In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is consider...
In this paper, the minimal control placement prob-lem for Turing's reaction-diffusion model is studied. Turing's model describes the process of morphogens diffusing and reacting with each other and is considered as one of the most fundamental models to explain pattern formation in a devel-oping embryo. controlling pattern formation artificially has gained increasing attention in the field of development biology, which motivates us to investigate this problem mathematically. In this work, the two-dimensional Turing's reaction-diffusion model is discretized into square grids. The minimal control placement problem for the diffusion system is investigated first. The symmetric control sets are defined based on the symmetry of the network structure. A necessary condition is provided to guarantee controllability. Under certain circumstances, we prove that this condition is also sufficient. Then we show that the necessary condition can also be applied to the reaction-diffusion system by means of suitable extension of the symmetric control sets. Under similar circumstances, a sufficient condition is given to place the minimal control for the reaction-diffusion system.
Tree-based ensemble models are easy to implement and have been widely used in various fields. However, they have limitations in industrial process applications since the majority of tree-based ensemble models are pron...
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Tree-based ensemble models are easy to implement and have been widely used in various fields. However, they have limitations in industrial process applications since the majority of tree-based ensemble models are prone to over-fitting. In addition, the internal structure of tree-based ensemble models is very complex and the output of the model is also difficult to explain, which makes its application in industrial soft sensors very challenging. The purpose of this work is to build accurate and interpretable soft sensors for industrial processes. First, to deal with overfitting, a robust tree-based ensemble model and extremely randomized trees are used to build accurate soft sensors. Then, to improve model interpretability, an interpretable machine learning algorithm, namely Shapely additive explanation, is used to infer the global and local contributions of each feature to the predictions. Finally, the effectiveness of the proposed algorithms is validated on real industrial fluid catalytic cracker unit data.
Soil-available potassium content is essential for agricultural crop management. The electrochemical measurement method exhibits advantages of high speed, low cost, and simple operation. However, it encounters challeng...
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Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investi...
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Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a n...
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Achieving optimal speed regulation for permanent magnet synchronous motors (PMSMs) remains a challenging task, particularly in selecting the most suitable controller to meet desired objectives. This paper considers th...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
Achieving optimal speed regulation for permanent magnet synchronous motors (PMSMs) remains a challenging task, particularly in selecting the most suitable controller to meet desired objectives. This paper considers the optimal speed tracking problem of PMSMs. We employ a reinforcement learning algorithm to obtain optimal controller with a linear form. The reinforcement learning approach incorporates a critic-actor network structure, which approximates the value function and optimal control, respectively. Specifically, a weight updating method for the critic network is introduced, where the update of the actor network is guided by the critic network. Through data collection and neural network training, dynamic acquisition of the optimal controller gain is achieved. In addition, a novel activation function is implemented in the network, leading to improved control performance concerning dataset size and reduced training time. At final, a real-world PMSM application is presented along with comparisons to demonstrate effectiveness. The desire idea of resulting optimal controllers is not only applicable to linear systems but also capable of approximating arbitrary nonlinear optimal controllers, thus offering practical engineering versatility.
The paper presents an approach to developing new educational programs for information technologies (IT) specialists using job market analysis. We developed a set of innovative software tools for collecting data from j...
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In this paper, we consider constrained optimization problems with convex, smooth objective and constraints. We propose a new stochastic gradient algorithm, called the Stochastic Moving Ball Approximation (SMBA) method...
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This paper studies a structure-preserving model reduction problem for large-scale second-order dynamical systems via the framework of time-domain moment matching. The moments of a second-order system are interpreted a...
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The article deals with the aspects of voltage stabilization of an induction generator in a system with a capacitive current relay regulator. Simulation is carried out by the process in an induction generator with capa...
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