Optimization of industrial activities is significantly helped by Advanced Process control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Int...
详细信息
In this paper, we present the methodology for the dimension of an innovative DC generator specifically designed for wind energy systems, featuring axial flux and permanent magnets. The evolution of the sliding contact...
详细信息
control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to o...
详细信息
The paper is concerned with developing a methodology to analyze complex-structured systems of control indicators within decarbonization projects. The proposed methodology is based on a formal description of control sc...
详细信息
Centralized controllers are not scalable because they require high computational cost and communication bandwidth (full connectivity). Furthermore, the entire group will also be vulnerable if the centralized agent is ...
详细信息
ISBN:
(数字)9781665405935
ISBN:
(纸本)9781665405935
Centralized controllers are not scalable because they require high computational cost and communication bandwidth (full connectivity). Furthermore, the entire group will also be vulnerable if the centralized agent is under a cyber attack. So, the need for distributed controllers becomes a necessity. However, finding efficient distributed controllers is challenging. Hence the primary goal of this work is to learn controllers that emulate centralized controllers but only use local information. We use SVM-regression, Gaussian process regression, and neural networks to learn the centralized controllers applied to two problems, a one-dimensional aerial platooning and synchronized convergence of multiple UAVs onto a stationary target. We discuss different learning models, i.e., single and unique learning models. Also, we investigate two types of learning-based control approaches, Full Learning control (FLC) and Partial Learning control (PLC). Further, from simulations, we show the effectiveness of the proposed approach to these problems.
The article examines issues of ecological safety of drinking water. The Jeyranbatan reservoir, which supplies drinking water to the cities of Baku and Sumgayit, was selected as an object. The anthropogenic and natural...
详细信息
An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model....
详细信息
Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has re...
详细信息
Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy researchers for its outstanding ability to provide complex mappings. However, deep neural networks involve complex configurations, making it challenging to utilize them in practice. This paper assesses and presents a number of model-control techniques, categorized as model-oriented and data-oriented, to achieve more robust and efficacious deep neural networks for applications in the nonlinear modeling of wind power with wind speed. These carefully refined models are also compared with polynomials, simple neural networks, and not optimized deep networks with annual data of an Arctic wind farm. The results show that deep networks with sufficient parameter tunings, training optimizations, and modeling exhibit superior performance and generalization, thus possessing considerable advantages in wind energy engineering. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
To enhance the efficiency of the surrogate-assisted evolutionary algorithm (SAEA) for solving high-dimensional expensive optimization problems with multiple local optima and multivariate coupling, an SAEA incorporatin...
详细信息
The proceedings contain 40 papers. The topics discussed include: calculation of soft magnetic high-entropy alloys systems;enhancement of tribological properties of tool metal-ceramic materials by high-power ion beams;...
ISBN:
(纸本)9781510681590
The proceedings contain 40 papers. The topics discussed include: calculation of soft magnetic high-entropy alloys systems;enhancement of tribological properties of tool metal-ceramic materials by high-power ion beams;some features of the formation of gas hydrates in solutions of common salt;study of the features of formation of gas hydrates in solutions of common salt and calcium chloride;development of mathematical models for nonlinear nonstationary random processes;detecting anomalies in forest input data in a supply chain system using machine learning;grid approximation in the problems of synthesis of UAV controlsystems;development of an emotion and age recognition model based on the combination of local binary patterns and squeeze-and-excitation block method;and simulation modeling as a decision support system tool for improving the environmental safety of road transport.
暂无评论