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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control II"

共 4771 条记录，以下是211-220 订阅

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A Comprehensive Simulation Study of Hydraulic Fracturing Test Site 2 (HFTS-2): Part ii – Development Optimization in the Delaware Basin Using an Integrated modeling Workflow

A Comprehensive Simulation Study of Hydraulic Fracturing Tes...

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2023 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2023

作者： Han, Jichao Zhao, Yu Li, Han Tan, Jiasen Bessa, Fadila Liu, Shunhua Sahni, Vinay Pudugramam, Venkateswaran Sriram Xie, Xueying Behzadi, Hamid Oxy United States

For unconventional reservoir development, it is critical to understand the impact of well spacing, completion designs, formation pressure depletion, and parent-child interference, etc. The comprehensive diagnostic dataset acquired in the Hydraulic Fracturing Test Site 2 (HFTS-2) project in the Delaware Basin provided a unique opportunity to develop a comprehensive modeling workflow for accelerating development optimization by building and calibrating integrated fracture and reservoir models. Full well models were constructed in this work to better capture the nonuniform depletion effect along the lateral by calibration with actual parent-child wells. The fracture model was calibrated with key observations from the HFTS-2 diagnostic dataset, which included fiber optic, microseismic, and downhole pressure data, along with hydraulic fracture and proppant descriptions and DFIT and image logs (presented in Part I of the study). The production data, pressure gauge data, and observations of well interference tests were then honored through history matching of the reservoir model. Finally, the fully calibrated model was employed to perform sensitivity analysis and to optimize completion design and field development. A simplified, fit-for-purpose workflow, as both a supplementary and a complementary modeling approach, was also introduced to provide quick initial guidance for the integrated workflow. Model calibration: The integrated fracture and reservoir models were fully calibrated with the comprehensive HFTS-2 diagnostic dataset that included fracture and production data and matched with DFIT and pressure gauge data. The prediction capability of the calibrated model was validated using other methods, such as decline curve analysis, rate transient analysis, and pressure-normalized rate analysis. Parent-child effect: Diagnostics and calibrated subsurface models both showed asymmetric fracture geometries for stages in child wells that overlapped parent wells due to the deple

关键词： Sensitivity analysis

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analysis of Electrical Automation Transformation Model Based on Matra Offset Algorithm

Analysis of Electrical Automation Transformation Model Based...

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2024 International conference on Power, Electrical Engineering, Electronics and control, PEEEC 2024

作者： Tang, Shanshan Anhui Vocational and Technical College Anhui Hefei230011 China

ISBN: (纸本)9798350378917

In this paper, the common problems of electrical engineering under automation environment are systematically analyzed, and some new solutions are put forward. First of all, this paper reviews the research status of this field both at home and abroad. Secondly, this paper introduces the application of Matra displacement model in the field of power system in detail, and summarizes the views of domestic and foreign experts on the development process and characteristics of this technology research. Then, starting from the theoretical basis and basic concepts, this paper expounds the important role of software in the modeling of computer simulation system and analyzes the impact on the system. Finally, the transformation of electrical automation is realized by MATLAB, and the corresponding results are given. The test results show that the static deviation data range is 0.084-0.096, while the dynamic deviation data range is 0.089-0.097;communication compatibility is as high as {1 0 0%} and as low as 96%. © 2024 IEEE.

关键词： MATLAB

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Deployment of Safety Predictive Analytics to Prevent Workplace Incidents and Promote Event Reduction: A Machine Learning Approach Towards a data-Driven Safety System 3

Deployment of Safety Predictive Analytics to Prevent Workpla...

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3rd International conference on Digital data processing, DDP 2023

作者： Hajar, Ait Lamkademe Ahmed, Naddami Karim, Choukri National Higher School of Electricity and Mechanics Hassan Ii University Laboratory of Advanced Research in Industrial and Logistics Engineering Casablanca Morocco Enset Mohammedia Hassan Ii University Eeis Laboratory Casablanca Morocco

ISBN: (纸本)9798350329018

With the ongoing era of digitization and the ability to bring together huge data sets, there is enormous potential for many industries and business functions to take advantage of their 'big data' sets by performing advanced analytics, allowing managers to make business decisions. When applied correctly, predictive analytics allow organizations to gain deep insight into their business and deploy their scarce resources in an optimal way (Predictive Solutions, 2012). Thus, improving safety in workplaces is no exception to using predictive analysis to predict and prevent events in real-time. Increasing process safety entails lowering the likelihood that hazardous environments may have negative effects. Unfortunately, it is impossible to measure the probability factor directly. The absence of an accident does not provide helpful information because Target Zero cannot be accomplished until the hazard is eliminated. On the other hand, numerous cause elements that could have been better monitored and controlled are discovered after a significant accident occurs. In this paper, we advocate for a proactive, dynamic system by embedding predictive analysis into two process stages. At the design stage, we suggest a predictive modeling process: 1. Define goals 2. Collect and preprocess data 3. Address missing data through data imputation 4. Build candidate predictive models 5. Train, test and cross-validate candidate models 6. Select 'the best' models based on multiple evaluation metrics 7. At runtime, consume the predictions from selected predictive models using the end-user interface. Depending on the data input and the time it takes for learning algorithms to learn, we claim the system can predict future potential input variables for the system in a single model and proactively adjust to meet intermediate and longterm goals and safety needs. A subset of data supports the proposal through in-sample validation. The validation through simulation was done based on actual data extr

关键词： data mining

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Full Wavelength Selection and Deep Spectra Feature Extraction Network for Near-Infrared Spectroscopy Quantitative analysis 13

Full Wavelength Selection and Deep Spectra Feature Extractio...

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13th IEEE data Driven control and Learning Systems conference, DDCLS 2024

作者： Liu, Jinlong Luan, Xiaoli Liu, Fei Jiangnan University Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangsu Wuxi214122 China

ISBN: (纸本)9798350361674

Near-infrared spectroscopy (NIRS) has been widely used in the chemical industry and has attracted much attention because of its fast and non-destructive characteristics. However, spectra are susceptible to interference from external environmental factors, leading to spectral overlap and noise problems. To solve these problems, spectral wavelength selection can be performed. Traditional selection methods require manually choosing the appropriate method based on spectral data and combining it with appropriate regression modeling, which is complicated and time-consuming. In contrast, this study proposes a novel spectral wavelength selection method that intelligently filters the entire wavelength based on the correlation between spectra and output variables. Simultaneously, it utilizes a deep spectra feature extraction module to obtain a more abstract spectral representation applied in regression prediction. Evaluation results on the training and test sets demonstrate the outstanding performance of this model: RMSE of 0.007796 and 0.01036 for the training and test sets, respectively, R2 of 0.9918 and 0.9842, with an impressive RPD of 7.9703. These metrics indicate that this model meets the rigorous requirements for high-performance models in the chemical engineering domain. © 2024 IEEE.

关键词： Near infrared spectroscopy

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Utilizing Proxy Re-Encryption for Enhanced Security in data Sharing based on Blockchain 7

Utilizing Proxy Re-Encryption for Enhanced Security in Data ...

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7th International conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Saisanthiya, D. Saxena, Naman Saini, Pratham SRM Institute of Science and Technology Networking and Communications School of Computing Faculty of Engineering and Technology Chennai603203 India

ISBN: (纸本)9798350372816

The rapid growth of the Internet of Things (IoT) has underscored the critical need for robust information exchange mechanisms, especially within cloud services. However, this advancement also introduces significant data security risks due to the potential for misuse. To address these concerns, this research proposes an innovative method to bolster data security in cloud environments using proxy re-encryption. Our method employs identity-based encryption, enabling data owners to securely upload encrypted data to the cloud. Authorized users can then access this data through a proxy re-encryption process. To handle computationally demanding tasks, an edge device functions as a proxy, thereby alleviating the limitations of typical IoT devices. A key feature of our system is the integration of blockchain technology, which decentralizes data sharing and alleviates the bottlenecks inherent in centralized systems, while also enabling fine-grained access control. A thorough data security analysis and review indicate that our solution effectively ensures data confidentiality, integrity, and public *** of Form, Bottom of Form. © 2024 IEEE.

关键词： Cloud security

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Research on Probability of Discharge in Emergency Departments Based on Stochastic Timed Petri Nets and Machine Learning 21

Research on Probability of Discharge in Emergency Department...

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21st International conference on Networking, Sensing and control, ICNSC 2024

作者： Wang, Yinglong Yu, Wangyang Fang, Xianwen Meng, Lei Cheng, Yumeng Zhang, Jing School of Computer Science Shaanxi Normal University Xi'an China School of Mathematics and Big Data Anhui University of Science and Technology Huainan China Qinghai Normal University Information Network Center Xining China

ISBN: (纸本)9798350365221

Currently, factors such as hidden health hazards, unhealthy lifestyles, and environmental pollution have led to the frequent occurrence of diseases, causing a common problem - a sharp increase in hospital admissions. However, for emergency departments (EDs), their medical resources and scale are insufficient to meet the challenges of this conventional hospital level. Thus, modeling the diagnostic process of the ED, and applying an appropriate method to predict the probability of discharges within a certain time, is of significant importance for achieving rational resource allocation. Effective allocation of medical resources can ensure that patients receive timely and effective medical care, improve the quality and level of medical services, and thereby enhance patient satisfaction with medical care. Therefore, the modeling tool for this paper is stochastic timed Petri nets (STPN), in order to more accurately simulate various random events and delays that occur within the ED. Additionally, for better analysis of the model, we will convert the time interval values assigned to transitions into time interval values assigned to places, creating a Petri net known as TSdPN. Using Net learning method to predict patient discharge probabilities helps healthcare providers allocate beds, resources, and follow-up medical services more accurately, thereby improving the efficiency of medical resource utilization. Combining these two approaches makes ED operations more intelligent and efficient, providing patients with a better quality medical experience. © 2024 IEEE.

关键词： Diagnosis

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Optimal Scheduling Policies for Remote Estimation of Autoregressive Markov processes over Time-Correlated Fading Channel 62

Optimal Scheduling Policies for Remote Estimation of Autoreg...

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62nd IEEE conference on Decision and control (CDC)

作者： Dutta, Manali Singh, Rahul Indian Inst Sci Dept Elect & Commun Engn Bengaluru 560012 Karnataka India

ISBN: (纸本)9798350301243

We consider the problem of optimally scheduling transmissions for remote estimation of a discrete-time autoregressive Markov process that is driven by white Gaussian noise. A sensor observes this process, and then decides to either encode the current state of this process into a data packet and attempts to transmit it to the estimator over an unreliable wireless channel modeled as a Gilbert-Elliott channel [1] [2] [3], or does not send any update. Each transmission attempt consumes lambda units of transmission power, and the remote estimator is assumed to be linear. The channel state is revealed only via the feedback (ACK/NACK) of a transmission, and hence the channel state is not revealed if no transmission occurs. The goal of the scheduler is to minimize the expected value of an infinite-horizon cumulative discounted cost, in which the instantaneous cost is composed of the following two quantities: (i) squared estimation error, (ii) transmission power. We posed this problem as a partially observable Markov decision process (POMDP), in which the scheduler maintains a belief about the current state of the channel, and makes decisions on the basis of the current value of the error e(t) (defined in (6)), and the belief state. To aid its analysis, we introduce an easier-to-analyze "folded POMDP." We then analyze this folded POMDP and show that there is an optimal scheduling policy that has threshold structure, i.e. for each value of the error e, there is a threshold b(star)(e) such that when the error is equal to e, this policy transmits only when the current belief state is greater than b(star) (e).

关键词： Remote estimation Gilbert-Elliott channel partially observable Markov decision process (POMDP) threshold-type policy

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Input Variable Relation and Importance Learning for Industrial process Soft Sensing with Spatial-Temporal Graph Neural Networks 43

Input Variable Relation and Importance Learning for Industri...

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43rd Chinese control conference, CCC 2024

作者： Tang, Mei Qi, Yu Tang, Siyuan Yang, Zeyu Yao, Le Hangzhou Normal University School of Mathematics Hangzhou311121 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China

ISBN: (纸本)9789887581581

In soft sensor modeling, spatial correlations among variables within industrial processes are often overlooked by conventional data-driven methods, leading to a missed opportunity to enhance model accuracy and interpretability. This paper addresses this gap by proposing a variable structure learning graph convolutional network (VSL-STGCN) model that leverages the spatial correlations of variables for improved forecasting. The model employs an adjacency matrix self-learning approach, which does not rely on prior knowledge and learns the graph structure through end-to-end gradient descent, making it suitable for the complex and dynamic nature of industrial processes. Moreover, through utilizing feature importance analysis method based on Shapley value, researchers can gain insights into the decision-making process, leading to increased trust in the model predictions. The integration of Shapley values into the GNN model provides a robust interpretability framework, allowing for the quantification of each variable contribution to the model output. This enhances the transparency and credibility for soft sensing model, particularly for key indicators in industrial processes, and facilitates targeted monitoring and control in real-world applications. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Graph neural networks

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Digital twin development of yeast fed-batch cultures for vaccine production 11

Digital twin development of yeast fed-batch cultures for vac...

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11th Vienna International conference on Mathematical Modelling (MATHMOD)

作者： Benavides, Micaela Gerkens, Pascal de Lannoy, Gael Dewasme, Laurent Wouwer, Alain Vande Univ Mons Syst Estimat Control & Optimizat SECO B-7000 Mons Belgium

This paper reports on designing a digital twin of a yeast culture process in an industrial context. Using a rich experimental data set corresponding to different input profiles, an original dynamic model based on the assumption of cell overflow metabolism is derived. The kinetic laws are represented by smooth functions combining Monod and Jerusalimski factors, which model rate activation and inhibition, respectively. Parameter estimation is achieved following an iterative procedure including practical identifiability analyses which allow the design of informative experiments, enhancing parameter precision and accuracy.

关键词： modeling Parameter estimation Biotechnology Yeast cultures

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Halfway Generic Verification of Railway control Systems 5th

Halfway Generic Verification of Railway Control Systems

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5th International conference on Reliability, Safety and Security of Railway Systems - Modelling, analysis, Verification and Certification (RSSRail)

作者： Zickert, Gustav Stathatos, Nikitas Prover Krukmakargatan 21 Stockholm Sweden

ISBN: (纸本)9783031433658;9783031433665

Formal verification of railway control systems, particularly Generic Applications (GAs) and Specific Applications (SAs), is crucial due to their complexity and safety-critical nature. This paper presents a novel framework, Halfway Generic Verification (HGV), applying Software Product Line (SPL) principles for formal verification of railway control systems. The HGV method offers a balanced approach that can verify a broad set of systems derived from the same Generic Design Specification (GDS) in a single computational sweep, retaining feasibility. It also highlights the potential benefits of implementing SPL analysis in modeling and verification of railway control systems, which include enhancing the process of configuration data generation and ensuring the correctness of the GDS and Generic Safety Specification. The effectiveness of the HGV approach is demonstrated with a prototype implementation utilizing the Prover iLock tool.

关键词： Formal Verification Generic Application Software Product Line Railway control System Interlocking Metaproduct Variability Encoding

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