With the rapid development of industrial automation and intelligence, fault detection technology has become key to ensuring system stability and safety. Neural networks, as outstanding data processing and pattern reco...
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The Industrial Internet of Things (IIoT) has emerged as a breakthrough technology in the industrial industry, providing new prospects for improving production processes, increasing operational efficiency, and enabling...
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This article introduces a campus information management approach using digital twin visualization. By integrating Web3D technology and visualizationtechniques, diverse campus data is incorporated into a virtual envir...
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The present research gives extensive attitude towards using Gen AI for foretelling election results and also analyze the behavior of the voter while voting. By capitalizing various data sources from preceeding electio...
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The proceedings contain 326 papers. The topics discussed include: sustainable solutions for a greener future: energy management through IoT;enhancing sentiment analysis accuracy through data driven hyperparameter opti...
ISBN:
(纸本)9798350389432
The proceedings contain 326 papers. The topics discussed include: sustainable solutions for a greener future: energy management through IoT;enhancing sentiment analysis accuracy through data driven hyperparameter optimization using convolutional neural network;a robust modelling of an artificial intelligence assisted brainwaves based robotic motion control system using Internet of Things and EEG signals;exploring emerging technologies for gastrointestinal cancer diagnosis;multi-lingual speech emotion recognition: investigating similarities between English and German languages;a gradient and double threshold-based algorithm for improved wideband spectrum sensing;an efficient dimensionality reduction techniques to data visualization;experimental evaluation in identification of kidney cancer using modified learning scheme;and AI and business planning: revolutionizing forecasting and resource allocation.
Drilling parameter optimization constitutes a pivotal technique for expediting the intricate processes involved in oil and gas drilling operations. Presently, optimization strategies predominantly hinge on statistical...
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ISBN:
(纸本)9781959025375
Drilling parameter optimization constitutes a pivotal technique for expediting the intricate processes involved in oil and gas drilling operations. Presently, optimization strategies predominantly hinge on statistical data analysis or data-driven models and optimization algorithms. However, these methods often overlook the real-time changes occurring in the downhole rock-breaking environment. Additionally, achieving high precision with data-driven rate of penetration (ROP) models proves challenging, as they may struggle to accurately reflect the intricate mechanisms involved in rock-breaking. Moreover, the inherent randomness and uncertainty associated with optimization algorithms pose difficulties in ensuring the consistent acceleration of the drilling process. This study introduces areal-time targets-based intelligent optimization and decision-making system tailored specifically for field drilling *** system workflow comprises data preprocessing, predictive optimization, and interpretive decision-making stages. In the crucial modeling and prediction phase, we developed a highly accurate ROP prediction model using advanced machine learning techniques and mechanistic empirical constraints. By employing three model updating mechanisms alongside a 3D heat map visualization technique, the most robust current ROP model can be selected, thereby enhancing the adaptability of the model to dynamic downhole environment. This process establishes a solid foundation for achieving specific ROP targets. In the optimization decision stage, this work proposes the optimal path decision process with specific target parameter optimization and small parameter fluctuation. The process begins by determining the ROP enhancement target for the next stage based on the average ROP of the current formation. Subsequently, the eligible combinations of drilling parameters undergo a preliminary screening. Afterwards, the optimal path decision-making process is informed by the considera
Petroleum engineers usually create a set of hundreds of models of a given oil reservoir under analysis to represent its uncertainties. Assisted optimization approaches may help engineers to select a subset of these mo...
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Constraint programming techniques are particularly successful at solving discrete optimization problems such as resource allocation, scheduling or transport problems which are ubiquitous in the industry. Although hist...
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The proceedings contain 59 papers. The topics discussed include: a generic modeling approach towards simulating an urban primary and secondary healthcare facility network;a survey of visualization capabilities for sim...
ISBN:
(纸本)9781713873280
The proceedings contain 59 papers. The topics discussed include: a generic modeling approach towards simulating an urban primary and secondary healthcare facility network;a survey of visualization capabilities for simulation environments;agent-based model output analysis – a comprehensive statistical framework;decision support framework for automating the optimization of edge computing federations;error estimators for adaptive scheduling algorithm for serial co-simulation;hybrid agent-based and discrete event simulation in mason;model-based systems engineering and simulation of a molten salt reactor power plant for requirements analysis;modeling forced migration: a system dynamic approach;predictive modeling and simulation system for the management of harmful cyanobacteria blooms;simulation and optimizationtechniques for the mitigation of disruptions to supply chains;and a statechart template library for IoT system modelling.
In recent years, Convolutional Neural Networks (CNN) have revolutionized the field of medical science by efficiently expediting the process of detection and classification of diseases such as cancer. Adam is one of th...
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