An accurate magnetic model for electric motors is essential for high performance control strategies. The magnetic model is typically acquired by massive experiments of measuring magnetic flux data throughout the opera...
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ISBN:
(纸本)9798350360875;9798350360868
An accurate magnetic model for electric motors is essential for high performance control strategies. The magnetic model is typically acquired by massive experiments of measuring magnetic flux data throughout the operating current range, and then applied in the controlprocess via a look-up table of measurements. Both the acquisition and the application processes are time-consuming and not suitable for low-latency controls. To address this issue, we propose a novel compressed sensing-based method to recover a high-fidelity flux map from limited randomly sampled data points, and further infer an analytical magnetic model of the recovered flux map. This analytical model can then be used to efficiently compute the magnetic flux instead of looking up measurement data, given the stator current in the control loop. The proposed approach is validated on data simulated by finite element analysis (FEA).
data-driven surrogate models of dynamical systems based on the extended dynamic mode decomposition are nowadays well-established and widely applied. Further, for non-holonomic systems exhibiting a multiplicative coupl...
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data-driven surrogate models of dynamical systems based on the extended dynamic mode decomposition are nowadays well-established and widely applied. Further, for non-holonomic systems exhibiting a multiplicative coupling between states and controls, the usage of bilinear surrogate models has proven beneficial. However, an analysis of the approximation quality and its dependence on different hyperparameters, including physics-motivated dictionary choices, with real-world experimental data is still missing. To close this gap and due to its high practical relevance and widespread usage in applications such as service robotics, we investigate Koopman-based surrogate modeling for a differential-drive mobile robot, also in hardware. 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/)
Urban mobility is increasingly challenged by traffic congestion, leading to inefficiencies, environmental harm, and economic losses. This research presents an intelligent traffic control system that leverages cloud co...
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This paper investigates the impact of Large Language Models (LLMs), specifically GPT, on dataanalysis tasks within the framework of CRISP-DM (Cross-Industry Standard process for data Mining). In order to assess the e...
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ISBN:
(纸本)9783031602269;9783031602276
This paper investigates the impact of Large Language Models (LLMs), specifically GPT, on dataanalysis tasks within the framework of CRISP-DM (Cross-Industry Standard process for data Mining). In order to assess the efficiency of text-to-code language models in data-related tasks, we systematically examine the performance of LLMs in the stages of the data mining process. GPT models are tested against a series of Python programming and SQL tasks derived from a Master's program's curriculum. The tasks focus on data exploration, visualization, preprocessing, and advanced analytical tasks like association rule mining and classification. The findings show that GPT models exhibit proficiency in Python programming across various CRISP-DM stages, particularly in data Understanding, Preparation, and modeling. They adeptly utilize Python libraries for data manipulation and visualization, demonstrating potential as effective tools in data science. However, the study also uncovers areas where the GPT Text-to-code model shows partial correctness, highlighting the need for human oversight in complex dataanalysis scenarios. This research contributes to understanding how AI can augment traditional dataanalysis methods, particularly under the CRISP-DM framework. It reveals the potential of LLMs in automating stages of dataanalysis, suggesting an acceleration in analytical processes and decision-making. The study provides valuable insights for organizations integrating AI into dataanalysis, balancing AI strengths with human expertise.
We propose A controller based on generalized predictive control is used to control the robot, so as to realize the precise tracking and control of the motion trajectory of the unhooked robot. As a nonlinear walking me...
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ISBN:
(纸本)9798350388084;9798350388077
We propose A controller based on generalized predictive control is used to control the robot, so as to realize the precise tracking and control of the motion trajectory of the unhooked robot. As a nonlinear walking mechanism, the train coupler robot must ensure that it can accurately catch up with the coupler to be picked up and move in sync with the coupler after catching up. Therefore, a trajectory tracking control algorithm must be designed to control the actual operation process based on the given ideal displacement curve to ensure that the robot can operate safely and stably. Generalized predictive control has the advantages of solid robustness, broad applicability, simple parameter adjustment, strong predictive ability, and fast response speed. The Elman neural network is a dynamic feedback-type recursive neural network that combines local memory units with local feedback links, with good adaptive, self-organizing, intense learning, fault-tolerant, and anti-interference capabilities. In addressing the modeling difficulties caused by the nonlinear characteristics of the train coupler robot, a generalized predictive control algorithm is adopted for tracking control. The use of the Elman neural networks helps to address the problem of modeling nonlinear systems. Specific experimental data indicates that this algorithm can ensure the robot operates safely and smoothly, reaching the target position and velocity.
A set of low-cost project schedule control system based on BIM technology is established. This paper discusses the requirements analysis, function design, architecture and data warehouse design. ARM7 SCM, LCD module, ...
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作者：
Zhou, LongfeiZhang, LinKonz, NicholasMIT
Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Beihang Univ
Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Duke Univ
Dept Elect & Comp Engn Durham NC 27708 USA
Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, w...
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Computer vision (CV) techniques have played an important role in promoting the informatization, digitization, and intelligence of industrial manufacturing systems. Considering the rapid development of CV techniques, we present a comprehensive review of the state of the art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation, and three-dimensional modeling. A system framework of CV in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, CV algorithms, a decision-making module, and an actuator. Applications of CV to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data preprocessing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for nonannotated dataprocessing, developing effective data preprocessing mechanisms, customizing CV models, and opportunities aroused by 5G.
The growing integration of power electronic devices in power systems has brought attention to frequency stability issues due to low inertia as well as inadequate frequency regulation. In this regard, the paper defines...
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data fusion technology is an information processing discipline that just emerged in the 1990s, and its application range is very wide, while target tracking and recognition is still the most important subject of data ...
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Ultra-high voltage power bushings are widely used in power systems. In the operation and maintenance process of the ultra-high voltage power bushings, testing and analysis are generally based on the equipment itself. ...
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ISBN:
(纸本)9781510668393
Ultra-high voltage power bushings are widely used in power systems. In the operation and maintenance process of the ultra-high voltage power bushings, testing and analysis are generally based on the equipment itself. If data is obtained from the equipment itself, there will be incomplete data transmission, and the obtained data is generally voltage, current, temperature and other sensing data that are easily obtained from outside power equipment. But for the internal data of power equipment, including online data information such as flow velocity field and pressure field. Therefore, this article attempts to establish the three-dimensional visual simulation model for the digital twin of ultra-high voltage power casing equipment, and further sets key tracking nodes on the three-dimensional configuration model to quantitatively track its multi-dimensional physical quantity change curves and trends. First of all, 3D modeling of large terrain of electric equipment is carried out. Virtual texture technology is used to block and fuse large area and multi resolution texture data. Different detail Hierarchical database model are used to block large area terrain, including texture image selection, ground surface background map selection, texture coverage and grid division. After determining the morphology of the terrain, digital twin 3D modelinganalysis was immediately conducted on the power equipment. In the 3D digital twin modeling, the converter transformer body and converter transformer bushing are taken as examples for analysis and explanation. The detailed structure of bushing equipment is considered in the twin model. At the same time, the electrical, thermal and mechanical Multi-physics simulation of converter transformer bushing are coupled and analyzed. Multi-physics simulation simulation results are associated with the key nodes in the 3D digital twin model to realize interaction of physical quantity information between the simulation model and the twin model. The
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