This paper presents the design and development of a sensor controller, focusing on efficient data handling for integrated sensors utilized in robotic applications. In this study, we describe an extensive analysis of d...
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It's significance to establish a rational model of the randomness of new energy power for the analysis and control of new energy power system. One way to realize the probability prediction of new energy power gene...
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To enhance the ability of site organizers, government regulatory agencies, and security law enforcement forces to control the event activity process, large-scale site operation and maintenance work requires a strong d...
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Drawdown and choke management is an important process in production control and reservoir performance, primarily in unconventional and tight gas fields, where a well may produce with a high wellhead pressure for a lon...
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Drawdown and choke management is an important process in production control and reservoir performance, primarily in unconventional and tight gas fields, where a well may produce with a high wellhead pressure for a long period. Under critical conditions, the gas or liquid rate is a function of upstream pressure, gas-liquid ratio and choke aperture diameter. The analysis of well decline behavior and production performance has an added complexity when there are problems in terms of fluid rate measurement and different production conditions related to choke changes. Then, there is a need of finding a model for a two-phase flow to understand the choke size selection effects, analyze production limitations and performance, normalize production data, and simulate different operating conditions. A data-driven method was explored aiming at providing a formulation approach for a production choke performance model in different fields. Filtered 4000 points after dataanalysis from seven fields were incorporated to calibrate a two-phase flow model, grouping the data in three main datasets (unconventional oil, unconventional gas, tight gas). Empirical correlations and coefficients were obtained from a multivariable regression model, using a Gilbert-type formula for each group of fields. Confidence and validation tests indicate the observed parameter is very well explained by the independent variables, resulting in the maximization of prediction accuracy and determination coefficients. The comparison and deviation of estimated parameters with real data confirms the robustness of the modeling. The evaluation of the results demonstrates that the two-phase choke model obtained with the multivariable regression methodology allows: - validation of production data in the allocation process (e.g., estimated variable vs real variable). - incorporation of predicted data in production datasets when a variable is missing or not measured. - production normalization: evaluation of previous or
Confirmatory Factor analysis is an advanced sta-tistical technique that aims to explain the covariance structure of high-dimensional data by utilizing a small number of factors. The computation of the correlation matr...
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Withering is a crucial step in the tea-making process, and the quality of tea leaves after withering directly affects the quality of the final tea product. Computer technology and simulation can significantly shorten ...
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The success of a satellite mission depends on a lot of factors. The satellite thermal control system (TCS) is one of the most important subsystems that ensures that the satellite’s components remain within an accepta...
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The present paper examines how well various machine learning approaches predict solar irradiation levels. Applications like solar energy generation and climate modeling rely on accurate predictions of sun irradiation....
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Deep learning has become a popular tool for fault detection in industrial processes to learn complex nonlinear ***,the features extracted from most traditional deep networks usually ignore the geometric characters and...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Deep learning has become a popular tool for fault detection in industrial processes to learn complex nonlinear ***,the features extracted from most traditional deep networks usually ignore the geometric characters and singularities of the process *** representative features cannot be extracted effectively,which may lead to the inaccurate modeling and is not beneficial for the process ***,this paper proposes a feature learning method based on multifractal analysis and stacked autoencoder(MF-SAE) for fault detection in complex multivariate ***-SAE can learn high-level multifractal features from the raw data in an unsupervised *** analysis is frrst introduced to extract the multi-scale self-similar characteristics from industrial processdata,in which sigmoid function is added for preprocessing the process *** to the redundant information existing in the multi-scale feature,SAE is then utilized to learn key feature from the extracted multifractal *** learned hidden feature and residual feature are provided to construct the monitoring *** fault detection performance of MF-SAE is tested on the Tennessee Eastman(TE) process.
The proceedings contain 37 papers. The special focus in this conference is on Russian Automation. The topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological Analys...
ISBN:
(纸本)9783031824937
The proceedings contain 37 papers. The special focus in this conference is on Russian Automation. The topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological analysis and Synthesis Features of Technological processes;a Modern Method of Wireless control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless controlling Gauge Device Signal Is Triggered;integration of Automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and processing Thermoelectric data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an Intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger control of Technological processes of Resource Provision;system for Statistical Assessment of Means of controlling Engineers’ Qualification for Information Support to Manage the Recruiting process in Industry;smart Enumeration Technology;comparative analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement Learning with External Teacher for Building Energy Management.
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