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SPE Annual Technical conference and Exhibition 2019, ATCE 2019

作者： Unal, Ebru Siddiqui, Fahd Rezaei, Ali Eltaleb, Ibrahim Kabir, Shah Soliman, Mohamed Y. Dindoruk, Birol University of Houston United States Shell International Exploration and Production Inc.

ISBN: (纸本)9781613996638

Inter-well connectivity (IWC) is one of the most significant properties when evaluating the success of a waterflood. This connectivity has been obtained from various physics-based methods such as simulations, tracers and using heuristics and semi-analytical tools like capacitance-resistance model (CRM). Production and injection data are a key piece of information required to compute the IWC. In this study, we present a new method for estimating IWC using signal processing techniques on the wavelet transform of the injection and production rate data. First, the injection and production rates are subjected to multiresolution analysis using the wavelet transform to determine the detail coefficients. The variance of the detail coefficients is then computed and is ready to be processed using various signal processing techniques. Signal processing techniques such as cross-correlation, time lag, Spearman correlation, and Kendal correlation are used to identify the level of relationship between the processed injection and production data in wavelet scale space. Based on the correlation coefficients, a new IWC link parameter is proposed for characterizing the IWC between well pairs. The IWC link parameters between well pairs are then plotted for visual representation. We created several simulation models for multi-well systems, established water-flood patterns, and for randomly placed wells to establish the new IWC link parameter. The resulting injection and production rates were analyzed using the methodology above and the new IWC link parameter is established in terms of cross-correlation coefficient. We also performed several simulations for a heterogenous reservoir to compute and compare the accuracy of the new IWC link parameter. Finally, the methodology is subjected to real field waterflooding, and compared against the CRM results, which shows a good agreement. The visual representation gives new insight into whether the connectivity is being affected by the reservoir

关键词： Heuristic methods

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A Surface-Theoretic Approach for Statistical Shape Modeling 22nd

A Surface-Theoretic Approach for Statistical Shape Modeling

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10th International Workshop on Machine Learning in Medical Imaging (MLMI) / 22nd International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Ambellan, Felix Zachow, Stefan von Tycowicz, Christoph Zuse Inst Berlin Visual Data Anal Berlin Germany

ISBN: (纸本)9783030322519;9783030322502

We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.

关键词： Statistical shape analysis Principal Geodesic analysis Lie groups Classification Manifold valued statistics

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A Framework for Sedimentological data Structure Extraction in Gold Mining Facies Delineation

A Framework for Sedimentological Data Structure Extraction i...

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2019 IEEE Asia-Pacific conference on Computer Science and data Engineering, CSDE 2019

作者： Khoza, Phumlani Nhlanganiso University of Witwatersrand Scilinx Research School of Computer Science Applied Mathematics Johannesburg South Africa

ISBN: (纸本)9781728163031

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies. © 2019 IEEE.

关键词： Quality control

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visual analysis and exploration of COVID-19 Based on Multi-source Heterogeneous data

Visual Analysis and Exploration of COVID-19 Based on Multi-s...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yun Zhou Hu He Jieqi Rong Yun Cheng YongChang Li Wei Zhong Fu Jiang Hu Nan University of Finance and Economics Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

The novel coronavirus pneumonia (COVID-19) has raged in many countries around the world. In the process of fighting against the COVID-19, unprecedented large-scale epidemic data have been produced such as case data, spatio-temporal data, public opinion data and so on. The increasingly complex data poses a significant challenge to understand. A two-level interactive visualization system named COVID-19Vis is proposed in this paper, which collects epidemic data from multiple sources and provides an interactive mode of multi-graph linkage. Users can not only easily analyze and interpret the spatial-temporal characteristics and potential rules of the epidemic, but also find the relationship between policy, online public opinion and the development of the epidemic situation. Through a large number of visualization effects and user feedback, the effectiveness and practicability of the COVID-19Vis are further verified.

关键词： COVID-19 Epidemics visualization Market research data visualization Hospitals Urban areas

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HiPiler: visual exploration of Large Genome Interaction Matrices with Interactive Small Multiples

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 522-531页

作者： Lekschas, Fritz Bach, Benjamin Kerpedjiev, Peter Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Cambridge MA 02138 USA Harvard Med Sch Boston MA USA

This paper presents an interactive visualization interface HiPiler for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROls) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROls in such large matrices. In HiPiler, ROls are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

关键词： Interactive Small Multiples Matrix Comparison Biomedical visualization Genomics

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Packed-Memory Quadtree: A cache-oblivious data structure for visual exploration of streaming spatiotemporal big data

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COMPUTERS & GRAPHICS-UK 2018年 76卷 117-128页

作者： Toss, Julio Pahins, Cicero A. L. Raffin, Bruno Comba, Joao L. D. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Grenoble Alpes Inria CNRS Grenoble INPLIG F-38000 Grenoble France

The visual analysis of large multidimensional spatiotemporal datasets poses challenging questions regarding storage requirements and query performance. Several data structures have recently been proposed to address these problems that rely on indexes that pre-compute different aggregations from a known-a-priori dataset. Consider now the problem of handling streaming datasets, in which data arrive as one or more continuous data streams. Such datasets introduce challenges to the data structure, which now has to support dynamic updates (insertions/deletions) and rebalancing operations to perform self reorganizations. In this work, we present the Packed-Memory Quadtree (PMQ), a novel data structure designed to support visual exploration of streaming spatiotemporal datasets. PMQ is cache-oblivious to perform well under different cache configurations. We store streaming data in an internal index that keeps a spatiotemporal ordering over the data following a quadtree representation, with support for real-time insertions and deletions. We validate our data structure under different dynamic scenarios and compare to competing strategies. We demonstrate how PMQ could be used to answer different types of visual spatiotemporal range queries of streaming datasets. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Spatiotemporal datasets Streaming Real-time exploration Range queries

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visual analysis of retinal changes with optical coherence tomography

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visual COMPUTER 2018年第9期34卷 1209-1224页

作者： Roehlig, Martin Schmidt, Christoph Prakasam, Ruby Kala Rosenthal, Paul Schumann, Heidrun Stachs, Oliver Univ Rostock Inst Comp Sci Rostock Germany Univ Rostock Dept Ophthalmol Rostock Germany

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in detecting a wide range of ocular diseases. Despite the diagnostic value of OCT, managing and analyzing resulting data is challenging. We apply two visual analysis strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, emphasizing, selecting, and comparing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of retinal OCT data for assisting medical diagnoses. Domain experts applied our solution successfully to study early retinal changes in patients suffering from type 1 diabetes mellitus.

关键词： visual analysis Optical coherence tomography OCT data Ophthalmology Retina

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Sketch-aided retrieval of incomplete 3D cultural heritage objects 12

Sketch-aided retrieval of incomplete 3D cultural heritage ob...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual conference of the European Association for Computer Graphics, EG 2019

作者： Lengauer, S. Komar, A. Labrada, A. Karl, S. Trinkl, E. Preiner, R. Bustos, B. Schreck, T. Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria Institute of Archaeology University of Graz Austria Millenium Institute for Foundational Research on Data Department of Computer Science University of Chile Chile

ISBN: (纸本)9783038680772

Due to advances in digitization technology, documentation efforts and digital library systems, increasingly large collections of visual Cultural Heritage (CH) object data becomes available, offering rich opportunities for domain analysis, e.g., for comparing, tracing and studying objects created over time. In principle, existing shape- and image-based similarity search methods can aid such domain analysis tasks. However, in practice, visual object data are given in different modalities, including 2D, 3D, sketches or conventional drawings like profile sections or unwrappings. In addition, collections may be distributed across different publications and repositories, posing a challenge for implementing encompassing search and analysis systems. We introduce a methodology and system for cross-modal visual search in CH object data. Specifically, we propose a new query modality based on 3D views enhanced by user sketches (3D+sketch). This allows for adding new context to the search, which is useful e.g., for searching based on incomplete query objects, or for testing hypotheses on existence of certain shapes in a collection. We present an appropriately designed workflow for constructing query views from incomplete 3D objects enhanced by a user sketch based on shape completion and texture inpainting. visual cues additionally help users compare retrieved objects with the query. We apply our method on a set of relevant 3D and view-based CH object data, demonstrating the feasibility of our approach and its potential to support analysis of domain experts in Archaeology and the field of CH in general. © 2019 The Author(s)

关键词： Textures

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"Hear Us Speak" - Listening To Women'S Experiences Of Perinatal Distress And The Transactional analysis Psychotherapy Treatment They Received

"Hear Us Speak" - Listening To Women'S Experiences Of Perina...

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作者： E Haynes University of Salford

学位级别：博士

This PhD study examines Transactional analysis (TA) psychotherapy as a treatment for perinatal mental illness, by listening to the experiences of women who have received it within a private practice, psychotherapeutic setting. The study also addresses the gap in research into womenâs experiences of perinatal mental illness. In this study, âperinatal mental illnessâ includes stress, anxiety, depression and puerperal psychosis. Perinatal mental illness is common and has wide-reaching consequences. Historically, research has focused on postnatal depression. However, many women become unwell in pregnancy, and research shows that stress and anxiety could be more prevalent than depression. Significant barriers to treatment are stigma and shame, causing women to silence themselves as a consequence. Engaging women into treatment and subsequent compliancy is difficult. Conventional treatment, of antidepressants, is controversial due to the risk of medication in utero and breast milk. Women say they would prefer psychological therapy, yet there is little research into suitable types. TA psychotherapy offers a plausible, diverse and versatile treatment, which can be shaped towards the client needs, an important factor in perinatal mental illness. A qualitative, narrative approach has been used to facilitate an exploration of womenâs experiences. data collection was by Free Association Narrative Interviewing and a creative methodology, forming a deeper, richer understanding of personal perspective, using the visual and the narrative as a co-creative construction. data analysis used The Listening Guide Method to highlight difference and hear the multiple voices in one personâs narrative, as well as taking notice of temporal movement within the narrative. Key findings: i) TA psychotherapy was found to be a useful and treatment for the five participants; ii) TA addressed the specificity of illness; iii) TA was found to be a durable treatment; iv) the relational dyad was

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Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving

Imitative Reinforcement Learning Fusing Vision and Pure Purs...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Mingxing Peng Zhihao Gong Chen Sun Long Chen Dongpu Cao School of Data and Computer Science Sun Yat-sen University China Waterloo CogDrive Lab University of Waterloo Canada

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments and terrible weather conditions. In this paper, we propose a two-stage framework, called IPP-RL, to handle these problems. IPP means an Imitation learning method fusing visual information with the additional steering angle calculated by Pure-Pursuit (PP) method, and RL means using Reinforcement Learning for further training. In our IPP model, the visual information captured by camera can be compensated by the calculated steering angle, thus it could perform well under bad weather conditions. However, imitation learning performance is limited by the driving data severely. Thus we use a reinforcement learning method-Deep Deterministic Policy Gradient (DDPG)-in the second stage training, which shares the learned weights from pretrained IPP model. In this way, our IPP-RL can lower the dependency of imitation learning on demonstration data and solve the problem of low exploration efficiency caused by randomly initialized weights in reinforcement learning. Moreover, we design a more reasonable reward function and use the n-step return to update the critic-network in DDPG. Our experiments on CARLA driving benchmark demonstrate that our IPP-RL is robust to lousy weather conditions and shows remarkable generalization capability in unknown environments on navigation task.

关键词： Learning (artificial intelligence) Meteorology Robustness Task analysis Navigation Training Autonomous vehicles

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