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Well log analysis and geochemical data to identify source rock and hydrocarbon reservoir: Northeast Java Basin study case

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AIP conference Proceedings 2020年第1期2245卷

作者： Tri Nopiyanti Tumpal B. Nainggolan Ordas Dewato Dyna Auliya Haq 1Universitas Lampung Jl Prof. Dr. Sumantri Brojonegoro No.1 Bandar Lampung 35145 Indonesia 2Marine Geological Institute - Kementerian ESDM Indonesia

Comprehensive characterizarion methods are carried out to determine accurate source rock and reservoir identification. Geochemical data has become a critical part of recent unconventional exploration and development. However, due to high cost of geological core extraction and analysis, geophysical wireline logging tools have become the primary source of downhole measurement of geomechanical properties. This study covers an integrated approach at defining geochemical report derived from geological core extraction and analysis and its relationship with geophysical wireline logs of 5 (five) wells at Northeast Java Basin. Geophysical wireline logs can be utilized to identify reservoir and source rock intervals in the early stage of well drilling. However, the well logs that directly measure the hidrogen content of the kerogen do no exist. Consequently, it is utilized for source rock evaluations and calculation of Total Organic Carbon (TOC) which are most commonly include sonic, density, gamma ray, neutron, and resistivity. The Van Krevelen diagram has been applied to all 5 (five) wells that indicates 2 (two) of them have potential gas – kerogen type III/iv with marginally mature to mature source rock. The integration of well logs and geochemical data greatly improves the accuracy and understanding of the controls of reservoir quality and source rock. It can be used for further step of knowing basin potential and its prospect level.

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

4th International Conference of Pioneer Computer Scientists,...

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

ISBN: (纸本)9789811322020

The proceedings contain 116 papers. The special focus in this conference is on Pioneer Computer Scientists, Engineers and Educators. The topics include: The construction approach of statutes database;weighted clustering coefficients based feature extraction and selection for collaboration relation prediction;a representation-based pseudo nearest neighbor classifier;Research on network intrusion data based on KNN and feature extraction algorithm;PSHCAR: A position-irrelevant scene-aware human complex activities recognizing algorithm on mobile phones;visual-based character embedding via principal component analysis;a novel experience-based exploration method for q-learning;overlapping community detection based on community connection similarity of maximum clique;heterogeneous network community detection algorithm based on maximum bipartite clique;the competence of volunteer computing for mapreduce big data applications;novel algorithm for mining frequent patterns of moving objects based on dictionary tree improvement;malCommunity: A graph-based evaluation model for malware family clustering;negative influence maximization in social networks;mining correlation relationship of users from trajectory data;context-aware network embedding via variation autoencoders for link prediction;SFSC: Segment feature sampling classifier for time series classification;Fuzzy C-mean clustering based: LEO satellite handover;an improved apriori algorithm based on matrix and double correlation profit constraint;mining and ranking important nodes in complex network by K-shell and degree difference;representation learning for knowledge graph with dynamic step;research on the security protection scheme for container-based cloud platform node based on blockchain technology;an improved K-means parallel algorithm based on cloud computing;statistical learning-based prediction of execution time of data-intensive program under hadoop2.0;E-CAT: Evaluating crowdsourced android testing.

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

4th International Conference of Pioneer Computer Scientists,...

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4th International conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

ISBN: (纸本)9789811322051

The proceedings contain 54 papers. The special focus in this conference is on Pioneer Computer Scientists, Engineers and Educators. The topics include: The construction approach of statutes database;weighted clustering coefficients based feature extraction and selection for collaboration relation prediction;a representation-based pseudo nearest neighbor classifier;Research on network intrusion data based on KNN and feature extraction algorithm;PSHCAR: A position-irrelevant scene-aware human complex activities recognizing algorithm on mobile phones;visual-based character embedding via principal component analysis;a novel experience-based exploration method for q-learning;overlapping community detection based on community connection similarity of maximum clique;heterogeneous network community detection algorithm based on maximum bipartite clique;the competence of volunteer computing for mapreduce big data applications;novel algorithm for mining frequent patterns of moving objects based on dictionary tree improvement;malCommunity: A graph-based evaluation model for malware family clustering;negative influence maximization in social networks;mining correlation relationship of users from trajectory data;context-aware network embedding via variation autoencoders for link prediction;SFSC: Segment feature sampling classifier for time series classification;Fuzzy C-mean clustering based: LEO satellite handover;an improved apriori algorithm based on matrix and double correlation profit constraint;mining and ranking important nodes in complex network by K-shell and degree difference;representation learning for knowledge graph with dynamic step;research on the security protection scheme for container-based cloud platform node based on blockchain technology;an improved K-means parallel algorithm based on cloud computing;statistical learning-based prediction of execution time of data-intensive program under hadoop2.0;E-CAT: Evaluating crowdsourced android testing.

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visualizing Dynamic Gene Interactions to Reverse Engineer Gene Regulatory Networks using Topological data analysis 21

Visualizing Dynamic Gene Interactions to Reverse Engineer Ge...

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21st International conference on Information visualisation (iv)

作者： Perkins, Miriam Daniels, Karen Univ Massachusetts Lowell Lowell MA 01854 USA

ISBN: (纸本)9781538608319

This research uses visual analytics to better understand genome dynamics, through a novel application of topological data analysis (TDA) to time series gene expression data. TDA is a model-free approach in which relations are obtained directly from the data. We build the dynamics of the system into the topology, then calculate the influence of potential regulatory genes over the expression of other genes. An interactive 3D visualization is provided to aid in the discovery of functional relationships. These capabilities are contained in a new R package. We apply our technique to synthetic data from the DREAM4 gene regulatory network inference challenge and compare our results to both the challenge submissions and those produced by networkBMA, a Bioconductor package designed to work with time series gene expression data. A case study is presented detailing the use of the visual analytics tool.

关键词： visualization Gene regulatory network Topological data analysis

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visual Integration of Meteorological and Sensor data for Identifying Suspicious Company Behavior

Visual Integration of Meteorological and Sensor Data for Ide...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Seebacher, Daniel Schneider, Bruno Behrisch, Michael Univ Konstanz Constance Germany Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781538631638

We present an approach developed in course of the VAST 2017 Mini-Challenge 2. To help the ornithologist Mitch to investigate the noxious gases emitted by the four companies south of the nature preserve, we employ a combination of interactive visualizations that allow for an exploration of the data. In this paper, we present our visual-interactive approach for analyzing suspicious patterns in the data. By taking the wind data into consideration, as well, our approach allows the retrieval of patterns in the chemical releases and identify key polluters.

关键词： Chemicals Monitoring Companies Wind speed visualization Task analysis Time series analysis

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DeepPhase: Surgical Phase Recognition in CATARACTS Videos 21st

DeepPhase: Surgical Phase Recognition in CATARACTS Videos

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21st International conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th Eurographics Workshop on visual Computing for Biology and Medicine (VCBM) / International Workshop on Computational Diffusion MRI (CDMRI)

作者： Zisimopoulos, Odysseas Flouty, Evangello Luengo, Imanol Giataganas, Petros Nehme, Jean Chow, Andre Stoyanov, Danail Kinosis Ltd Digital Surg 230 City Rd London EC1V 2QY England UCL Gower St London WC1E 6BT England

ISBN: (纸本)9783030009373;9783030009366

Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance postsurgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments' recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.

关键词： Surgical vision Instrument detection Surgical workflow Deep learning Surgical data science

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visual Analytic Design for Characterizing Air-Sampling Sensor Performance and Operation

Visual Analytic Design for Characterizing Air-Sampling Senso...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Quadri, Ghulam Jilani Tuladhar, Anwesh Malla, Sulav Rosen, Paul Univ S Florida Dept Comp Engn Tampa FL 33620 USA

ISBN: (纸本)9781538631638

analysis and exploration of similar continuous data for various air-sampler sensor have been performed by developing a processing tool. The analysis and design for characterizing sensor data have been stated and described. Continuous 24X7 sensor data and metrological data leads the layout choices for the analytical design. Such design choices are helpful to understand the pattern of similar reading for various devices at a time. In this paper, we describe the use of the mentioned design choices for to identify pattern, unusual behavior. We used this tool and design choice to solve VAST 2017 mini challenge 2.

关键词： Air-sampler vector dot-product multi-label data multi-variate data

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A visual Analytics Approach for Word Relevances in Multiple Texts 21

A Visual Analytics Approach for Word Relevances in Multiple ...

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21st International conference on Information visualisation (iv)

作者： Rodrigues, Nils Burch, Michael Di Silvestro, Lorenzo Weiskopf, Daniel Univ Stuttgart VISUS Stuttgart Germany Univ Catania DMI Catania Italy

ISBN: (纸本)9781538608319

We investigate the problem of analyzing word frequencies in multiple text sources with the aim to give an overview of word-based similarities in several texts as a starting point for further analysis. To reach this goal, we designed a visual analytics approach composed of typical stages and processes, combining algorithmic analysis, visualization techniques, the human users with their perceptual abilities, as well as interaction methods for both the data analysis and the visualization component. By our algorithmic analysis, we first generate a multivariate dataset where words build the cases and the individual text sources the attributes. Real-valued relevances express the significances of each word in each of the text sources. From the visualization perspective, we describe how this multivariate dataset can be visualized to generate, confirm, rebuild, refine, or reject hypotheses with the goal to derive meaning, knowledge, and insights from several text sources. We discuss benefits and drawbacks of the visualization approaches when analyzing word relevances in multiple texts.

关键词： data visualization Correlation visual analytics Algorithm design and analysis Layout

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visual analysis of Confocal Raman Spectroscopy data using Cascaded Transfer Function Design

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COMPUTER GRAPHICS FORUM 2017年第3期36卷 239-249页

作者： Schikora, Christoph M. Plack, Markus Bornemann, Rainer Bolivar, Peter Haring Kolb, Andreas Univ Siegen Comp Graph & Multimedia Syst Siegen Germany Univ Siegen High Frequency & Quantum Elect Siegen Germany

2D Confocal Raman Microscopy (CRM) data consist of high dimensional per-pixel spectral data of 1000 bands and allows for complex spectral and spatial-spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi-variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a-priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material-related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi-automatic, cascaded, 1D and 2D multidimensional transfer functions (MD-TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD-TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.

关键词： Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Display algorithm I.4.10 [Image Processing and Computer Vision]: Multidimensional I.5.3 [Pattern Recognition]: Algorithms

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Temporal Pattern analysis and Source Detection through visual analysis on Multi-Dimensional Time Series data

Temporal Pattern Analysis and Source Detection through Visua...

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IEEE conference on visual Analytics Science and Technology (VAST)

作者： Jiang, Ruike Huang, Wei Ma, Nan Hong, Fan Zhao, Ying Yuan, Xiaoru Peking Univ Key Lab Machine Percept Minist Educ Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Peking Univ Beijing Engn Technol Res Ctr Virtual Simulat & Vi Beijing Peoples R China QIHOO 360 Beijing Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

In VAST Challenge 2017, we developed a visual exploration system for detection of sensor anomaly, pattern of chemical distribution and responsible factory for each release of chemical. In this report, we discuss detai... 详细信息

ISBN: (纸本)9781538631638

关键词： Chemicals Production facilities visualization Time series analysis data preprocessing Monitoring ISO

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