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AN INDIRECT METHOD OF FRACTURING PRESSURE INTERPRETATION BASED ON data-DRIVEN WORKFLOWS 83

AN INDIRECT METHOD OF FRACTURING PRESSURE INTERPRETATION BAS...

引用

83rd EAGE conference and Exhibition 2022

作者： Hou, L. Elsworth, D. Wang, X. The University of Warwick United Kingdom The Pennsylvania State University United States ETH Zurich Switzerland

ISBN: (纸本)9781713859314

Fracturing pressure is one of the most important measurements recovered during hydraulic fracturing operations and carries important information regarding proppant transport and formation properties. We propose an indirect pressure-interpretation method to extract useful information, in which fracturing pressure is conversely used as output, rather than as input in conventional methods. A data-driven workflow is established to predict fracturing pressure by combing three methods (numerical models, machine learning algorithms, and error analysis) based on a dual-element hypothesis (injection and formation elements that control pressure variations). The numerical models extract injection and formation features for training the machine learning algorithms, then are promoted or optimized by analyzing their contribution in reducing prediction errors. The advanced models, in consequence, boost numerical characterizations of injection safety and formation evaluation. Two cases are presented to illustrate applications of the new method in the evaluation of proppant injection and brittleness index. A new mechanism of proppant transport and new brittleness calculation are reported by processing field data. The new method, integrating different numerical models, is compatible with broader applications in the evaluation of engineering and geological responses to hydraulic fracturing. Copyright© (2022) by the European Association of Geoscientists & Engineers (EAGE). All rights reserved.

关键词： Numerical models

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Prediction Technology of Ocean Acidification Based on Quantum Particle Swarm Optimization and Long Short-Term Memory Network

Prediction Technology of Ocean Acidification Based on Quantu...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Daizhen Wu PLA Dalian Naval Academy Dalian China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

In this paper, the ocean acidification prediction problem was studied deeply, and a new prediction model was constructed by using quantum particle swarm optimization algorithm (QPSO) and long short-term memory neural network (LSTM). Firstly, by introducing the quantum particle swarm optimization algorithm, the limitations of the traditional particle swarm optimization algorithm in search accuracy and global search ability are overcome, and the convergence speed and prediction accuracy of the model are improved. Secondly, using the ability of long short-term memory neural network to process time series data, it effectively captures the long-term dependence relationship in ocean acidification data, and further enhances the prediction ability of the model. Finally, through a series of experimental verification and performance evaluation, including root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2), it is proved that the model has high accuracy and generalization ability in ocean acidification prediction. The model provides a new method for the effective prediction of ocean acidification.

关键词： Accuracy Time series analysis Neural networks Predictive models Prediction algorithms data models Ocean acidification Particle swarm optimization Long short term memory Optimization

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Text classification based on machine learning for Tibetan social network 10

Text classification based on machine learning for Tibetan so...

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10th international conference on Advanced Cloud and Big data (CBD)

作者： Lv, Hui Li, Fenfang Liang, Yatao Duo, La Shen, Jun Li, Yan Zhou, Qingguo Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China Northwest Minzu Univ Key Lab Chinas Natl Linguist Informat Technol Lanzhou Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

ISBN: (纸本)9798350309713

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and text Convolutional Neural Networks (CNN) are used as classifiers to determine the best approach in Tibetan Social Network. In addition, term frequency-inverse document frequency (TF-IDF) is used to extract hot words and generate the word cloud. The results show that the random forest is significantly better than other machine learning algorithms on Tibetan text classification.

关键词： text classification social network Tibetan data mining

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Improving Stochastic Gradient Descent Initializing with data Summarization 25th

Improving Stochastic Gradient Descent Initializing with Data...

引用

25th international conference on Big data Analytics and Knowledge Discovery (DaWaK)

作者： Varghese, Robin Ordonez, Carlos Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9783031398308;9783031398315

Linear Regression (LR) is the prototypical statistical model, which can be applied on a wide range of predictive problems. Ordinary Least Squares (OLS), is the standard technique for estimating the parameters of the LR model. However, such computation can be slow and resource-hungry for large data sets with high dimensionality, due to heavy matrix operations. More importantly, OLS may be impractical for large data sets as the entire data set is required to be loaded into main memory, often exceeding RAM capacity. These limitations emphasize the need for optimization techniques to compute LR. Two state of the art algorithms used to compute LR are: Stochastic Gradient Descent (SGD) and data Summarization (DS), combined with a matrix factorization. A few decades ago DS was the main technique to accelerate data mining computations, followed by SGD. Nowadays, SGD has become the workhorse behind most ML algorithms and deep neural networks. Merging both techniques, we propose to initialize SGD with a solution computed via DS on the initial batch of points, leaving SGD computation on the remaining points "as is". An experimental evaluation with several data sets shows our improved SGD algorithm reaches higher quality solutions (lower MSE error, higher R-2) and it converges faster (less iterations, reduced data usage, less computation time). We believe our simple SGD change can benefit many more ML models beyond LR.

关键词： Matrix algebra

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data Management in EpiGraph COVID-19 Epidemic Simulator 27th

Data Management in EpiGraph COVID-19 Epidemic Simulator

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27th international European conference on Parallel and Distributed Computing (Euro-Par)

作者： Guzman-Merino, Miguel Duran, Christian Marinescu, Maria-Cristina Delgado-Sanz, Concepcion Gomez-Barroso, Diana Carretero, Jesus Singh, David E. Univ Carlos III Madrid Dept Comp Sci Leganes Spain Barcelona Supercomp Ctr Barcelona Spain CIBER Epidemiol & Salud Publ CIBERESP Madrid Spain Carlos III Inst Hlth Natl Ctr Epidemiol Madrid Spain

ISBN: (纸本)9783031061561;9783031061554

The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate many heterogeneous data sources. The work we describe in this paper focuses on the data management aspect of EpiGraph, a scalable agent-based virus-propagation simulator. We describe the data acquisition and pre-processing tasks that are necessary to map the data to the different models implemented in EpiGraph in a way that is efficient and comprehensible. We also report on post-processing, analysis, and visualization of the outputs, tasks that are fundamental to make the simulation results useful for the final users. Our simulator captures complex interactions between social processes, virus characteristics, travel patterns, climate, vaccination, and non-pharmaceutical interventions. We end by demonstrating the entire pipeline with one evaluation for Spain for the third COVID wave starting on December 27th of 2020.

关键词： Epidemiological simulation COVID-19 Heterogeneous data processing Parallel tool

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A Pair-Metamorphosis-Decouple Synthetic data Scheme for Color Fundus Image Registration

A Pair-Metamorphosis-Decouple Synthetic Data Scheme for Colo...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Chen, Zailiang Liu, Yuqi Shen, Hailan Luo, Tianhao Zhao, Rongchang School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color fundus (CF) image registration is crucial for accurate information fusion;it could obtain more details of retinal structure to assist clinical diagnosis. Existing methods suf-fer from costing time or dataset size, making CF image reg-istration still a challenging task. In this paper, we propose a novel pair-metamorphosis-decouple synthetic data scheme for learning-based CF image registration and ameliorate the registration model for retinal image. Specifically, we take ad-vantage of the pairing information of the registration task to decouple the differences between the pairing data and expand the representative ability of the dataset by synthesizing data. Furthermore, the registration framework is ameliorated ac-cording to the characteristics of the blood vessels in the retinal image. Experiments on the public dataset (FIRE) show that our synthetic data scheme could bring general performance promotion to registration models, and our registration method is superior to other state-of-the-art unsupervised algorithms. © 2022 IEEE.

关键词： Image registration

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Survey on Query Optimization of GPU database 23

Survey on Query Optimization of GPU database

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Jiahui Gai, Rongli Duan, Liming School of Information Engineering Dalian University Dalian University Dalian China Doctor of Dalian Commodity Exchange Dalian Commodity Exchange Dalian China

ISBN: (纸本)9781665494571

In the era of artificial intelligence, CPU-driven database is not applicable in terms of data access speed and processing speed. With ultra-high-speed computing power and ultra-large data processing bandwidth capabilities, GPU is favored by people. The database branch with GPU computing as the core (GDBMS) is booming. Because of its high characteristics of concurrency (simple) operation, high throughput operation, computationally intensive (ALU unit), the ability to interact with artificial intelligence, time-space data analysis, and data visualization, it has completely changed the traditional database format. The query operation is a common operation of the database system, and the optimization of the query is always a core task. This article first systematically introduces the history of database development, and then introduces several query optimization methods that currently exist. Finally, according to the existing query optimization methods of the database, the development trend of GPU database query optimization is prospected. © 2021 IEEE.

关键词： Concurrent computing Training Machine learning algorithms databases Query processing Graphics processing units Training data

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Efficient and accurate fine-grained trip identification for metro passengers from cellular trajectory data 8

Efficient and accurate fine-grained trip identification for ...

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8th international conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Li, Guanyao Xu, Ruyu Deng, Xingdong Liu, Yang Zheng, Chenghong Shi, Tingyan Zhao, Chuanbao Zha, Jiuping Guangzhou Urban Planning & Design Survey Research Institute Co. Ltd Guangzhou China Collaborative Innovation Center for Natural Resources Planning Marine Technology of Guangzhou Guangzhou China Guangdong Enterprise Key Laboratory for Urban Sensing Monitoring and Early Warning Guangzhou China School of Civil Engineering and Transportation South China University of Technology Guangzhou China Transportation College of Jilin University Changchun China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China College of Art and Science New York University New York United States

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

Gaining a comprehensive understanding of individuals' fine-grained metro trips within a city is crucial for effective urban planning, optimizing public transport network design, and facilitating the arrangement of public transport timetables. As cellular data allow us to analyze travel behaviors of the whole population, we study the problem of identify metro trip from cellular trajectory data in this work. Existing identification approaches designed for GPS trajectories often lack applicability to cellular data due to their lower spatial-temporal granularity compared to GPS data. Consequently, most approaches for cellular data focus on identifying coarse-grained transportation modes. To address the challenges of location noise and irregular data sampling for cellular data, we propose an efficient and accurate approach for fine-grained metro trip identification. Our approach employs a series of data processing operation to enhance the quality of cellular data, mitigating the effects of noise and irregular sampling. Moreover, considering the spatial-temporal information in the trajectory and metro network, we propose an effective spatial-temporal Hidden Markov Model to jointly split trajectories and identify metro trips from cellular data. Extensive experiments on two real datasets show that our proposed approach is significantly accurate and efficient for metro trip identification. © 2024 SPIE.

关键词： Hidden Markov models

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Supervised and Unsupervised Machine Learning Approaches on Class Imbalanced data 5

Supervised and Unsupervised Machine Learning Approaches on C...

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5th IEEE international conference on Smart Systems and Technologies, SST 2022

作者： Ugarković, Alen Orěski, Diiana University of Zagreb Faculty of Organization and Informatics Varazdin Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Huge amounts of data are stored digitally every day. This data has various characteristics. Class imbalance is one of the characteristics that has the effect of machine learning algorithms performance and this problem is receiving attention among academia and industry. Class imbalance occurs when the number of instances in one class is significantly different than the number of instances in the other class (in binary classification). In this paper, we are combining supervised and unsupervised machine learning approaches on one imbalanced dataset from a publicly available repository. Unsupervised machine learning approach of cluster analysis is applied on the most significant variables discovered by sensitivity analysis on predictive models developed by decision tree. Our results indicated a hybrid approach of decision tree and cluster analysis as a promising tool to work with imbalanced data. © 2022 IEEE.

关键词： Decision trees

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A Low-Complexity Three-Dimensional Parameter Estimation Algorithm for FMCW Radar 6

A Low-Complexity Three-Dimensional Parameter Estimation Algo...

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6th IEEE international conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Yuhan, Yuan Yingchun, Li Qi, Long Peng, Wu Tao, Jin Zhiquan, Zhou School of Information Science and Engineering Harbin Institute of Technology Weihai Weihai China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798350399059

Frequency Modulated Continuous Wave (FMCW) radar has been widely used in the field of vehicle mounted millimeter wave radar to detect the parameters of targets. A low complexity three-dimensional (3D) parameter estimation algorithm for time-division multiplexing multiple-input multiple-output (TDM-MIMO) FMCW radar is proposed to detect the range, velocity, and azimuth angle of targets. The proposed algorithm utilized the data block selection and rank-reduced technique to avoid the high computational burden of the traditional subspace-based algorithm. Moreover, the proposed algorithm solves the problem of velocity ambiguity in conventional TDM-MIMO FMCW signal processing. The simulation results show that the accuracy and resolution of the algorithm are much higher than the conventional algorithms, such as 3D-FFT, and 3D-MUSIC. © 2023 IEEE.

关键词： Parameter estimation

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