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optimization of Playful Learning on the Unispher™ Platform by simulation modeling 8

Optimization of Playful Learning on the Unispher™ Platform ...

引用

8th International conference on Computational Linguistics and Intelligent Systems. Volume II: modeling, optimization, and Controlling in Information and Technology Systems Workshop, MOCITSW-CoLInS 2024

作者： Bisikalo, Oleg Bunyak, Yuriy Sofina, Olga Kvyetnyy, Roman Arie, David Vinnytsia National Technical University Vinnytsia Ukraine Spilna Sprava Company Vinnytsia Ukraine UnispherLtd RamatGan Israel

Education optimization of playful learning student's groups is considered. The goal of optimization is to form groups of students who will show high performance of education using game forms of learning. The optimization is based on the student's classification according to interest and learning ability, behavioral characteristics, character traits and social characteristics, as well as on predicting their individual and group performance, considering the mutual influence and work of teachers and mentors. The students are presented by vectors of their feature's numerical assessments. The student's classification is carried out using the method of Independent Component Analysis on a data driven basis obtained by Singular Value Decomposition. The method allows to classify students' data taking into account the significance of the parameters and an uncertainty of some parameter's values through their randomization. The method allows to select students with different learning abilities and behavioral characteristics for each group in order to create conditions for the coordinated work of the group. The method allows to distribute students with special characteristics evenly into groups. © 2024 Copyright for this paper by its authors.

关键词： Students

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Research on modeling Method of Digital Twin of Lithium Battery Based on Data-Model Hybrid Drive 4

Research on Modeling Method of Digital Twin of Lithium Batte...

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4th IEEE International conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Zhang, Yuxin Wu, Jianhua Sun, Shanxun Ye, Tao Energy and Electricity Research Center Jinan University Zhuhai China Guangdong Electrical and Intelligent Control Engineering Technology Research Center Institute of Rail Transportation Jinan University Zhuhai China

ISBN: (纸本)9798350359589

Lithium battery, as an important part of new energy storage, its scientific and safe use and management has become a key factor to achieve low-carbon technology. Lithium-ion battery is a highly nonlinear system, and its operating state is susceptible to environmental interference and other disturbances, resulting in errors. Aiming at the error caused by the disturbance of the running state of lithium battery, this paper proposes a method of digital twin modeling of lithium-ion battery which integrates the mechanism model and the data-driven model. The adaptive particle swarm optimization support vector machine regression (SVR) algorithm was used to conduct data-driven modeling for the errors between the mechanism model and the battery entity data, and the errors were corrected to obtain a real-time and high-precision battery model, which realized condition monitoring and offline simulation display. Moreover, the accuracy of the model was verified through experiments, proving that it can lay a foundation for the safe and intelligent management and control of the battery system. © 2024 IEEE.

关键词： data-driven digital twin lithium-ion battery mechanism model support vector machine regression

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Contrasting Two Methods Along the Spectrum of Geologic Concept vs Seismic-driven Approaches to Reservoir modeling: Case Study from a Deepwater Field, Offshore Nigeria

Contrasting Two Methods Along the Spectrum of Geologic Conce...

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2024 SPE Nigeria Annual International conference and Exhibition, NAIC 2024

作者： Anene, C.C. Nwachukwu, D.U. Oshuntuyi, T.O. Adebowale, B.T. Ndianefo, U.K. Adegbaju, A.S. Onyido, C.P. Dada, R.D. Olopade, B.A. Orupabo, B.A. ExxonMobil Technology and Engineering Company TX United States Esso Exploration and Production Nigeria Limited Lagos Nigeria

ISBN: (纸本)9781959025474

The approach taken to integrate static data in reservoir modeling, varies along a spectrum from purely concept driven on one end to data driven on the other. Different methods can be adopted along this spectrum with varying levels of data integration and control. The method adopted usually depends on several factors including time available for the study, quality of data available etc. In this study, we contrast two methods along this spectrum to show the pros and cons of both. The study uses data from a mature field in deep-water Nigeria with over 16 years of production from 11 oil producers and eight water injectors. Declining field production and a depleting opportunity inventory necessitated a comprehensive study of the field and building of integrated reservoir models. The results from the field study will be the basis for drill-well and workover opportunity generation/maturation, water injection optimization and other value-added field depletion optimization strategies. A dual pronged approach was adopted for the reservoir modeling: 1. A concept-driven approach involving representation of key reservoir architectural elements using object-based modeling (OBM) and use of qualitative seismic-conditioning to distribute key reservoir properties such as Net-To-Gross ratio, porosity etc. 2. A seismic data-driven approach involving direct sampling of seismic inversion-derived properties into the model grids. Two distinct reservoir models were built and taken through model initialization, history-matching, and flow simulation. The history-matched model scenarios will be instrumental in generating production forecasts for field management and for infill well opportunity generation/maturation. This dual pronged approach enabled comparison of both modeling methods in terms of quality of match to existing static and dynamic data, reservoir characterization cycle time and achievement of field study objectives. The key learnings from the application of these two methods in a

关键词： Well workover

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Optimizing Power Efficiency: Converting Recurrent Neural Networks to Spiking Neural Networks for Time-Domain Analysis 6

Optimizing Power Efficiency: Converting Recurrent Neural Net...

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6th International conference on Data-driven optimization of Complex Systems (DOCS)

作者： Xie, Bo Qiu, Jiarui Zhang, Fengzhao Li, Erping Huang, Shaoying Tan, Shurun Zhejiang Univ Univ Illinois Urbana Champaign Inst Hangzhou Peoples R China Singapore Univ Technol & Design Singapore Singapore Zhejiang Univ Hangzhou 314400 Zhejiang Peoples R China

ISBN: (纸本)9798350377859;9798350377842

The deep learning method has recently been used in the modeling of high-speed links to speed up the transient analysis procedure in the time domain. However, neural networks cost a lot of power. Spiking Neural Networks (SNNs) have garnered significant academic interest owing to their distinct characteristic of low power consumption. In this paper, we collected data from circuit to train an RNN for accelerating the transient analysis procedure, then we used an ANN-SNN conversion method to get an SNN. This paper effectively addresses the issue of precision degradation caused during the normalization. By retraining the normalized recurrent neural network (RNN) to map the RNN rules, its predictive performance was restored to that of the RNN model prior to normalization. Our SNN model demonstrated excellent predictive performance while achieving a 294.6 times reduction in power consumption compared to the RNN when test on a Lynxi chip HP201.

关键词： RNN spiking neural network cascade signal integrity time-domain channel simulation

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Design optimization for Bellow Soft Pneumatic Actuators in Shape-Matching

Design Optimization for Bellow Soft Pneumatic Actuators in S...

引用

IEEE International conference on Soft Robotics (RoboSoft)

作者： Yao, Yao Chen, Yuwen He, Liang Maiolino, Perla Univ Oxford Oxford Robot Inst Oxford OX1 2JD England Univ Oxford Dept Engn Sci Oxford OX1 3PJ England

ISBN: (纸本)9798350332223

Design optimization of soft actuators is essential for task-oriented applications. Models derived from analytical solutions, the Finite Element Method (FEM), or empirical characterized datasets are widely used to estimate the response of the actuators during actuation, acting as the backbone for design optimization. Faced with the trade-off between speed and accuracy, substantial challenges occur when moving from simulation to optimization due to the compliant, high degree of freedom, and high-dimensional design space of the soft-bodied robot. FEM becomes increasingly computationally expensive with increased design complexity during optimization iterations, while the data-driven modeling approach (e.g., Artificial Neural Network) consumes significant resources prior to optimization. To address the challenge of highly nonlinear and non-convex design optimization in soft robots using the black box modeling, this paper compares of Bayesian optimization (BO) algorithm and genetic algorithm (GA) with FEM and Artificial Neural Network (ANN) models. The shape-matching of a multi-legged robot (a starfish) is demonstrated as an example of a task-oriented design scenario that presents design optimization challenges of the design space scalability. The experimental results show that the bi-level BO outperforms BO with FEM by achieving 2.8 to 9.8 times smaller objective values within a certain time for low-dimensional design problems;GA with the ANN model can achieve lower objective values 3 to 18 times faster in high-dimensional design problems than bi-level BO with FEM in low-dimensional design problems.

关键词： Pneumatic actuators Computational modeling Scalability Artificial neural networks Soft robotics Finite element analysis Task analysis

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Numerical simulation Study on Folding Behavior of Paper and Structural optimization of End-Effector 4

Numerical Simulation Study on Folding Behavior of Paper and ...

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4th International conference on Applied Mathematics, modeling and Computer simulation, AMMCS 2024

作者： Hong, Jie Zhou, Xuanli Wang, Gang Zhou, Kui Ren, Ziwen Shanghai Tobacco Machinery Co. Ltd. Shanghai China

ISBN: (纸本)9781643685519

The study of paper folding behavior is a key topic in the field of packaging. Due to the complex mechanical properties of paper, its deformation under different working conditions is difficult to predict. If the mechanical structure design is unreasonable, it will directly lead to the failure of the packaging box to fold. Based on the actual packaging process, this paper creatively establishes a set of simulation models suitable for predicting the folding effect of paper boxes driven by different mechanical structures. Using this model, the influence of the rebound angle of the lower edges of the box on the folding effect of the box was studied. The simulation results show that the original mechanical structure can adapt to a rebound angle within 7.5°. We then optimized the structure of the existing end-effector and proposed three new mechanical structures. The simulation results show that the third structure can adapt to rebound angle within 15°, which greatly improves the adaptability of packaging equipment and the success rate of packaging. This research can provide a strong basis for predicting the folding effect of paper and designing the packaging mechanical structure. © 2024 The Authors.

关键词： Structural optimization

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Computationally Efficient Data-driven MPC for Agile Quadrotor Flight

Computationally Efficient Data-Driven MPC for Agile Quadroto...

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American Control conference (ACC)

作者： Choo, Wonoo Kayacan, Erkan Univ Oklahoma Sch Aerosp & Mech Engn Norman OK 73019 USA

ISBN: (纸本)9798350328066

This paper develops computationally efficient data-driven model predictive control (MPC) for Agile quadrotor flight. Agile quadrotors in high-speed flights can experience high levels of aerodynamic effects. modeling these turbulent aerodynamic effects is a cumbersome task and the resulting model may be overly complex and computationally infeasible. Combining Gaussian Process (GP) regression models with a simple dynamic model of the system has demonstrated significant improvements in control performance. However, direct integration of the GP models to the MPC pipeline poses a significant computational burden to the optimization process. Therefore, we present an approach to separate the GP models to the MPC pipeline by computing the model corrections using reference trajectory and the current state measurements prior to the online MPC optimization. This method has been validated in the Gazebo simulation environment and has demonstrated of up to 50% reduction in trajectory tracking error, matching the performance of the direct GP integration method with improved computational efficiency.

关键词： Model predictive control

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Towards Agent-based Disease Spread modeling Combining Knowledge-driven simulation and Machine Learning

Towards Agent-based Disease Spread Modeling Combining Knowle...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Gunder, Maurice Ispizua Y, Facundo R. Mahlein, Anne-Katrin Bauckhage, Christian University of Bonn Institute for Computer Science Bonn Germany Beet Research IfZ Institute for Sugar Göttingen Germany

ISBN: (纸本)9798350362480

Thanks to the progress in precision agriculture and remote sensing, automatic visual disease severity scoring of largescale fields is getting more efficient. This potentially allows the investigation and observation of disease spread dynamics and infection incidence in real-world field trials. Furthermore, phytopathological research has been obtaining comprehensive knowledge about pathogens and epidemiology for decades. In this work, we want to leverage modern data-driven Machine Learning (ML) methods and connect them with available knowledge incorporated by simulation techniques. As a result, we show how to create infection datasets from disease severity annotations and environmental data that can serve as training data for a transformer-based ML model. First preliminary results show coherent, interpretable results that motivate for further optimization and improvements of our modeling strategy. © 2024 IEEE.

关键词： Adversarial machine learning

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User-centered configuration of soft hip flexion exosuit designs to assist individuals with multiple sclerosis through simulated human-in-the-loop optimization

User-centered configuration of soft hip flexion exosuit desi...

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International conference on Rehabilitation Robotics (ICORR)

作者： Neuman, Ross M. Fey, Nicholas P. Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA

ISBN: (纸本)9798350342758

Soft exosuits hold promise as assistive technology for people with gait deficits owing to a variety of causes. A key aspect of providing useful assistance is to keep the human user at the center of all considerations made in the design, configuration, and prescribed use of an assistive device. This work details a method for informing the configuration of a soft hip flexion exosuit by 1) modeling the user's shape and movements in order to simulate the mechanical interaction of the exosuit and user, 2) incorporating the mechanical effects of the exosuit into a muscle-driven musculoskeletal gait simulation, and 3) using the results of these simulations to define a cost function that is minimized via Bayesian optimization. This process is carried out for models of four different people with multiple sclerosis, and the final optimized configurations for each subject are compared. For all users, the estimated metabolic cost of transport was reduced below baseline, no-device levels. This work represents a step toward more individualized, user-centric modeling of assistive devices, and demonstrates a system for informing the physical configuration of an exosuit on a case-by-case basis using real patient data.

关键词： Cost functions

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Advanced Energy Flow and Power Quality Management in a Wind-PV-Battery Microgrid: AI-driven Modelling and Control

Advanced Energy Flow and Power Quality Management in a Wind-...

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2024 IEEE International conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Pandey, Amit Kumar Tiwari, P. Nishad, D.K. MMMUT Electrical Engineering UP Gorakhpur India

ISBN: (纸本)9798350385199

This paper presents a comprehensive Analysis of artificial intelligence (AI) techniques for advanced energy flow and power quality management in wind-PV-battery microgrids. The study compares optimization, machine learning, and multi-agent approaches for microgrid energy management and explores AI-driven modeling of wind turbines, PV systems, and batteries to capture complex nonlinearities and uncertainties. AI-based control strategies for voltage and frequency regulation, harmonic mitigation, and reactive power compensation are investigated, highlighting their strengths and limitations. The paper identifies research gaps and future directions, emphasizing the need for scalable, secure, and resilient AI systems for large-scale microgrid energy management. Hybrid AI methods incorporating optimization, learning, and multi-agent approaches are recommended. The study concludes that AI enables intelligent, adaptive, and autonomous operation of renewable-rich microgrids, contributing to a more sustainable and reliable energy future. simulation results demonstrate the effectiveness of AI-based control techniques in improving power quality and energy management in microgrids. © 2024 IEEE.

关键词： Windmill

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