-level unit commitment problem is proposed to provide a robust, flexible and scalable scheme.
Conference papers on the topic "Robust hierarchical ensemble"
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Wang, Mengmeng, Yong Liu, and Rong Xiong. "Robust object tracking with a hierarchical ensemble framework." In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. http://dx.doi.org/10.1109/iros.2016.7759091.
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Zou, Xu, Sheng Zhong, Luxin Yan, Xiangyun Zhao, Jiahuan Zhou, and Ying Wu. "Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00023.
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Vu, Quang Hieu, Dymitr Ruta, and Ling Cen. "An ensemble model with hierarchical decomposition and aggregation for highly scalable and robust classification." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f564.
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de Hoop, Stephan, and Denis Voskov. "Fast and Robust Scheme for Uncertainty Quantification in Naturally Fractured Reservoirs." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203968-ms.
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Abstract The main objective of this study is to perform Uncertainty Quantification (UQ) using a detailed representation of fractured reservoirs. This is achieved by creating a simplified representation of the fracture network while preserving the main characteristics of the high-fidelity model. We include information at different scales in the UQ workflow which allows for a large reduction in the computational time while converging to the high-fidelity full ensemble statistics. Ultimately, it allows us to make accurate predictions on geothermal energy production in highly heterogeneous fractured porous media. The numerical reservoir simulation tool we use in this work is the Delft Advanced Research Terra Simulator (DARTS). It is based on Finite Volume approximation in space, fully coupled explicit approximation in time, and uses the novel linearization technique called Operator-Based Linearization (OBL) for the system of discretized nonlinear governing equations. We use a fracture network generation algorithm that uses distributions for length, angles, size of fracture sets, and connectivity as its main input. This allows us to generate a large number of complex fracture networks at the reservoir scale. We developed a pre-processing algorithm to simplify the fracture network and use graph theory to analyze the connectivity before and after pre-processing. Furthermore, we use metric space modeling methods for statistical analysis. A robust coarsening method for the Discrete Fracture Matrix model (DFM) is developed which allows for great control over the degree of simplification of the network’s topology and connectivity. We apply the framework to modeling of geothermal energy extraction. The pre-processing algorithm allows for a hierarchical representation of the fracture network, which in turn is utilized in the reduced UQ methodology. The reduced UQ workflow uses the coarser representation of the fracture networks to partition/rank the high-fidelity parameter space. Then a small subset of high-fidelity models is chosen to represent the full ensemble statistics. Hereby, the computational time of the UQ is reduced by two/three orders of magnitude, while converging to similar statistics as the high-fidelity full ensemble statistics. The methods developed in this study are part of a larger project on a prediction of energy production from carboniferous carbonates. The final goal is to perform UQ in pre-salt reservoirs which are characterized by complex reservoir architecture (i.e., large karstification and fracture networks). The UQ of fractured reservoirs is typically done in the petroleum industry using effective media models. We present a methodology that can efficiently handle a large ensemble of DFM models, which represent complex fracture networks and allow for making decisions under uncertainty using more detailed high-resolution numerical models.