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Human and Passive Lower-Limb Exoskeleton Interaction Analysis: Computational Study with Dynamics simulation using Nonlinear Model Predictive Control 62

Human and Passive Lower-Limb Exoskeleton Interaction Analysi...

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62nd Annual conference of the Society-of-Instrument-and-Control-Engineers (SICE)

作者： Haraguchi, Naoto Nasr, Ali Inkol, Keaton A. Hase, Kazunori McPhee, John Tokyo Metropolitan Univ Dept Mech Syst Engn Tokyo Japan Univ Waterloo Dept Syst Design Engn Waterloo ON Canada

ISBN: (纸本)9784907764807

Forward dynamics simulations have the advantage of assessing performance of novel exoskeleton designs at a low cost. For developing a new passive lower-limb exoskeleton, the simulation needs to represent the sitting posture in which the wearer performs working tasks while maintaining balance with the whole body. The present study constructed a forward dynamics simulation for analyzing and developing a new passive lower-limb exoskeleton;the validity of the simulation was investigated using experimental data. The present method computes the interactions between the exoskeleton and wearer, such as reaction forces, physical posture, and physical load, based on the forward dynamics simulation driven by nonlinear model predictive control (NMPC). The NMPC cost function consisted of the physical load and the fitness of working task with constraints to evaluate balance. As a result, the present simulation represented the characteristic posture when sitting on the exoskeleton in which the wearer performs the working task while maintaining balance with the whole body. However, the simulation computed an upright posture of the lumbar joint that differed from the experimental results and needs to be improved. In future work, the simulation will be modified for representing the valid physical posture when wearing the exoskeleton, such as simulating the physical motion of the same working task as in the experiment, and modeling the interaction between the human, exoskeleton, and ground.

关键词： Biomechanical analysis Multibody model Model-based design optimization Balance control

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A Data-driven Prediction Method of Short-Term Cooling Load Based on Machine Learning and Regression Analysis 4

A Data-Driven Prediction Method of Short-Term Cooling Load B...

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4th International conference on Energy Engineering and Power Systems, EEPS 2024

作者： Guo, Jie Zhao, Qiang University of Jinan School of Physics and Technology Jinan China

ISBN: (纸本)9798350366914

The modeling and prediction of air conditioning load in residential buildings has been an indispensable step in energy conservation and cost reduction. This paper proposes a data-driven load prediction model based on machine learning technology. Firstly, the input data is processed by feature engineering according to the Pearson Correlation Coefficient (PCC) between load and features. Secondly, three machine learning algorithms (SVR, MLP, and RF) and Pearson Correlation Coefficient (GTO) optimization algorithm are used for prediction. Finally, the predicted load is output by linear analysis and weighted regression. The model is verified by using residential building simulation data generated by Dest software. The influence of model integration on the overall prediction results and the error distribution of predicted load in different periods are analyzed and discussed. The Mean Absolute Error (EMAE) of predicted load is 1.52kW, and the Mean Absolute Percentage Error (EMAPE) is 10.7%. © 2024 IEEE.

关键词： Prediction models

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simulation and Performance Analysis of Electric Vehicle 3

Simulation and Performance Analysis of Electric Vehicle

引用

3rd IEEE International conference on Intelligent Techniques in Control, optimization and Signal Processing, INCOS 2024

作者： Dickson, Jean Devaraj, D. Kalasalingam Academy of Research and Education Departmnt of Electrical and Electronics Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350361186

Reducing greenhouse gas emissions and pollution in our environment can be achieved with electric automobiles. Electric Vehicle is highly required to replace the impact of IC engines. The goals include enhancing fuel efficiency, cutting emissions, guaranteeing drivability, and preserving the energy storage system's longevity and current state of charge while taking constraints into account. Power train that is driven by fuel cells, batteries, and super capacitors can offer an economical transportation solution. An effective Energy Management System (EMS) is highly required to distribute power across the power train's components by choosing the right operating modes. This article has discussed the electric vehicle's modeling. An optimized model has been created by taking into account real key parameters. This study compares the actual speed of the vehicle with the input drive speed to determine the optimal performance of an electric car. The initial and final charge of the battery is used to compare the energy consumption value of the electric vehicle. Examined has been the impact of many characteristics on the energy consumption and performance of vehicles. © 2024 IEEE.

关键词： Energy management systems

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Direct data-driven control with signal temporal logic specifications 8

Direct data-driven control with signal temporal logic specif...

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8th IFAC conference on Analysis and Design of Hybrid Systems (ADHS)

作者： van Huijgevoort, B. C. Verhoek, C. Toth, R. Haesaert, S. Eindhoven Univ Technol Eindhoven Netherlands Max Planck Inst Software Syst Saarbrucken Germany Inst Comp Sci & Control Budapest Hungary

Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task. In this work, we develop a direct data-driven control synthesis method for temporal logic specifications, which does not require this explicit modeling step, capable of providing certificates for the general class of linear systems. After collecting a single sequence of input-output data from the system, we synthesize a controller, such that the controlled system satisfies a (possibly unbounded) temporal logic specification. The underlying optimization problem is solved by mixed-integer linear programming. We demonstrate the applicability of the results through simulation examples. Copyright (c) 2024 The Authors.

关键词： Mixed-integer linear programming

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An EV-Traction Inverter Data-driven Modelling for Digital Twin Development 23

An EV-Traction Inverter Data-Driven Modelling for Digital Tw...

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23rd International Scientific conference on Electric Power Engineering, EPE 2023

作者： Ibrahim, Mahmoud Raja, Hadi Ashraf Rassolkin, Anton Vaimann, Toomas Kallaste, Ants Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

ISBN: (纸本)9798350335934

Digital Twin (DT) technology has achieved a significant breakthrough in the development of numerous applications, including electric vehicles (EVs). This technique involves the creation of a virtual version of a real object, utilizing simulation and optimization tools with real data to optimize its functioning. The traction inverter is considered the brain of the EV drive system, as it controls the power flow between the battery and the electric motor. It converts the DC voltage from the battery into AC voltage and adjusts the frequency and amplitude to control the speed and torque of the motor. This research work presents the DT modelling procedures of an EV traction inverter. The basic principles of DT are discussed, and the model-driven DT modelling technique is compared to the data-driven. The motor-drive physical model (test bench) is provided. A data-driven model of the inverter was generated using a nonlinear regression-based artificial neural network ANN. Historical data from experimental tests were used as a modeling reference. The obtained results from the data-driven model achieved high prediction accuracy. © 2023 IEEE.

关键词： Digital storage

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Data-driven Acceleration Method for Discrete Interprovincial Medium and Long-term Power Transactions Clearing Problem 9

Data-driven Acceleration Method for Discrete Interprovincial...

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9th Asia conference on Power and Electrical Engineering, ACPEE 2024

作者： Gao, Qian Yang, Zhifang Cheng, Haihua Li, Wenyuan Zhou, Ziqing Deng, Hui Chongqing University State Key Laboratory of Power Transmission Equipment Technology Chongqing China China Electric Power Research Institute Nanjing China Power Market Simulation Laboratory State Grid Zhejiang Electric Power CO. LTD. Research Institute Hangzhou China

ISBN: (纸本)9798350309638

The optimization of the interprovincial medium and long-term power transactions clearing problem aims to maximize social welfare through the selection of optimal purchasing and selling pairs. Considering the extensive range of time periods, buyers, sellers, transmission paths, and the discrete nature of selling in blocks, it is intractable to obtain the optimal solution within a practical computing time limit, even using the state-of-the-art discrete optimizer. To address this challenge, this paper proposes a data-driven approach to accelerate the solution process by fully exploring few-shot historical data. First, the stable structure of the solution pattern is identified and fixed in advance to reduce the model scale. Second, multiple warm-start solutions are selected using the bidding price feature. Finally, the solution process is accelerated to achieve an improved decision. Based on practical data in China, the case study demonstrates that the proposed method can improve the solution quality by 1 million RMB on average within the solution time limit of 1 hour, compared with directly solving the clearing problem using the state-of-the-art commercial solver. © 2024 IEEE.

关键词： optimization

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Artificial Intelligence Data-driven Petri nets Approach for Virtualizing Digital Twins

Artificial Intelligence Data-Driven Petri nets Approach for ...

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2023 IEEE International conference on Industrial Technology, ICIT 2023

作者： Junior, Alexandre Oliveira Calvo-Rolle, Jose-Luis Leitao, Paulo Instituto Politécnico de Bragança Bragança5300-253 Portugal University of A Coruña Department of Industrial Engineering A CoruÃ±a Ferrol15405 Spain Bragança5300-253 Portugal

ISBN: (纸本)9798350336504

Virtualization is one key design principle in Industry 4.0, with the modeling and simulation of the physical assets playing crucial roles in the Digital Twin context. Different approaches can be used to implement the virtual asset models, ranging from simple equations to complex mathematical models. Petri nets formalism is a suitable approach to model and simulate the physical asset operation in the Digital context, particularly those that are event-driven, taking advantage of its inherent robust mathematical foundation. Having this in mind, this paper proposes a Petri nets approach, which considers Artificial Intelligent data-driven analytics associated to timed transitions to support the execution of what-if simulation aiming the monitoring, diagnosis, prediction, and optimization. The proposed approach was tested in an experimental punching machine, allowing the early identification the performance degradation in the Digital Twin and the selection of actions to be implemented in the physical asset to improve its operation. © 2023 IEEE.

关键词： Petri nets

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An Open Source Multiphysics Workflow for the Analysis of Fusion driven Systems

An Open Source Multiphysics Workflow for the Analysis of Fus...

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2024 International conference on Physics of Reactors, PHYSOR 2024

作者： Nyberg, Matthew J. Eickman, Joseph T. Shriwise, Patrick C. Lindley, Ben A. University of Wisconsin-Madison MadisonWI United States Argonne National Laboratory LemontIL United States

ISBN: (纸本)9780894487972

A workflow is presented in this paper which couples the flexible source generation available within OpenMC to the open source multi-physics solver GeN-Foam for accurate multi-physics and multidimensional simulation of externally driven systems (EDS). Some previous transient analysis of EDS in literature have been limited to 1-D models, but this does not account for significant differences in the spatial flux and fission spectra exist between critical and subcritically driven systems, or significant differences based on the spatial distribution of the driving source. These geometric variations have proven to be important to the neutronics analysis of fission-fusion hybrid (FFH) systems, of which design, down-selection, and optimization studies have been previously performed. Coupled multi-physics solvers are especially important for the liquid-fueled molten salt fast reactor concepts which have been down-selected as part of previous minor actinide burner design. Subcritical molten salt fast reactor systems based on validated critical models were simulated using GeN-Foam for transients such as source overpower, which is a process specific to EDS, and results match theoretical behavior. Multi-dimensional, mesh-based modeling allows complex geometries systems to be simulated accurately, and the workflow presented could lead to an open-source workflow for multi-physics fusion analysis. © 2024 AMERICAN NUCLEAR SOCIETY. All rights reserved.

关键词： Molten salt reactor

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Data-driven Home Energy Management optimization Using Reinforcement Learning 4th

Data-Driven Home Energy Management Optimization Using Reinfo...

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4th International conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： El Aouni, Abdelaziz Naimi, Salah Eddine Ayat, Yassine Mir, Ismail Laboratory of Energy Embedded System and Information Processing ENSA Mohammed First University Oujda Morocco

ISBN: (纸本)9789819606436

In the realm of home energy management (HEM) system, the integration of demand response (DR) strategies with predictive modeling holds immense potential for optimizing consumption patterns. This article presents a comprehensive framework that combines reinforcement learning (RL) with predictive modeling using a Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) architecture. The CNN-LSTM model is employed to forecast photovoltaic (PV) power production and electricity prices, crucial factors influencing energy consumption decisions. The proposed approach considers various household appliance, including non-shiftable, power-shiftable, time-shiftable and Electric mobility devices, each represented as an autonomous agent, enabling decentralized decision-making tailored to specific operational constraints. By employing RL techniques, the model facilitates intelligent control, prioritizing the minimization of energy costs while simultaneously taking into account user comfort and appliance functionality. The simulation results validate the effectiveness and resilience of the proposed algorithm, showcasing a remarkable 36.7% reduction in electricity bills without compromising consumer satisfaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart homes

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Data-driven Modelling and Predictive Control for the Geometry and Mechanical Properties of Freeform Bending Processes 11

Data-Driven Modelling and Predictive Control for the Geometr...

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11th Vienna International conference on Mathematical Modelling (MATHMOD)

作者： Ismail, Ahmed Boehm, Viktor Stebner, Sophie Kong, Linghao Volk, Wolfram Muenstermann, Sebastian Lohmann, Boris Tech Univ Munich Sch Engn & Design Chair Automat Control Boltzmannstr 15 D-8570 Garching Germany Tech Univ Munich Sch Engn & Design Chair Met Forming & Casting Walther Meissner Str 4 D-85748 Garching Germany RWTH Aachen Unviers Integrityy Mat & Struct Intzestr 1 D-52072 Aachen Germany

Freeform bending is a technique used to bend different tube profiles into complex structures. In the realm of Aritificial Intelligence and Machine Learning, data-driven approaches in modeling and in optimization based control contribute to the job being executed, by increasing product quality and reducing energy consumption and material waste. In previous works, soft-sensors as well as different factors affecting the geometry and the residual stresses have been investigated and been utilized in a preliminary closed-loop control structure. Later on, this control structure has been replaced with a control strategy called Residual Strategy Algorithm, that could, based on previous deviations in the geometry, proactively react in order to reduce their effects. In this paper, the previously developed Residual Strategy Algorithm is extended to include both, the geometry as well as the residual stresses of the tube being bent. Also the modelling of both will be discussed altogether. The validation of the algorithm is done using simulation results. Copyright (c) 2025 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： Data-driven Modelling Predictive Control Free-form bending Residual Stresses Curvature Process optimization Soft sensors Closed-loop control Feed-forward control Gaussian Process Regression Neural Network Artificial Intelligence Machine Learning Cyber Physical Production Systems

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