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Combining neural networks and control: potentialities, patterns and perspectives 22

Combining neural networks and control: potentialities, patte...

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22nd IFAC World Congress

作者： Cerf, Sophie Rutten, Éric Univ. Lille Inria CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France Univ. Grenoble Alpes Inria CNRS LIG GrenobleF-38000 France

ISBN: (纸本)9781713872344

Machine learning tools are widely used for knowledge extraction, modeling, and decision tasks;a range of problems that control Theory also tackles. Their relations have been largely explored by looking at stochastic control and Markov Decision processes, due to the proximity of their formulations. However, novel links between machine learning and deterministic control are emerging;combining both approaches, e.g. by performing identification with learning, or controlling the training process. The recent flourishing literature is vast: there is a need to identify challenges, trends and opportunities on this interface. This survey contributes i) to the compared analysis of both fields. ii) Based on literature review, a categorization of combinations of learning and control is drawn. In the control framework, learning has been used for modeling, controllers tuning or adaptation, generating a controller or as a controller itself, for translating complex objectives, or checking controlled systems. Conversely, in the learning framework, control is used for tuning hyperparameters, selecting or generating training data, as the training or decision-making algorithm itself or to guarantee learning properties. iii) Finally, discussions on the literature open novel promising combinations to be explored, such as control of neural networks' training process. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： control and automation data-driven control Learning for control Machine Learning Machine learning in modelling Neural Networks prediction Reinforcement learning and deep learning in control

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process- and Material-Specific modeling to Use in Simulation-Based and Resource-Oriented Decision Support Systems Using the Example of Calcium Silicate Brick Production Planning 19th

Process- and Material-Specific Modeling to Use in Simulation...

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19th Global conference on Sustainable Manufacturing, GCSM 2023

作者： Schrage, Tobias Schuderer, Peter Franke, Jörg Technische Hochschule Ingolstadt Esplanade 10 Ingolstadt80584 Germany Department of Mechanical Engineering Institute for Factory Automation and Production Systems Friedrich-Alexander-University Egerlandstr.7 Erlangen91058 Germany

ISBN: (纸本)9783031774287

As part of the fourth industrial revolution, data analysis and artificial intelligence are being integrated into production processes. In addition, energy consumption and CO2 costs are becoming decisive factors in the resource-oriented management of companies. For energy intensive and hybrid production processes a simulation-based decision support system (DSS) for production planning is validated and further developed for the sand lime brick industry to support the production planning process. The integration of empirical knowledge in the energy-intensive control of steam processes, in which quality-critical product parameters are set via thermodynamically complex relationships, is still part of current research approaches. In this paper, an approach for the mapping of an energetic system behavior in the energy-intensive and hybrid production processes will be discussed using the example of calcium silicate brick (CSB) production. Possibilities for using Discrete event simulation (DES) to increase the energy efficiency of steam processes are summarized and linked to formalized empirical knowledge in artificial Intelligence (AI) approaches. © The Author(s) 2025.

关键词： Quality control

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Design and Development of Sensor data Handling controller for Modern Robotics 7

Design and Development of Sensor Data Handling Controller fo...

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

作者： Megalingam, Rajesh Kannan Kuttankulungara Manoharan, Sakthiprasad Saju, Abhishek Premlal Labs Department of Electronics and Communication India

ISBN: (纸本)9798350372816

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 data collected with various sensors using multiple communication interfaces, such as USB Serial, I2C, and UART, which are used to transmit control commands and sensor data. A Teensy 4.1 microcontroller serves as the sensor controller in this system, enabling communication between the sensor controller and the system master controller. The study provides comprehensive environmental perception and control in robotics with the integration of ultrasonic sensors, current sensors, IMU, and hall sensors. To guarantee dependable sensor data acquisition and system functionality, the testing approach evaluates both manual and protocol-based communication for each sensor. This work demonstrates the simultaneous data reception made possible by the integration of several sensors with different protocols into a single microcontroller. The process of evaluating accuracy required contrasting human measurements with protocol-based communication for a range of sensors, including IMUs (orientation detection), current sensors (current flow measurement), and ultrasonic sensors (distance measurement). The integrated sensor system was put to the test using a variety of measuring techniques, including software-based and manual protocols, as part of the validation process. The precision of individual sensors is demonstrated by the results, which validate the correctness of the data acquired. The results of the experiment provide a detailed comparison between data provided by software and manual measurements, confirming the dependability and efficiency of the integrated sensor system. From the experiment results it is clear that the accuracy of the system is above 90%. © 2024 IEEE.

关键词： Ultrasonic sensors

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Using HTA and UML in analysis and Design Phases for a VR-Based Nursing Lab

Using HTA and UML in Analysis and Design Phases for a VR-Bas...

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International conference on Extended Reality (XR Salento)

作者： Ewais, Ahmed Asia, Mohammad Herndon, Cole Tighe, Orion Ulbrich, John Obeid, Mohammad F. Arab Amer Univ Comp Sci Dept Jenin Palestine Shenandoah Univ Div Appl Technol Winchester VA USA Arab Amer Univ Hlth Sci Dept Ramallah Palestine

ISBN: (纸本)9783031717031;9783031717048

Due to interruptions in the learning process at different universities in Palestine either because of the political situtaation or the COVID-19 pandemic, distance learning is becoming an adopted approach during academic semesters in disaster management planning for such universities. Consequently, different Learning Management Systems (LMS) are supporting synchronous and asynchronous teaching modalities. However, distance learning has some obstacles for nursing students who need to acquire skills taught in a real nursing laboratories. Among the possible solutions that can alleviate such an issue is virtual reality (VR) technology. This study aims at analyzing and designing a VR system (NursingXR) that provides training and assessments to nursing students of fundamentals of nursing lab courses in their undergraduate programs. The study presents a hybrid approach which utilizes the Multimedia Development Life Cycle (MDLC) along with Hierarchical Task analysis (HTA) and Unified modeling Language (UML) diagrams. The outcome is then used to provide the necessary information to VR developers in the implementation phase of the project. To validate the proposed requirements and HTA and UML diagrams, a prototype was developed based on output from analysis, design, and data collection phases. Results showed that the proposed hybrid approach is valid to develop VR applications in the medical domain.

关键词： Virtual Reality Nursing skill training Hierarchical Task analysis UML Development Approach analysis and Design

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Research on modeling Method of New Energy Generation about Randomness 7

Research on Modeling Method of New Energy Generation about R...

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7th IEEE conference on Energy Internet and Energy System Integration, EI2 2023

作者： Chen, Li Yang, Ming Wang, Zewei Li, Peng Wang, Chao Guan, Xiyang Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China New Energy Power Forecast Changji University Changji China

ISBN: (纸本)9798350345094

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 generation is to model and quantify the probability distribution of prediction error based on the deterministic prediction results. Based on Itô process model, this paper describes the stochastic characteristics of new energy power in the form of stochastic differential equation. Firstly, the model based method is used to construct Itô process parameters under the condition of given statistical model, and the probability distribution of prediction object is obtained by superimposing the probability distribution of prediction error on the deterministic prediction results. Secondly, the method based on historical data is used to estimate the Itô process parameters with the maximum likelihood value given the historical data. Finally, a numerical example proves that Itô process is practical in describing the randomness of new energy sources. © 2023 IEEE.

关键词： Forecasting

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Technical Conception of Large-Scale Site Operation and Maintenance Supported by Hyperspectral Information 1

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7th Chinese conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Nie, Cong Zhou, Jian Jiao, Yingjie Wang, Shaoqi Xi’an Modern Control Technology Research Institute Xi’an China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

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 demand for the spatial distribution and operation trajectory of facilities, equipment, personnel, and other factors. In this article, starting with hyperspectral recognition of device element information in complex environments, the overall concept of UAV hyperspectral telemetry and physical information association modeling. Through the release of UAV aerial survey platform technology, advantage is taken of the ability of hyperspectral targets. Focusing on analysis of technologies like hyperspectral image target detection based on sparse representation in complex backgrounds, large-scale data management is combined with storage and physical-information dynamic correlation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Unmanned aerial vehicles (UAV)

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Multi-regression modeling Approach for Choke Performance and Production analysis

Multi-regression Modeling Approach for Choke Performance and...

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

作者： Vicente, Mario Belkenoff, Raúl YPF S.A. Argentina

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 data analysis 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

关键词： Gases

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A New Algorithm to Compute the Correlation Matrix Implied By a Confirmatory Factor analysis Model 3

A New Algorithm to Compute the Correlation Matrix Implied By...

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3rd International conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2023

作者： Seyid Abdellahi, Ebnou Abdem El Hadri, Zouhair Iaousse, M'Barek Mohammed v University in Rabat Department of Mathematics Rabat Morocco Hassan Ii University of Casablanca Department of Mathematics Rabat Morocco

ISBN: (纸本)9798350398366

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 matrix implied by the model is the main step in the entire modeling process, and it is critical for estimating the model's parameters and testing its validity. This computation typically involves a method that relies on certain assumptions. This paper introduces a new algorithm for computing the implied correlation matrix that is simpler, more flexible, provides more accurate results, and is less restrictive in terms of assumptions. An illustrative example of this proposed algorithm is presented. Finally, the paper discusses and illustrates theoretical and numerical comparisons between the classical estimation method and the proposed approach based on the proposed algorithm. © 2023 IEEE.

关键词： Iterative methods

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Multi-Physical Field Coupling Numerical Simulation analysis of Selenium-Rich Tea Withering Environment Based on COMSOL 24

Multi-Physical Field Coupling Numerical Simulation Analysis ...

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5th International conference on Computer Information and Big data Applications, CIBDA 2024

作者： Ren, Shuang Liu, Sanjun College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi445000 China

ISBN: (纸本)9798400718106

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 project timelines and reduce costs. This paper is based on the COMSOL Multiphysics software to carry out a full-scale digital modeling of the withering room currently used in the Enshi area. It studies the distribution characteristics of temperature and humidity within the withering room and explores the design schemes for the layout of the withering heat source and dehumidification outlets. The results show that the withering room model designed in this paper, when meeting the temperature and humidity conditions needed for tea withering, has a more uniform temperature distribution inside, requires shorter heating time, and consumes less energy. The research findings can provide corresponding engineering recommendations for the design and construction of tea machinery equipment. © 2024 ACM.

关键词： Multiphysics

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Multifractal analysis and stacked autoencoder-based feature learning method for multivariate processes monitoring 41

Multifractal analysis and stacked autoencoder-based feature ...

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第41届中国控制会议

作者： Feng Yu Jianchang Liu Liangliang Shang Dongming Liu Department of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University School of Electrical Engineering Nantong University

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 process data,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.

关键词： Deep learning Industrial process Multifractal analysis Stacked autoencoder

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