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Genetic Algorithm with Efficient Selection Using Pareto Front Modeling and data Envelopment analysis

Genetic Algorithm with Efficient Selection Using Pareto Fron...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Mamoru Doi Kenya Sugihara Masao Arakawa Mitsubishi Electric Corporation Kanagawa Japan Graduate School of Information Production and Systems Waseda University Fukuoka Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

There exist various multi-objective optimization problems(MOPs) in the real world that require acquiring diverse solutions efficiently. Various multi-objective evolutionary algorithms (MOEAs) have been proposed to solve MOPs. Generally, in MOEAs, the same score criterion is conducted in Survival and Tournament Selection. In Survival, parent selection score should be conducted to ensure convergence and diversity, and in Tournament Selection, the selection score of individuals for crossover should be conducted to select individuals with high convergence compared to other individuals. Therefore, evolutionary computation algorithms are expected to perform efficient exploration while maintaining diversity. In this work, we propose an efficient selection algorithm using data envelopment analysis and pareto front modeling. In Survival, we perform an evaluation using existing methods to ensure high convergence and diversity, and in Tournament Selection, we use the data envelopment analysis method to select individuals with high convergence compared to other individuals. This allows us to balance both high convergence and diversity. Moreover, by implementing Pareto front modeling, we were also successful in addressing the issue of assuming convexity in the data envelopment analysis method. In comparison using the HV METRIC on the WFG and DTLZ benchmark functions, the proposed method showed superior results compared to the existing methods of NSGA-ii, AGE-MOEA-ii, and DEA-GA with the exception of 9-objective functions.

关键词： Analytical models Shape Diversity reception Evolutionary computation Benchmark testing data models Newton method Optimization Convergence data envelopment analysis

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Classification and Evaluation of Reservoir Pore Structures Based on Petrophysical Facies: A Case Study on Formation KC in KX Field, Caspian Basin 11th

Classification and Evaluation of Reservoir Pore Structures B...

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11th International Field exploration and Development conference, IFEDC 2021

作者： Lin, Ya-ping Wang, Zhen Zheng, Jun-zhang Luo, Man Hou, Jue Research Institute of Petroleum Exploration and Development CNPC Beijing China China National Oil and Gas Exploration and Development Corporation CNPC Beijing China

ISBN: (纸本)9789811921483

Based on the observation, study and analysis of core data, thin section, well logging and drilling data, the sedimentary facies, diagenetic facies and fracture facies of the carbonate reservoirs were carried out in Carboniferous Formation KC, KX oilfield, Caspian Basin. In this paper, the petrophysical facies of oil intervals in KC formation were examined to evaluate the pore and fracture structure and to predict zones with high porosity and permeability by classification. The petrophysical facies were divided by the superposition and combination of sedimentary facies, diagenetic facies and fracture facies. And a series of petrophysical facies of KC formation such as the pore-dissolved vuggy, the fracture-pore type, the high-angle connected fracture, were identified in this way. There are four main categories of petrophysical facies (PF-I, PF-ii, PF-iiI and PF-IV), which are characterized based on the constructive and destructive effect of sedimentary facies, diagenetic facies and fracture facies on the reservoir property and pore structure in KC formation. Since the reservoir quality index (RQI) and the limestone structure parameter (LSP) are the important factors for describing the reservoir micro-pore and fracture structure quantitatively, both RQI and LSP values were calculated respectively in the different petrophysical facies. Therefore, the classification and evaluation of KC formation reservoir pore structure in a single well were realized by the classification of petrophysical facies combined with well logging and core data. Finally, the vertical and horizontal distribution of sedimentary micro-facies and diagenetic facies of each single layer of KC formation were plotted, the distribution of favorable petrophysical facies can be used to predict the zones with high porosity and permeability. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pore structure

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Exploring and Analyzing Change: The Janus Project 22

Exploring and Analyzing Change: The Janus Project

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31st ACM International conference on Information and Knowledge Management (CIKM)

作者： Srivastava, Divesh Bleifuss, Tobias Bornemann, Leon Kalashnikov, Dmitri V. Naumann, Felix AT&T Dallas TX 75202 USA Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781450392365

data change, all the time. The Janus project seeks to address the Variability dimension of Big data by modeling, exploring, and analyzing such change, providing valuable insights into the evolving real world and ways in which data about it are collected and used. We start by identifying technical challenges that need to be addressed to realize the Janus vision. Towards this end, we have extracted and worked with the histories of various structured datasets, including DBLP, IMDB, open government data, and Wikipedia, for which a detailed history of every edit is available. Our DBChEx (database Change Explorer) prototype enables interactive exploration of data and schema changes, and we show how DBChEx can help users gain valuable insights by exploring two real-world datasets, IMDB and Wikipedia infoboxes. Based on an analysis of the history of 3.5M tables on the English Wikipedia for a total of 53.8M table versions, we then illustrate the rich history of structured Wikipedia data: we show that tables are created in certain locations, they change their shape, they move, they grow, they shrink, their data change, they vanish, and they re-appear;indeed, each table has a life of its own. Finally, to help automatically interpret the useful knowledge harbored in the history of Wikipedia tables, we present recent results on two technical problems: (i) identifying Natural Keys, a particularly important piece of metadata, which serves as a primary key in tables over time and consists of attributes inherent to an entity, and (ii) matching tables, infoboxes and lists within a Wikipedia page across page revisions. We solve these problems at scale and make the resulting curated datasets available to the community to facilitate future research.

关键词： big data variability change exploration natural keys matching structured data

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Feasibility Evaluation of Warm Solvent Assisted Gravity Drainage Process in Low-Carbon Developing Super-Heavy Oil or Oil Sands Project 84

Feasibility Evaluation of Warm Solvent Assisted Gravity Drai...

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2023 SPE EuropEC - Europe Energy conference featured at the 84th EAGE Annual conference and Exhibition, EURO 2023

作者： Liang, Guangyue Xie, Qian Liu, Yang Liu, Shangqi Xia, Zhaohui Bao, Yu Zhou, Jiuning Research Institute of Petroleum Exploration and Development CNPC China

ISBN: (纸本)9781613999912

It is very difficult to realize good economy returns using conventional SAGD process in many oil sands projects due to large CPF investment, massive steam injection, expensive surface diluent adding and increasing carbon emission tax. By contrast, warm solvent assisted gravity drainage process (WSAGD) is a promising low-carbon technology to deal with these SAGD challenges. This paper conducted feasibility evaluation by combined with Nsolv Best pilot analysis and a series of physical simulations. From 2014 to 2017, WSAGD pilot was successfully carried out by injecting butane at 60℃ in Suncor Dover oil sands. Its reservoir geological characteristics, physical properties, development technology policy and production performance were systematically analyzed. Combined with 4D seismic interpretation, RST and observation well data, the size and growth rate of solvent chamber were monitored and analyzed. Considering great uncertainty in numerical simulations influenced by many factors including grid size, solvent diffusion coefficient, interfacial tension and capillary force, a series of experimental tests and physical simulations were conducted. The behavior of viscosity reduction, interfacial tension reduction and microscopic oil displacement related to different solvents were systematically tested including propane, butane, pentane and hexane. Particularly, the performance of SAGD and WSAGD process were evaluated by 2D and 3D visual physical simulations. In Nsolv Best pilot, the target reservoir is low pressure, thin and shallow buried. The oil rate reached 250-300 barrels per day under 300 m horizontal section, and API degree of produced oil was upgraded to 13-16 from original 8. After 3 years of tests, the width of solvent chamber is 40-60m, lateral and vertical 1.56 m and 0.96 m per month, and horizontal conformance is 67%. The experiments results show that viscosity reduction trend will flatten out when the solvent concentration exceeds 10 vol% due to partial asphalt

关键词： Solvents

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exploration of the Fusion of FAST Algorithm and SIFT Algorithm in Image Feature Extraction

Exploration of the Fusion of FAST Algorithm and SIFT Algorit...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Jingyi Zuo Xianghua Ma School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of computer vision, image feature extraction is the bridge connecting low-level image data with high-level visual tasks such as image matching and object recognition. The SIFT algorithm occupies an important position in feature extraction due to its outstanding stability and uniqueness, but its computational complexity is relatively high, limiting its application in scenarios with high real-time requirements, and the algorithm runs relatively slowly. In contrast, the FAST algorithm is known for its efficiency, but it does not provide a method for calculating feature descriptors and lacks invariance to scale and rotation changes, only suitable for translational image changes. Therefore, this paper explores the fusion of the FAST algorithm and the SIFT algorithm, aiming to combine their strengths.

关键词： visualization Systematics Limiting Image matching Focusing Feature extraction Stability analysis Real-time systems Object recognition Optimization

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Interactive visualization of BioMedical data

Interactive Visualization of BioMedical Data

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IEEE International conference on Big data

作者： Mohammad Shafiqul Islam Rahat Ibn Rafiq Guenter Tusch College of Computing Grand Valley State University Michigan USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed’s annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool’s design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration.

关键词： Drugs data visualization Biomarkers Biology Gene expression data mining Bioinformatics Chemicals Diseases Biomedical imaging

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Effects of Stimulus Sequences on Brain-Computer Interfaces Using Code-Modulated visual Evoked Potentials: An Offline Simulation 17th

Effects of Stimulus Sequences on Brain-Computer Interfaces U...

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17th International Work-conference on Artificial Neural Networks (IWANN)

作者： Thielen, Jordy Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav Thomas van Aquinostr 4 NL-6525 GD Nijmegen Netherlands

ISBN: (纸本)9783031430770;9783031430787

Brain-computer interfaces (BCIs) translate brain activity into computer commands opening a novel non-muscular channel for communication and control. Before BCIs can be used effectively, they require extensive calibration of their machine learning algorithms using labeled training data. This tedious calibration process is not only time-consuming and costly but also restricts the exploration and optimization of stimulus parameters that could greatly enhance BCI performance. To overcome the challenge of acquiring large training datasets, a simulation framework was developed to eliminate the need for recording calibration data. Unlike previous studies, this simulation framework incorporates a biologically plausible forward model of the code-modulated visual evoked potential (c-VEP). By utilizing synthetic data generated by this improved simulation framework, an offline study was conducted to systematically compare five different stimulus conditions: almost perfect autocorrelation (APA) sequence, de Bruijn sequence, Golay sequence, Gold code, and m-sequence. The results of the study revealed that the Golay sequence achieved the highest grand average performance, followed by the APA sequence, m-sequence, Gold code, and finally the de Bruijn sequence. Furthermore, when the stimulus sequence was optimized for individual participants, typically the Golay and APA sequences exhibited the highest classification accuracy, leading to a significant improvement in overall accuracy as compared to using the Golay sequence for all participants. This research represents an important initial step towards optimizing stimulus parameters for BCIs using simulated data. This approach has the potential to accelerate the development and optimization of BCIs, resulting in more effective applications that can be tailored to individual users.

关键词： Brain-computer interfacing Code-modulated visual evoked potentials Electroencephalography Simulation

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SHAPELET BASED visual ASSESSMENT OF CLUSTER TENDENCY IN ANALYZING COMPLEX UPPER LIMBMOTION

SHAPELET BASED VISUAL ASSESSMENT OF CLUSTER TENDENCY IN ANAL...

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IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Datta, Shreyasi Karmakar, Chandan Rathore, Punit Palaniswami, Marimuthu Univ Melbourne Dept Elect & Elect Engn Melbourne Vic Australia Deakin Univ Sch Informat Technol Melbourne Vic Australia MIT Senseable City Lab Cambridge MA 02139 USA

ISBN: (纸本)9781728176055

The evolution of ubiquitous sensors has led to the generation of copious amounts of waveform data. Human motion waveform analysis has found significance in clinical and home-based activity monitoring. exploration of cluster structure in such waveform data prior to developing learning models is an important pattern recognition problem. A prominent category of algorithms in this direction, known as visual Assessment of (cluster) Tendency (VAT), employs visual approaches to study cluster evolution through heat maps. This paper proposes shape-iVAT, a new relative of an improved VAT model, that captures local time-series characteristics through representative subsequences, known as shapelets, to identify interesting patterns in motion data. We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. These shapelets are used to transform waveform data into a dissimilarity matrix forVAT evaluation. We demonstrate that the proposed method outperforms standard VAT with global distance measures for identifying complex upper limb motion captured using a camera-based motion sensing device. We also show that our method has significance in efficient and interpretable cluster tendency assessment for anomaly detection and continuous motion monitoring.

关键词： Cluster tendency distance measures shapelet time-series upper limb motion

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data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities

Data-driven visual analytics of Human Mobility data and gree...

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Intelligent Technologies (CONIT), International conference on

作者： Sivasubramanian Ramanathan Kulsoom Syed Tejaswini Chavan SVKMs NMIMS Navi Mumbai B. Tech Computer Science & Business Systems Mumbai India SVKMs NMIMS Navi Mumbai School of Technology Management & Engineering Mumbai India

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities.

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Learning and Prediction of Vehicle-Terrain Interaction from 3D Vision 2

Learning and Prediction of Vehicle-Terrain Interaction from ...

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conference on Multimodal Sensing and Artificial Intelligence - Technologies and Applications ii

作者： Ugenti, Angelo Vulpi, Fabio Milella, Annalisa Reina, Giulio Polytech Bari Dept Mech Math & Management Via Orabona 4 I-70125 Bari Italy Natl Res Council CNR Inst Intelligent Ind Technol & Syst Adv Mfg STIIM Via Amendola 122 D-O I-70126 Bari Italy

ISBN: (数字)9781510644052

ISBN: (纸本)9781510644052

Wheel-terrain interaction plays a critical role for vehicle mobility on natural terrain, such as in agricultural, planetary exploration and off-road settings. Estimation of the terrain characteristics and the way they affect traversability is essential for the vehicle to better plan its safest and energy-efficient path. This work proposes a novel approach to learn and predict from a distance the motion resistance encountered by a robotic vehicle, while traversing natural soil, by using visual information from a stereovision device. To this end, terrain appearance and geometry information are first correlated to resistance torque measurements during a learning phase via two alternative regression approaches, namely Least-Squares Boosting and Long-Short Term Memory Recurrent Neural Network. Then, such a relationship is exploited to predict motion resistance remotely, based on visual data only. Results obtained in preliminary experimental tests on ploughed and compact terrain are presented to show the feasibility of the proposed method.

关键词： Rough-terrain robots Vehicle-terrain interaction Multi-Sensor data processing Online learning and prediction Agricultural robotics

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