检索结果-内蒙古大学图书馆

63rd ieee conference on Decision and control, CDC 2024

作者： Zhang, Yi Mikelsons, Lars University of Augsburg Chair of Mechatronics Faculty of Applied Computer Science Germany

ISBN: (纸本)9798350316339

Inverse modeling is the process uncovering the relationships from the system observations to its inputs. It is essential in various fields such as control, robotics, and signal processing. We propose an inverse modeling method using amortized variational inference based on conditional normalizing flows (IMFlow). IMFlow is data-driven and can therefore be applied to black-box environments with limited observability and unknown complexity. Besides, the probabilistic modeling characteristics of conditional normalizing flows allow IMFlow to cope with unknown system uncertainties. We deploy IMFlow as a probabilistic model predictive controller, which estimates the control inputs as stochastic processes based on reference signals and system responses. In addition, we also adjust IMFlow to an online model-free reinforcement learning setting. We demonstrate our proposed method achieves the same accuracy in comparison to the standard model predictive control method using white-box models. © 2024 ieee.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Improved Virtual Sample Generation Method Based on Conditional Regions Denoising Diffusion Probabilistic Model for Soft Sensors with Small data 13

Improved Virtual Sample Generation Method Based on Condition...

引用

13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Mo, Wei-Tao Zhu, Qun-Xiong Song, Xiao-Lu He, Yan-Lin Xu, Yuan College of Information Science and Technology Beijing100029 China Ministry of Education of China Engineering Research Center of Intelligent Pse Beijing100029 China

ISBN: (纸本)9798350361674

Soft sensors have excellent adaptability to complex environments and are widely used in complex industrial processes. However, its construction relies on high-quality samples, which poses a challenge to construct reliable soft sensors. To solve this challenge, this paper proposes a conditional regions denoising diffusion probabilistic model-based virtual sample generation (CRDDPM-VSG) method. It aims to make the generated samples better to fill the low-density regions. In CRDDPM-VSG, density-based spatial clustering with noise application (DBSCAN) is used to find low-density samples. Then, the improved CRDDPM is used to generate samples in the specified low-density sample regions. Finally, the generated samples are added to the original samples to enrich the sample information, thus improving the performance of the soft sensor. We used two sets of data to validate the performance of CRDDPM-VSG. Results show that CRDDPM-VSG not only generates samples that match the original distribution, but also better boosts the prediction ability of the soft sensor. © 2024 ieee.

关键词： Virtual environments

来源：评论

学校读者我要写书评

暂无评论

Anti-Windup Active Power control for Wind Turbines Considering Loads Reduction 13

Anti-Windup Active Power Control for Wind Turbines Consideri...

引用

13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Wang, Guozhong Luo, Hao Jiao, Xuguo Yang, Zhibo Zhao, Xinxin School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

In recent years, the proportion of wind power in electricity contribution is increasing, and wind power generation technology has developed rapidly. How to enhance the stability of wind turbine systems and extend their service life are still challenging problems due to uncertain and intermittent characteristics of natural wind. In order to ensure that variable speed wind turbines track the active power instructions released by the power system, an improved anti-windup rotor speed control strategy is designed to switch different control strategies according to the wind turbine rotor speed value, with the aim of optimizing the mechanical loads of pitch systems. Firstly, in the low wind speed region, the maximum wind energy capture is achieved by optimal torque control. When the rotor speed increases to a critic value, torque control determined by the turbine's rated output power and rotor speed is adopted. In the high wind speed region, the pitch controller is used to adjust the wind energy capture coefficient to maintain the rotor speed at its rated value. Moreover, in order to solve the problem of integral saturation in traditional rotor speed control methods, this paper proposes a bounded constraints assisted Proportional-Integral (PI) pitch angle control scheme to optimize the operation of pitch system. This control method can effectively eliminates integral saturation, mitigate fatigue loads of wind turbines, reduces overshoot of rotor speed and ensures safe and stable operation of wind turbines. Finally, the proposed method is simulated on OpenFAST platform to verify its advancement. © 2024 ieee.

关键词： Wind turbines

来源：评论

学校读者我要写书评

暂无评论

data-driven Methods for Travel Time Estimation: A Survey 26

Data-driven Methods for Travel Time Estimation: A Survey

引用

ieee 26th International conference on Intelligent Transportation systems (ITSC)

作者： Zheng, Zhipeng Ye, Yongchao Zhu, Yuanshao Zhang, Shiyao Yu, James J. Q. Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Univ York Dept Comp Sci York N Yorkshire England

ISBN: (纸本)9798350399462

Travel time estimation is a crucial component of intelligent transportation systems, affecting various applications such as navigation, ride-hailing, and route planning. Traditional methods for travel time estimation rely on subjective judgments, limited data sources, and straightforward modeling techniques. Owing to recent advances in data mining and machine learning, numerous data-driven methods are adopted to address the problem that occurred in traditional schemes, which demonstrate exceptional performance. In this paper, we present a comprehensive survey of data-driven methods for travel time estimation, encompassing application scenarios, spatial-temporal modeling approaches, and data representation learning techniques. To support and promote further research in this field, we provide a valuable list of open data sources and source codes, offering researchers a solid foundation for their future endeavors. Furthermore, this survey discusses emerging trends and key challenges faced by the research community, such as the integration of real-time data streams and the use of uncertainty estimation. We also explore the potential impact of these advancements on transportation systems, highlighting opportunities for improvement and innovation. To the best of our knowledge, this work is among the first to offer a comprehensive, in-depth review of data-driven methods for travel time estimation, providing researchers and practitioners with a valuable reference in the field.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

A data-driven Approach to Geometric Modeling of systems with Low-Bandwidth Actuator Dynamics

A Data-Driven Approach to Geometric Modeling of Systems with...

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Deng, Siming Liu, Junning Datta, Bibekananda Pantula, Aishwarya Gracias, David H. Nguyen, Thao D. Bittner, Brian A. Cowan, Noah J. Johns Hopkins Univ Lab Computat Sensing & Robot Baltimore MD 21218 USA Johns Hopkins Univ Dept Mech Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD 21218 USA Johns Hopkins Univ Appl Phys Lab Laurel MD 97331 USA

ISBN: (纸本)9798350384581;9798350384574

It is challenging to perform system identification on soft robots due to their underactuated, high-dimensional dynamics. In this work, we present a data-driven modeling framework, based on geometric mechanics (also known as gauge theory) that can be applied to systems with low-bandwidth control of the system's internal configuration. This method constructs a series of connected models comprising actuator and locomotor dynamics based on data points from stochastically perturbed, repeated behaviors. By deriving these connected models from general formulations of dissipative Lagrangian systems with symmetry, we offer a method that can be applied broadly to robots with first-order, low-pass actuator dynamics, including swelling-driven actuators used in hydrogel crawlers. These models accurately capture the dynamics of the system shape and body movements of a simplified swimming robot model. We further apply our approach to a stimulus-responsive hydrogel simulator that captures the complexity of chemomechanical interactions that drive shape changes in biomedically relevant micromachines. Finally, we propose an approach of numerically optimizing control signals by iteratively refining models, which is applied to optimize the input waveform for the hydrogel crawler. This transfer to realistic environments provides promise for applications in locomotor design and biomedical engineering.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

IoT-driven Enhancement of Hydroponic Fertilization Efficiency Through Machine learning: A data-Centric Strategy 2

IoT-Driven Enhancement of Hydroponic Fertilization Efficienc...

引用

2nd International conference on Intelligent Cyber Physical systems and Internet of Things (ICoICI)

作者： Patel, Juhi Bhatt, Tejaskumar Joshi, Aditi GLS Univ Fac Comp Applicat & Informat Technol Ahmadabad Gujarat India GLS Univ Fac Engn & Technol Ahmadabad Gujarat India

ISBN: (纸本)9798331540661;9798331540678

Hydroponic systems offer a controlled environment for plant growth, where precise nutrient management is crucial for maximizing yields. This study integrates IoT sensors with machine learning algorithms to optimize nutrient composition-focusing on Nitrogen (N), Phosphorus (P), and Potassium (K)-in hydroponic setups. By leveraging data collected from IoT sensors, the machine learning models predict optimal nutrient levels, thereby improving nutrient use efficiency, plant growth rate, and overall yield. The results show that the IoT-enabled precision fertilization system increased nutrient use efficiency by 25%, enhanced plant growth rate by 20%, and boosted yield by 15% compared to traditional nutrient management methods. Additionally, the system's ability to provide real-time adjustments and remote monitoring minimizes human intervention and reduces nutrient wastage. This research highlights the transformative potential of data-driven approaches in controlled-environment agriculture, offering a sustainable path to significantly enhancing productivity. Future work will focus on expanding datasets and incorporating more advanced IoT sensors and machine learning algorithms to further refine the system's accuracy and adaptability.

关键词： Hydroponics IoT sensors Machine learning Nutrient optimization Plant growth Yield improvement Precision agriculture

来源：评论

学校读者我要写书评

暂无评论

Deep Reinforcement learning for Intelligent Transportation systems: A Survey

引用

ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第1期23卷 11-32页

作者： Haydari, Ammar Yilmaz, Yasin Univ S Florida Dept Elect Engn Tampa FL 33620 USA Univ Calif Davis Dept Elect & Comp Engn Davis CA 95616 USA

Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (RL) based traffic control applications are surveyed. Specifically, traffic signal control (TSC) applications based on (deep) RL, which have been studied extensively in the literature, are discussed in detail. Different problem formulations, RL parameters, and simulation environments for TSC are discussed comprehensively. In the literature, there are also several autonomous driving applications studied with deep RL models. Our survey extensively summarizes existing works in this field by categorizing them with respect to application types, control models and studied algorithms. In the end, we discuss the challenges and open questions regarding deep RL-based transportation applications.

关键词： Machine learning Autonomous vehicles control systems Markov processes Deep reinforcement learning intelligent transportation systems traffic signal control autonomous driving multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Comprehensive data-driven Framework for Detecting and Classifying Non-Technical Distribution Losses

引用

ieee ACCESS 2024年 12卷 96537-96551页

作者： Jene-Vinuesa, Marc Aragues-Penalba, Monica Sumper, Andreas Univ Politecn Cataluna Ctr Innovacio Tecnol Convertidors Estat Accionamen Dept Engn Elactr ETS Engn Ind Barcelona Barcelona 08028 Spain

Non-technical losses (NTL) pose multiple challenges across distribution grids. This paper introduces a comprehensive framework combining data-driven methods to detect and categorize fraud due to meter tampering or direct connections while identifying potential culprits. The hybrid methodology utilizes grid and consumer-related data to obtain NTL curves through an energy balance approach, yielding indicators such as magnitude, duration, and other features. A Random Forest classifier trained with real historical cases of NTL achieves a weighted F1 score of 0.859, effectively labelling fraud types. Additionally, an unsupervised detection model, integrating clustering and correlation methods, enables accurate identification of tampered meters. The paper introduces two adjustable parameters enabling utilities to fine-tune meter tampering detection strategies based on economic considerations. The results demonstrate that true positives can be increased at the expense of increasing false positives. Accurate fraud identification is achieved using the Fuzzy C-Means algorithm, with an F1 score of 0.9. The algorithm is tested on grids with distributed generation, with a decrease of 10% on the predictive performance when half of the users are prosumers, demonstrating the methodology's promising performance in real-world scenarios.

关键词： Fraud Classification algorithms Meters Voltage control Support vector machines Hardware Clustering algorithms Unsupervised learning Classification direct connection false data injection (FDI) fraud detection hybrid algorithm inspection strategies meter tampering non-technical losses (NTL) unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study 63

Meta-learning of data-driven controllers with automatic mode...

引用

63rd ieee conference on Decision and control, CDC 2024

作者： Busetto, Riccardo Breschi, Valentina Baracchi, Federica Formentin, Simone Politecnico di Milano Dip. di Elettronica Informazione e Bioingegneria Milano Italy Eindhoven University of Technology Dept. of Electrical Engineering Eindhoven Netherlands

ISBN: (纸本)9798350316339

data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming. Nonetheless, many of these algorithms are not entirely automated, often necessitating the adjustment of multiple hyperparameters through trial-and-error processes and demanding significant amounts of data. In this work, we explore a meta-learning approach to leverage prior knowledge about analogous (though not identical) systems, aiming to reduce both the experimental workload and ease the tuning of the available degrees of freedom. We validate this methodology through an experimental case study involving the tuning of proportional, integral (PI) controllers for brushless DC (BLDC) motors with variable loads and architectures. © 2024 ieee.

关键词：

来源：评论

学校读者我要写书评

暂无评论

data-driven Approach with Machine learning to Reduce Subjectivity in Multi-Attribute Decision Making Methods 17

Data-Driven Approach with Machine Learning to Reduce Subject...

引用

17th Annual ieee International systems conference (SysCon)

作者： Torkjazi, Mohammadreza Raz, Ali K. George Mason Univ Syst Engn & Operat Res Fairfax VA 22030 USA

ISBN: (纸本)9781665439947

Multi-Attribute Decision Making (MADM) methods are an integral component of trade-off studies which are frequently employed in systems Engineering when multiple interdependent decision criteria are involved. In MADM methods, each decision criterion is assigned a weight based on how important it is to the Decision-Makers (DMs), and a decision matrix is populated with values representing assessments of each alternative with respect to the decision criteria. MADM methods, therefore, are susceptible to subjectivity due to inherent bias in DM's preferences where slight fluctuation in stated DM's preference can drastically impact the outcome. In this paper, we propose a data-driven methodology with Machine learning to improve the effectiveness of MADM methods by reducing DMs' subjective biases resulting from criteria weights. In addition, the proposed methodology leverages Exploratory data Analysis to better determine the type of criteria as cost or benefit, depending upon whether it positively or negatively affects the MADM outcome. A sample trade study example of selecting a metropolitan area based on housing affordability is provided to illustrate how the proposed method is applied to generate data-based true criteria weights and types.

关键词： Multi-Criteria Decision Making Multi-Attribute Decision Making Subjective Weights Trade Study Machine learning (ML)

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：