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Federated Learning-Based Distributed Model Predictive control of Nonlinear Systems

Federated Learning-Based Distributed Model Predictive Contro...

引用

American control conference (ACC)

作者： Zeyuan Xu Zhe Wu Department of Chemical and Biomolecular Engineering National University of Singapore Singapore

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This work develops a federated learning-based distributed model predictive control (FL-DMPC) method for nonlinear systems with multiple subsystems to address the privacy-preserving issue of data transmission among subsystems and heterogeneity issue due to non-independent and identically distributed data among subsystems. Specifically, a novel FL framework is proposed to aggregate submodels into a global FL model with a sufficiently small modeling error with provable convergence properties derived based on iteration theory. Subsequently, by incorporating the FL model into a DMPC scheme, an FL-DMPC method is presented to achieve the expected performance of nonlinear systems. Finally, a chemical process network is adopted to demonstrate the effectiveness of the proposed FL-DMPC method.

关键词： data privacy Federated learning Simulation Distributed databases Predictive models Stability analysis data communication

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Clustering of Solutions Morphological Set by Engineering Systems modeling

Clustering of Solutions Morphological Set by Engineering Sys...

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International Russian Automation conference (RusAutoCon)

作者： Dmitry Rakov Department of technological processes and systems control Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) Moscow Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Clustering of the morphological set of engineering solutions in the synthesis of systems is the process of grouping similar solutions in order to facilitate their analysis and selection of the best option. This method simplifies the complex task of choosing the optimal solution by dividing it into smaller group analysis. The article discusses the clustering of synthesized solutions in morphological analysis. The issues of building clusters and the possibility of dividing individual clusters into subclusters are considered. The example shows the expediency of these procedures in mathematical modeling. After clustering a set of solutions, the generated groups are analyzed and the best option is selected, taking into account the specified criteria and constraints. This approach makes it possible to effectively solve the problems of synthesis of technical systems and reduces the time for making informed decisions. The development of cluster analysis of a morphological set of decisions contributes to improving the efficiency and quality of decisions made in various technical fields.

关键词： Industries Analytical models data analysis Automation Decision making Buildings Systems modeling Market research Mathematical models Business

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Mirage-App×Act-2024: A Novel dataset for Mobile App and Activity Traffic analysis

Mirage-App×Act-2024: A Novel Dataset for Mobile App and Act...

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IEEE International conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Idio Guarino Domenico Ciuonzo Antonio Montieri Antonio Pescapè University of Verona University of Napoli Federico II

ISBN: (数字)9798350387445

ISBN: (纸本)9798350387452

This paper presents MIRAGE-App×AcT-2024, a novel dataset originating from the efforts of the MIRAGE project, which collects traffic and corresponding ground-truth data from human-generated mobile-app interactions. By providing detailed insights into traffic patterns, the dataset supports advancements in mobile network optimization, security, and application performance evaluation. To this aim, we present an initial characterization and modeling of MIRAGE-App×AcT-2024. This new release aims to facilitate further research and development in mobile network traffic analysis, focusing on interactive, multi-activity apps and activity-level analysis.

关键词： Wireless communication Performance evaluation Computational modeling Focusing Telecommunication traffic Traffic control Security Research and development Optimization Mobile computing

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Predictive analysis of CPI Based on Machine Learning Algorithms

Predictive Analysis of CPI Based on Machine Learning Algorit...

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Power, Electrical Engineering, Electronics and control (PEEEC), International conference on

作者： Xiawei Wang School of Digital Economics Jinjiang College Sichuan University Pengshan Sichuan China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

In recent decades, with the vigorous development of the national economy, the living standards of residents have also risen, and various types of consumption have emerged. In order to study the consumption situation of residents, the government has proposed the Consumer Price Index (CPI) as an indicator to reflect changes in their consumption and service levels. In order to better study the Consumer Price Index, this paper proposes a predictive analysis method for CPI based on machine learning algorithms. The first stage of this research method involves data visualization, which includes two small steps: data preparation explanation and chart visualization. This stage mainly provides initial basis for subsequent modeling; The second stage is the highlight of machine learning (random forest) algorithm modeling, which uses the monthly indices of national, city, and rural levels to train the model separately, with scores of $0.9927,0.9936$, and 0.9919 respectively, and draws the best fitting line; The third stage is for the model to predict the new data for the next month. Through the verification of the entire process mentioned above, the model in this article has good predictive performance and can effectively help solve the problem of CPI prediction.

关键词： Training Analytical models Machine learning algorithms Economic indicators Urban areas data visualization Predictive models data models Predictive analytics Standards

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Optimizing Template Models to Quantifiably Assess Center of Mass Kinematic Reconstruction

Optimizing Template Models to Quantifiably Assess Center of ...

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International conference on Rehabilitation Robotics (ICORR)

作者： Kelly, David J. Wensing, Patrick M. Univ Notre Dame Dept Aerosp & Mech Engn 365 Fitzpatrick Hall Engn Notre Dame IN 46556 USA

ISBN: (数字)9781665488297

ISBN: (纸本)9781665488297

The center of mass (COM) plays a fundamental role in human ambulation, but the redundant nature of the human body adds complexity to mathematically modeling its dynamics. Template models like the Bipedal Spring Loaded Inverted Pendulum (B-SLIP) and the Virtual Pivot Point (VPP)address this complexity by removing the redundancy while retaining desired salient characteristics, such as the COM ***, template models for the COM during human walking have mostly been used for qualitative analysis due to issues such as overestimation of COM vertical displacement. This paper considers a quantifiable template-based analysis of human walking by using an optimization framework to set the model parameter values for matching both explicitly and implicitly considered gait characteristics. Furthermore, it is shown that allowing the leg stiffness of the B-SLIP and VPP model to vary throughout the gait cycle better matches vertical COM trajectories with 54%-63% error *** optimized template models show promise in retaining ground reaction force (GRF) information, which is not explicitly considered during the optimization process. Future work looks to incorporate these optimized trajectories as a reference for control of a lower-limb knee-ankle prosthesis.

关键词： Legged locomotion Analytical models Mathematical models data models Robustness Complexity theory Task analysis

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State Synchronization for Dual Digital Twin of EV Batteries by Lyapunov Stability Condition and Contraction analysis 49

State Synchronization for Dual Digital Twin of EV Batteries ...

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49th Annual conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Weng, Yu Xie, Jiahang Ron Hui, Shu Yuen Wen, Changyun Nguyen, Hung Dinh School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350331820

State synchronization is crucial in matching digital twins to physical objects dynamically in order to have trustworthy modeling and reliable prediction. This work proposes two approaches to ensure state synchronization on the introduced Dual Digital Twin (DDT). DDT is deployed on both the cloud and the edge ends for better realtime monitoring and control of EVs' batteries, especially when Internet communication is limited. In this paper, the state synchronization problem is cast as a stability problem and also a contraction problem to understand systems' evolution over time. The proposed approaches leverage the Lyapunov stability condition and contraction theory to enforce the synchronization between the twin models and real battery states with streaming data. The contraction analysis framework does not require the knowledge of initial points. The derived stability and contraction conditions that reflect the physical properties of dynamical systems can be embedded into the training process of the reduced order model (ROM) neural network. Simulation results demonstrate the effectiveness of the proposed approaches. The average ROM prediction error is 1.16% and has a 30.39% accuracy improvement with the proposed algorithm. © 2023 IEEE.

关键词： Dynamical systems

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modeling and Recognition of Movement-Inducing Fatigue State Based on ECG Signal 15th

Modeling and Recognition of Movement-Inducing Fatigue State ...

引用

15th International conference on Intelligent Robotics and Applications (ICIRA ) - Smart Robotics for Society

作者： Liu, Jingjing Zeng, Jia Wang, Zhiyong Liu, Honghai Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Harbin Inst Technol Shenzhen Shenzhen 518055 Guangdong Peoples R China

ISBN: (纸本)9783031138225;9783031138218

Fatigue monitoring is significant during movement process to avoid body injury cased by excessive exercise. To address this issue, we developed an automated framework to recognize human fatigue states based on electrocardiogram (ECG) collected by a smart wearable device. After preprocessing on the raw ECG data, both machine learning solution and deep learning solution were introduced to recognize the fatigue states. Specifically, a set of hand-crafted features were designed which are fed into different machine learning models for comparison. For the deep learning solution, the residual mechanism was employed to build a deep neural network for fatigue classification. The proposed methods were evaluated on data collected from subjects after running exercise and achieved an accuracy of 89.54%.

关键词： Fatigue analysis ECG Machine/deep learning

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Reduced Order modeling via CFD Simulation data for Inclusion Removal in Steel Refining Ladle

Reduced Order Modeling via CFD Simulation Data for Inclusion...

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International conference on control, Mechatronics and Automation (ICCMA)

作者： Hong Wang Xipeng Guo Chenn Zhou Bill King Judy Li Energy and Traqnsportation Science Directoriate Oak Ridge National Laboratory Oak Ridge TN USA Center for Innovation Through Visualization and Simulation Purdue University Northwest Hammond IN USA Cleveland-Cliffs Research & Innovation Center Cleveland-Cliffs Inc. Middletown OH USA

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper presents a novel reduced order modeling for ladle processing in steelmaking, where the inclusion is modeled in line with a number of inputs (the gas flowrate, slag thickness, slag viscosity, initial inclusion). At first, a comprehensive computational fluid dynamics (CFD) simulation was performed to generate calculated data base that represents the input and output (i.e., the inclusion mass). Then an exponential and quadratic functions are used as the fitting model that parametrize the time-domain response of the inclusion in the ladle processing. A neural network has then been used to model the relationship between the inputs and the parameters of the fitting functions. In total 86 case of CFD simulation have been carried out and used to build up the reduced order model, and desired results have been obtained.

关键词： Viscosity Computational fluid dynamics Computational modeling Neural networks Fitting process control Slag Reduced order systems data models Time-domain analysis

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analysis of Fractal Techniques using Python Technologies and Integration of Artificial Intelligence

Analysis of Fractal Techniques using Python Technologies and...

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International Carpathian control conference (ICCC)

作者： Bogdan Popa Şendrescu Dorin Gabriel-Terry Ureche Ion-Marian Popescu Anca Albita Department of Automation and Electronics University of Craiova Craiova Romania Staff Engineer Sugar CRM Craiova Romania

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

Fractal image processing remains today one of the test techniques for time processing speed on image analysis. Based on previous generation experiences for the Mandelbrot set, the article will present the performance of these algorithms in the context of new technologies using an artificial intelligence perspective. The focus of the work is to develop applications using the Python language for fractal generation and estimation of the results using artificial intelligence. It can be said that to achieve fractal images, a lot of computing power, time, and useful optimizations are required. Nowadays, all these techniques can advance from parallel computing to integration, modelling, and high performance using artificial intelligence to estimate the results of the generation. From the other perspective, fractal theory is unpredictable, representing part of chaos theory, which can upset the data of an A.I. system.

关键词： Training Image analysis Computational modeling process control Estimation data visualization Parallel processing Fractals Artificial intelligence Optimization

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A Universal Model for Harmonic Source Uncertainty Based on Gaussian process Regression

A Universal Model for Harmonic Source Uncertainty Based on G...

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Electrical Engineering and control Technologies (CEECT), conference on

作者： Xiangwei Chen Longyang Zhu Yi Zhang Chaoyun Yang Shuchang Chen Yanyi Kang Fuzhou Power Supply Company State Grid Fujian Electric Power Company Fuzhou China College of Electrical Engineering and Automation Fuzhou University Fuzhou China Guangdong Power Grid Zhuhai Power Supply Bureau Zhuhai China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

The extensive integration of renewable energy sources and large-scale power electronic devices into the grid has made the analysis and modeling of harmonic source behavior increasingly challenging. Existing models primarily provide precise value estimations but struggle to adequately reflect the uncertain impacts of unconsidered physical factors on the characteristics of harmonic sources, thus having limited universality and adaptability. This paper suggests a comprehensive model for addressing these concerns by utilizing Gaussian process Regression (GPR) to manage harmonic source uncertainty. Firstly, the linear dependency between harmonic currents and voltages of harmonic sources is embedded into the mean function of the Gaussian process. Secondly, an appropriate covariance function is selected to reflect the similarity of harmonic properties under different operating conditions of harmonic sources. Thirdly, model parameters are solved using the maximum likelihood method to perform interval predictions of harmonic currents. Finally, the feasibility of the proposed method for modeling single harmonic sources and complex multiharmonic source networks is verified through simulation data from a 12-pulse rectifier device and a complex network containing multiple harmonic sources. The results of the case studies demonstrate that the proposed method can capture the uncertain behavior of harmonic characteristics, with features such as high accuracy and strong universality.

关键词： Adaptation models Uncertainty Prevention and mitigation Rectifiers Gaussian processes Complex networks Harmonic analysis data models Monitoring Load modeling

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