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Application and optimization of virtual simulation teaching assisted by artificial intelligence in ideological and political courses in universities

Application and optimization of virtual simulation teaching ...

引用

2024 International conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Zhu, Yueke Wen, Mengnan Li, Yi Shantou University Marxist Academy Shantou University Guangdong Province Shantou City China Shantou University School of Business Shantou University Guangdong Province Shantou City China

ISBN: (纸本)9781510691797

With the rapid progress of information technology, the application of artificial intelligence algorithmand virtual simulation technology in the field of education has become more and more in-depth, especially in higher education, and these technologies have brought new teaching means and possibilities to the traditional ideological and political courses(IPCs). This study adopts the methods of case study and experimental design to analyze the learning effect of students and the changes in teaching interaction by introducing the virtual simulation teaching module based on artificial intelligence in the IPCs of several *** include: building an immersive virtual scene to enhance students' understanding of historical events and social phenomena;Personalized recommendation of learning content by AI algorithm to improve teaching effect;Applying natural language processing technology to realize intelligent question answering;Using affective analysis technology to evaluate students' feedback and optimize teaching *** application of artificial intelligence-driven simulation and modeling technology in ideological and political courses in colleges and universities can enhance the interaction and practicality of teaching, optimize students' thinking mode, and enhance the attraction and educational effect of the *** specific implementation process includes developing virtual simulation software suitable for the course content, tracking and analyzing students' learning behaviors using artificial intelligence technology, and adjusting teaching strategies based on data feedback. Students and teachers in the experimental group showed high acceptance of AI-assisted virtual simulation teaching technology. In terms of satisfaction scores, students and teachers in the experimental group generally give high scores of 4.0 or more, and the results of the study show that this new teaching mode can improve students' interest and participation in learning, d

关键词： Artificial Intelligence Artificial Intelligence Algorithms Civics Courses in Colleges Teaching Strategies Virtual simulation Teaching

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Machine Learning Enabled Directly Modulated Lasers

Machine Learning Enabled Directly Modulated Lasers

引用

International conference on Transparent Optical Networks

作者： Sergio Hernandez Christophe Peucheret Francesco Da Ros Darko Zibar DTU Fotonik Lyngby Denmark

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

The complex dynamics of systems based on directly modulated lasers (DMLs) can hinder their optimization, as their simulation entails numerical methods where gradient calculation is not readily available. Our proposed solution combines data-driven DML modeling with end-to-end (E2E) learning techniques to obtain optimal link configurations.

关键词： Optical fiber networks Laser modes optimization Numerical models Machine learning

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A Middleware for Digital Twin-Enabled Flying Network simulations Using UBSim and UB-ANC 18

A Middleware for Digital Twin-Enabled Flying Network Simulat...

引用

18th Annual International conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Moorthy, Sabarish Krishna Harindranath, Ankush McManus, Maxwell Guan, Zhangyu Mastronarde, Nicholas Bentley, Elizabeth Serena Medley, Michael Univ Buffalo Dept Elect Engn Buffalo NY 14260 USA Air Force Res Lab AFRL Rome NY 13440 USA

ISBN: (纸本)9781665495127

Data-driven control based on AIIML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSneker for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

关键词： Terms Unmanned Aerial Vehicle (UAV) Multi-Fidelity simulation UBSim UB-ANC Data-driven Control.

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simulation and optimization-Based Planning of the Use of Tank Containers in the Production of Specialty Chemicals 24

Simulation and Optimization-Based Planning of the Use of Tan...

引用

Proceedings of the Winter simulation conference

作者： Maximilian Kiefer Patrick Buhle Uwe Clausen Graduate School of Logistics TU Dortmund University Dortmund GERMANY Institute of Transport Logistics TU Dortmund University Dortmund GERMANY

ISBN: (纸本)9798331534202

Changing market conditions in the chemical industry are leading to an increased demand for fast and individually engineered chemicals. This results in a decline in mass production towards producing small, demand-driven quantities. A combination of changing demand and the need for short-term adjustments requires flexible production planning and logistics. To ensure logistics flexibility, primarily manual processes are used. However, this comes with the risk of direct contact between humans and chemicals. One way to avoid this contact and enable sufficient flexibility is to use tank containers directly connected to the production plants. This paper aims to develop a framework that combines simulation and optimization for planning the use of tank containers. Based on this framework, tank container storage materials will be selected using optimization. Furthermore, simulation helps to evaluate the influences of the selection on the logistics system. Here, the focus is on the management of general cargo containers.

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Predictive Control of a Morphing Energy-Harvesting Kite

Predictive Control of a Morphing Energy-Harvesting Kite

引用

IEEE conference on Control Technology and Applications (CCTA)

作者： Fine, Jacob B. Reed, James Naik, Kartik Vermillion, Chris North Carolina State Univ Raleigh NC 27695 USA North Carolina State Univ Dept Mech & Aerosp Engn Raleigh NC 27695 USA Altaeros Energies Inc Somerville MA USA Windlift Inc Durham NC USA

ISBN: (数字)9781665473385

ISBN: (纸本)9781665473385

In this work, we present a predictive control strategy, along with associated data-driven simulation results, for a morphing-wing energy-harvesting kite. The kite under consideration is capable of adjusting its tether length to seek the optimal wind environment within a spatiotemporally varying field, while actively adjusting its wing geometry based on the tether length, wind conditions, and energetic cost of morphing. Specifically, the kite deploys a two-step optimization process. First, power-optimal tether length, elevation, and aspect ratio setpoints are computed based on a quasi-static model. After those setpoints are computed, they are used to formulate terminal constraints within a model predictive control (MPC) formulation that prescribes rates of change to these variables over a receding horizon, accounting for a simplified dynamic model and morphing power requirements. To evaluate the morphing kite concept and associated predictive controller, simulations were performed based on spatiotemporally varying flow data obtained from observed boundary layer Doppler wind profile readings. In presenting the simulation results, the developed algorithm is compared to an impatient "optimum chasing" algorithm that changes the kite parameters as quickly as possible, without regard to the morphing power requirements that are accounted for within the MPC optimization. With the two-step optimization algorithm, a 25.53% increase was seen over the impatient "optimum chasing" algorithm.

关键词： Geometry Adaptation models Computational modeling simulation Heuristic algorithms Predictive models Prediction algorithms

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A Novel Decision-Making Framework for Waterflooding optimization using Long and Short-Term Memory Models and Metaheuristics

A Novel Decision-Making Framework for Waterflooding Optimiza...

引用

2023 SPE Latin American and Caribbean Petroleum Engineering conference, LACP 2023

作者： Castelblanco, A. X. Rodriguez Gildin, E. Cabrales, S.A. Medaglia, A.L. Industrial Engineering Universidad de Los Andes Bogotá Colombia Petroleum Engineering Texas A&M University College StationTX United States

ISBN: (纸本)9781613999905

Meeting future energy demands in the low-carbon emissions paradigm requires improved decision-making processes. Waterflooding plays a vital role in obtaining increased oil recovery factors andminimizing undesirable water production. However, waterflooding optimization involves costly wellcontrol management optimization methods especially when numerical simulation is used. Alternatives suchas data-driven proxy modeling can overcome the computation complexity drawbacks. In this paper, wedevelop a decision-making waterflooding framework, where an optimization component has embeddedfinancial and machine learning models, to establish the wells operational plan obtaining the maximum profitand the best oilfield management. In this work, we use a reduced-order predictive numerical model to generate synthetic data to trainmachine learning algorithms to be used in the optimization framework. We develop our methodology tofind the optimal strategy to drive a waterflooding project using a black-oil reservoir model. We implementthe proper orthogonal decomposition (POD)-based model reduction to evaluate the reservoir dynamics andcalculate the historical fluid production based on an operational plan, reducing the time consumption anddemand for computational resources. Based on these results, we train and test machine learning models topredict oil and water production rate for each well (output data) in which the operational wells' constraintschange over time (input data), and select which of them has higher accuracy in the forecast. We evaluate theLSTM (long short-term memory) which are focusing in time series forecasting, using a multivariate model,analyzing Vanilla, Stacked and Bidirectional. Lastly, iteratively, the LSTM selected are embedded into anon-linear optimization component to define the best operational strategy in an oilfield with waterflooding,considering the reservoir's physics and a financial evaluation in a short- to mid-term planning horizon. The proposed making d

关键词： Long short-term memory

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Data-driven Prediction and Predictive Control Methods for Eco-Driving in Production Vehicles 2nd

Data-Driven Prediction and Predictive Control Methods for Ec...

引用

2nd modeling, Estimation and Control conference (MECC)

作者： Baby, Tinu Vellamattathil Sotoudeh, Seyedeh Mahsa HomChaudhuri, Baisravan Illinois Inst Technol Chicago IL 60616 USA

This paper presents a study of perception and robust model-predictive control (MPC) strategies in realistic traffic environments, which are simulated using data from real-world driving experiments. In this paper, we consider a heterogeneous traffic environment, which includes human-driven vehicles, and study the performance of currently available automation in production vehicles. We then present a data-driven preceding vehicle's velocity and position prediction algorithm, and a robust MPC strategy that optimizes fuel consumption and takes into account the prediction errors. Data used in this paper are taken from experiments using a 2018 Cadillac CT6 vehicle. simulation results show up to 6.39% energy efficiency improvement. Copyright (c) 2022 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： Autonomous Vehicles Motion control Optimal control theory modeling for control optimization Learning and adaptation in autonomous vehicles.

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Knowledge-Guided Data-driven Decision-Making for Key Operational Variables in Sinering Processes 49

Knowledge-Guided Data-Driven Decision-Making for Key Operati...

引用

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

作者： Fang, Yijing Gui, Weihua Jiang, Zhaohui Zhu, Jilin Pan, Dong Central South University School of Automation Changsha China

ISBN: (纸本)9798350331820

Process modeling and intelligent decision-making play crucial roles in optimizing and controlling sintering processes. To tackle these tasks, we propose a novel approach that leverages a temporal convolutional network guided by working condition knowledge (WKGA-TCN) to predict the sintering condition. By incorporating sequential features, the model effectively captures the varying importance of different features. Subsequently, we simulate the state of the sintering process using our proposed method. To further enhance operational optimization, we introduce an intelligent decision-making strategy that combines dynamic matching of operating modes and a genetic algorithm. This strategy leverages historical data to learn from excellent operation cases while considering the inherent uncertainty in process data. Simultaneously, a model update strategy employing an event-triggered mechanism is introduced to guarantee the process model's comprehensive simulation of the current sintering process dynamics. Finally, we assess the effectiveness of the proposed method by applying it to actual industrial process data. © 2023 IEEE.

关键词： Decision making

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Early-Phase Performance-driven Design Using Generative Models 19th

Early-Phase Performance-Driven Design Using Generative Model...

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19th International conference on Computer-Aided Architectural Design (CAAD) - Design Imperatives - The Future is Now

作者： Ampanavos, Spyridon Malkawi, Ali Harvard Grad Sch Design Cambridge MA 02138 USA Harvard Ctr Green Bldg & Cities Cambridge MA 02138 USA

ISBN: (纸本)9789811912801;9789811912795

Current performance-driven building design methods are not widely adopted outside the research field for several reasons that make them difficult to integrate into a typical design process. In the early design phase, in particular, the time intensity and the cognitive load associated with optimization and form parametrization are incompatible with design exploration, which requires quick iteration. This research introduces a novel method for performance-driven geometry generation that can afford interaction directly in the 3d modeling environment, eliminating the need for explicit parametrization, and is multiple orders faster than the equivalent form optimization. The method uses Machine Learning techniques to train a generative model offline. The generative model learns a distribution of optimal performing geometries and their simulation contexts based on a dataset that addresses the performance(s) of interest. By navigating the generative model's latent space, geometries with the desired characteristics can be quickly generated. A case study is presented, demonstrating the generation of a synthetic dataset and the use of a Variational Autoencoder (VAE) as a generative model for geometries with optimal solar gain. The results show that the VAE-generated geometries perform on average at least as well as the optimized ones, suggesting that the introduced method shows a feasible path towards more intuitive and interactive early-phase performance-driven design assistance.

关键词： Performance driven design Machine learning Generative model

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Unconventional Surrogate-Assisted Approaches to EM-driven Antenna Design. modeling and optimization: Global, Multi-Objective, Statistical

Unconventional Surrogate-Assisted Approaches to EM-Driven An...

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European conference on Antennas and Propagation, EuCAP

作者： Slawomir Koziel Anna Pietrenko-Dabrowska Engineering Optimization & Modeling Center Reykjavik University Reykjavik Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

Design of contemporary antenna systems relies heavily on full-wave electromagnetic (EM) simulation tools. EM analysis ensures reliability but tends to be computationally expensive, which becomes a serious issue in the context of design automation. In recent years, utilization of surrogate modeling methods has been fostered to alleviate the cost-related difficulties. Yet, the existing procedures typically follow a few major approaches, e.g., different architectural variations of deep neural networks, hyper-parameter/model adjustments in Bayesian optimization, or more or less standard machine learning frameworks. This paper reviews several unconventional surrogate-assisted techniques that employ less common algorithmic tools such as the response feature technology, domain confinement, dimensionality reduction, or supplementary inverse predictors. We demonstrate how these tools can be incorporated into practical procedures for global and multi-criterial optimization, statistical analysis, and design-oriented behavioral modeling. A discussion of the algorithm operating principles is supplemented by antenna design cases.

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