检索结果-内蒙古大学图书馆

2024 ieee International conference on Big data, Bigdata 2024

作者： Norris, Aidan Abdeltawab, Hussein Wake Forest University Department of Engineering United States

ISBN: (纸本)9798350362480

Wind energy is one of the most prominent forms of renewable energy currently being implemented to decrease our reliance on fossil fuels and curb the emission rates of greenhouse gases. The growth of wind energy is challenged by the unpredictability of the wind and the power fluctuations accompanying variable resources. To account for the wind speed fluctuations, wind turbines have control systems to optimize the power generated. Turbine controllers are dependent on accurate measurements of the wind speed, usually made by devices called anemometers. Faults in anemometers can lead to suboptimal turbine performance and additional mechanical strain on the turbine. There are a wide range of methods employed towards the fault detection of anemometers, including data driven approaches, machine learning techniques, set value observations, and parameter estimation models. This paper proposes using knowledge of wind field and wake interactions at an entire wind farm to detect faults in anemometers by applying machine learning to simulated wake fields of wind farms. This approach was able to detect artificial errors of 5% and greater in wind speed readings with an accuracy of 94.1%. © 2024 ieee.

关键词： Anemometers

来源：评论

学校读者我要写书评

暂无评论

learning from similar systems and online data-driven LQR using iterative randomized data compression

Learning from similar systems and online data-driven LQR usi...

引用

European control conference (ECC)

作者： Kedia, Vatsal George, Sneha Susan Chakraborty, Debraj Indian Inst Technol Dept Elect Engn Mumbai Maharashtra India

ISBN: (纸本)9798331540920;9783907144107

The problem of data driven recursive computation of receding horizon LQR control through a randomized combination of online/current and historical/recorded data, is considered. It is assumed that large amounts of historical input-output data from a system, which is similar but not identical to the current system under consideration, is available. This (possibly large) data set is compressed through a novel randomized subspace algorithm to directly synthesize an initial solution of the standard LQR problem, which however is suboptimal due to the inaccuracy of the historical model. The first instance of this input is used to actuate the current system and the corresponding instantaneous output is used to iteratively resolve the LQR problem through a computationally inexpensive randomized rank-one update of the old compressed data. The first instance of the re-computed input is applied to the system at the next instant, output recorded and the entire procedure is repeated at each subsequent instant. As more current data becomes available, the algorithm learns automatically from the new data while simultaneously controlling the system in near optimal manner. The proposed algorithm is computationally inexpensive due to the initial and repeated compression of old and newly available data. Moreover, the simultaneous learning and control makes this algorithm particularly suited for adapting to unknown, poorly modeled and time varying systems without any explicit exploration stage. Simulations demonstrate the effectiveness of the proposed algorithm vs popular exploration/exploitation approaches to LQR control.

关键词： data compression

来源：评论

学校读者我要写书评

暂无评论

Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems With Markov Risk Measures

IEEE OPEN JOURNAL OF CONTROL SYSTEMS

引用

ieee OPEN JOURNAL OF control systems 2025年 4卷 70-82页

作者： Shao, Shiping Gupta, Abhishek Ohio State Univ Elect & Comp Engn Dept Columbus OH 43210 USA

We consider risk-sensitive Markov decision processes (MDPs), where the MDP model is influenced by a parameter which takes values in a compact metric space. These situations arise when the underlying dynamics of the system depend on parameters that drifts over time. For example, mass of a vehicle depends on the number of passengers in the vehicle, which may change from one trip to another. Similarly, the energy demand of a building depends on the local weather, which changes every hour of the day. We identify sufficient conditions under which small perturbations in the model parameters lead to small changes in the optimal value function and optimal policy. This is achieved by establishing the continuity of the value function with respect to the parameters. A direct consequence of this result is that an optimal policy under a specific parameter remains near-optimal if the parameter is perturbed slightly. Implications of the results for data-driven decision-making, decision-making with preference uncertainty, and systems with changing noise distributions are discussed.

关键词： Uncertainty Sufficient conditions Renewable energy sources Kernel Costs Mathematical models Random variables Perturbation methods Cost function Reinforcement learning Markov decision processes risk analysis robust control

来源：评论

学校读者我要写书评

暂无评论

Green HERO: Energy-Efficient Hierarchical Federated learning with Client Association in Industrial IoT

引用

ieee Internet of Things Magazine 2024年第5期7卷 30-36页

作者： Gong, Chenyu Ma, Mulei Zeng, Liekang Yang, Yang Wu, Liantao China East China Normal University China

The Industrial Internet of Things (IIoT) plays a pivotal role in advancing the automation and intelligence of industrial production processes. Its integration with various technologies is driving innovation in intelligent applications within industrial scenarios. Among these innovations, Federated learning (FL) stands out by offering a distributed training service for AI-driven applications in IIoT settings. However, the complexity of industrial control systems and the demands for intensive computing capabilities have raised concerns regarding energy consumption and carbon emissions. These concerns have prompted the need for the existing FL framework to evolve towards more sustainable and efficient practices. In this work, we propose a green distributed intelligence framework: Multi-Agent Deep Reinforcement learning (MA-DRL) based Hierarchical fEderated learning with client assOciation (HERO), which consists of three layers: the client layer, the edge layer (contains the Edge Server (ES) and the Edge Parameter Server (EPS)), and the cloud layer. This architecture enables two-layer aggregation, namely Edge-aggregation and Cloud-aggregation. The Edge-aggregation, in particular, can optimize data distribution and alleviate data heterogeneity difficulties, all of which contribute to an acceleration in model convergence speed and decrease training rounds. Eventually, it will lead to a reduction in energy consumption. Moreover, avoiding frequent data transmission between users and the cloud through adopting the computing resources at the edge can also lower communication energy consumption. In performance evaluation, we conduct extensive experiments on different datasets. The results demonstrate that the proposed HERO outperforms the baseline in energy consumption and training convergence rate. © 2018 ieee.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A data-driven Approach to MBTI Personality Classification: Insights from Machine learning Models 16

A Data-Driven Approach to MBTI Personality Classification: I...

引用

16th ieee International conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Dael, Fares A. Maidanova, Symbat Shayea, Ibraheem Abitova, Gulnara Seraly, Aigul İzmir Bakirçay University Management Information Systems İzmir Turkey Astana It University Computational and Data Science Astana Kazakhstan Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey Astana It University Intelligent Systems and Cybersecurity Astana Kazakhstan

ISBN: (纸本)9798331505264

The Myers-Briggs Type Indicator (MBTI) is one of the most widely recognized psychological tools for categorizing personality types, often used in various professional and personal development contexts. This study presents a data-driven approach to MBTI personality classification using a range of machine learning models. By leveraging a dataset comprising user responses and linguistic patterns, we aim to predict the MBTI personality types with greater accuracy and reliability. Various models, including Support Vector Machines (SVM), Random Forests, and Gradient Boosting Machines, were evaluated to determine their effectiveness in classifying the 16 MBTI types. Our findings reveal that machine learning models can significantly enhance the predictive accuracy of MBTI classification compared to traditional methods. The Random Forest model, in particular, demonstrated superior performance, achieving an accuracy of [insert specific accuracy here] across the dataset. We also explore the importance of feature selection and data preprocessing in improving model outcomes, highlighting key features that contribute to personality type prediction. The results of this study suggest that a data-driven approach, combined with machine learning techniques, provides a promising avenue for more nuanced and accurate MBTI personality assessments. This approach not only enhances our understanding of personality prediction but also offers practical implications for applications in psychology, human resources, and personal development. © 2024 ieee.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

The Transient Predictor 63

The Transient Predictor

引用

63rd ieee conference on Decision and control, CDC 2024

作者： Moffat, Keith Dorfler, Florian Chiuso, Alessandro Automatic Control Laboratory Zürich Switzerland University of Padova Dept. of Information Engineering Italy

ISBN: (纸本)9798350316339

This paper introduces the Transient Predictor and describes how it can be used to estimate the Multistep Predictor, which can be applied to applications such as data-driven Predictive control (DDPC). The Transient Predictor has two desirable traits that differentiate it from other methods for estimating the Multistep Predictor, such as the standard Subspace Predictor method: 1) Causality-the Transient Predictor asserts a causal relationship between future inputs and future outputs;and 2) Bias-the Transient Predictor is a consistent predictor of future outputs. This paper provides an easy-toimplement algorithm for estimating the Transient Predictor and in turn the Multistep Predictor, and demonstrates its efficacy for DDPC. In experiments, we find that the Transient Predictorbased DDPC performs remarkably well with small lead-in data lengths, indicating that it is well-suited for tasks in which large amounts of data are not available. In addition, the Transient Predictor is not afflicted by the same bias as subspace-based methods when data is gathered in closed loop. © 2024 ieee.

关键词： Electromagnetic transients

来源：评论

学校读者我要写书评

暂无评论

Reducing Cognitive Load in Teleoperating Swarms of Robots through a data-driven Shared control Approach

Reducing Cognitive Load in Teleoperating Swarms of Robots th...

引用

2024 International conference on Intelligent Robots and systems

作者： Turco, Enrico Castellani, Chiara Bo, Valerio Pacchierotti, Claudio Prattichizzo, Domenico Baldi, Tommaso Lisini Ist Italiano Tecnol Genoa Italy Univ Rennes IRISA Rennes INRIA CNRS Rennes France Univ Siena Dip Ingn Informaz & Sci Matemat Siena Italy

ISBN: (纸本)9798350377712;9798350377705

Multi-robot systems have gained increasing interest across various fields such as medicine, environmental monitoring, and more. Despite the evident advantages, the coordination of the swarm arises significant challenges for human operators, particularly concerning the cognitive burden needed for efficiently controlling the robots. In this study, we present a novel approach for enabling a human operator to effectively control the motion of multiple robots. Leveraging a shared control data-driven approach, we enable a single user to control the 9 degrees of freedom related to the pose and shape of a swarm. Our methodology was evaluated through an experimental campaign conducted in simulated 3D environments featuring a narrow cylindrical path, which could represent, e.g., blood vessels, industrial pipes. Subjective measures of cognitive load were assessed using a post-experiment questionnaire, comparing different levels of autonomy of the system. Results show substantial reductions in operator cognitive load when compared to conventional teleoperation techniques, accompanied by enhancements in task performance, including reduced completion times and fewer instances of contact with obstacles. This research underscores the efficacy of our approach in enhancing human-robot interaction and improving operational efficiency in multi-robot systems.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

data-driven Predictive control for a Class of Nonlinear systems with Polynomial Terms 4

Data-driven Predictive Control for a Class of Nonlinear Syst...

引用

4th IFAC conference on Modelling, Identification and control of Nonlinear systems (MICNON)

作者： Azarbahram, Ali Al Khatib, Mohammad Mishra, Vikas Kumar Bajcinca, Naim Rheinland Pfalz Tech Univ Kaiserslautern Landau Dept Mech & Proc Engn D-67663 Kaiserslautern Germany

This article proposes a non-parametric representation for a class of high-order unknown nonlinear systems comprising polynomial functions. A data-driven approach is introduced that uses pure data for future simulation based on any given input set. We also consider a comprehensive class of nonlinear autoregressive exogenous (NARX) models which covers other existing representations as special cases. Then, a non-parametric representation of this class of NARX models are investigated by leveraging the idea of re-expressing the system in the context of a non-minimal state-space reformulation. A data-driven predictive control (DDPC) approach is as a result introduced that uses the fundamental lemma for future prediction during the open-loop optimization. The recursive feasibility analysis and the proof of stability are also given. Finally, the simulation results quantify the effectiveness of the proposed approach in different scenarios. Copyright (c) 2024 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 predictive control (DDPC) data-driven simulation nonlinear systems behavioral systems theory NARX models Willems' fundamental lemma

来源：评论

学校读者我要写书评

暂无评论

Development of a Simulation-Based Solution Concept for AI-driven Clustering / Combination of Pick and Stow Operations to Improve Logistics Performance in SMEs 20

Development of a Simulation-Based Solution Concept for AI-Dr...

引用

ieee 20th International conference on Automation Science and Engineering (CASE)

作者： Schroth, Timo Hummel, Vera von Leipzig, Konrad Schuhmacher, Jan Reutlingen Univ ESB Business Sch Alteburgstr 150 D-72762 Reutlingen Germany Stellenbosch Univ Dept Ind Engn 145 Banghoek Rd ZA-7600 Stellenbosch South Africa

ISBN: (纸本)9798350358513;9798350358520

The industrial sector is evolving towards increased customization, diminishing batch sizes, and shorter product lifecycles, affecting intralogistics, which faces challenges in managing an expanding variety of parts and variants. This diversification leads to a decline in efficiency owing to the complexity in pick and stow operations, as traditional systems, digital solutions, and optimization methods mainly rely on historical data without incorporating near-real-time process information. Conventional approaches separate pick and stow operations in both process and workforce, culminating in extended process durations. Instead, data-driven AI-based methods offer a solution by clustering and combining pick and stow operations into optimized bundles, considering travel distance and time. The research employs AI algorithms to streamline picking and stowing, aiming to enhance logistics performance by reducing travel distance and time. Due to the absence of real data, a simulation-based procedure to generate synthetic test and training data is adopted. The real-world logistics system of the learning factory Werk150 is modeled in AnyLogic simulation software to carry out picking and stowing in a 3D warehouse layout. This database is leveraged to train an unsupervised machine learning model using the data analytics software TensorFlow by applying algorithms focused on clustering and combination. A comparative study of these algorithms is conducted to pinpoint optimal strategies for improving logistics performance. Future research will target this methodology, which will be enriched by experimental tests in Werk150 involving near-real-time data, practical investigations, and the use of real data to conclude with an analysis to validate the optimization strategies' effectiveness.

关键词： Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Training and Modeling with Privacy in Network data Using Machine Leaming

Training and Modeling with Privacy in Network Data Using Mac...

引用

2024 Innovations in Intelligent systems and Applications conference, ASYU 2024

作者： Baba, Onat Can Masazade, Engin Marmara University Electrical and Electronics Engineering Istanbul Turkey

ISBN: (纸本)9798350379433

Privacy concerns have become paramount in today's data-driven landscape, particularly with the widespread adoption of Internet of Things (IoT) devices. This paper explores the integration of machine learning algorithms, specifically Gaussian Naive Bayes, with privacy-preserving techniques like Differential Privacy to address these concerns. We delve into this innovative approach's principles, methods, and implications. Using a dataset representative of loT network traffic, we demonstrate how Gaussian Naive Bayes with Differential Privacy can effectively classify data while safeguarding individual privacy, highlighting its significance in pervasive connectivity and data-driven decision-making. © 2024 ieee.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：