Design of distributed functional interval observers for large-scale networks impulsive systems

2021 ◽  
pp. 014233122110226
Author(s):  
Dinh Cong Huong ◽  
Dao Thi Hai Yen ◽  
Mai Viet Thuan
Keyword(s):  
State Vector ◽  
Large Scale ◽  
Design Method ◽  
Linear Functions ◽  
Effective Algorithm ◽  
Impulsive Systems ◽  
Simulation Results ◽  
Large Scale Networks ◽  
Interval Observers ◽  
Bounded Uncertainties

In this paper, we consider the problem of designing distributed functional interval observers (IOs) for a class of large-scale networks impulsive systems with bounded uncertainties. We first design IOs for linear functions of the state vector of each system of the considered system. We then provide conditions for the existence of such IOs and an effective algorithm for computing unknown observer matrices. Finally, two examples and simulation results are given to illustrate the effectiveness of the proposed design method.

Functional interval observer design for singular fractional-order systems with disturbances

2020 ◽  
pp. 014233122094489
Author(s):  
Dinh Cong Huong ◽  
Dao Thi Hai Yen
Keyword(s):  
Fractional Order ◽  
Design Method ◽  
Observer Design ◽  
Effective Algorithm ◽  
Fractional Order Systems ◽  
Full State ◽  
Simulation Results ◽  
Interval Observer ◽  
Interval Observers ◽  
State Functions

In this study, we consider the problem of designing functional interval observers for a class of singular fractional-order systems. The goal of this work is to design not a full state interval observer for singular fractional-order systems, but to design an observer for state functions of this kind of systems. Conditions for the existence of such functional interval observers are given and an effective algorithm for computing unknown observer matrices is provided in this study. Two numerical examples and simulation results are provided to illustrate the effectiveness of the proposed design method.

A New Backpropagation Learning Algorithm for Layered Neural Networks with Nondifferentiable Units

2007 ◽  
Vol 19 (5) ◽  
pp. 1422-1435 ◽  
Author(s):  
Takahumi Oohori ◽  
Hidenori Naganuma ◽  
Kazuhisa Watanabe
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Performance ◽  
Large Scale ◽  
Learning Algorithm ◽  
Backpropagation Algorithm ◽  
Digital Version ◽  
Output Layer ◽  
Simulation Results ◽  
Large Scale Networks

We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance.

An Effective Algorithm for Large Scale Image and Terrain Data Walkthrough

2009 ◽  
Vol 11 (5) ◽  
pp. 604-609
Author(s):  
Liuqing MA ◽  
Guanfu SONG ◽  
Hui GUO ◽  
Qin ZHOU
Keyword(s):  
Large Scale ◽  
Effective Algorithm

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

scholarly journals Design of a Variable-Stiffness Compliant Skin for a Morphing Leading Edge

2021 ◽  
Vol 11 (7) ◽  
pp. 3165
Author(s):  
Zhigang Wang ◽  
Yu Yang
Keyword(s):  
Composite Laminate ◽  
Large Scale ◽  
Design Method ◽  
Leading Edge ◽  
Variable Stiffness ◽  
Manufacturing Constraints ◽  
Aircraft Wing ◽  
Noise Abatement ◽  
Civil Aircraft ◽  
Final Profile

A seamless and smooth morphing leading edge has remarkable potential for noise abatement and drag reduction of civil aircraft. Variable-stiffness compliant skin based on tailored composite laminate is a concept with great potential for morphing leading edge, but the currently proposed methods have difficulty in taking the manufacturing constraints or layup sequence into account during the optimization process. This paper proposes an innovative two-step design method for a variable-stiffness compliant skin of a morphing leading edge, which includes layup optimization and layup adjustment. The combination of these two steps can not only improve the deformation accuracy of the final profile of the compliant skin but also easily and effectively determine the layup sequence of the composite layup. With the design framework, an optimization model is created for a variable-stiffness compliant skin, and an adjustment method for its layups is presented. Finally, the deformed profiles between the directly optimized layups and the adjusted ones are compared to verify its morphing ability and accuracy. The final results demonstrate that the obtained deforming ability and accuracy are suitable for a large-scale aircraft wing.

scholarly journals Optimal Design of Novel Blade Profile for Savonius Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3484
Author(s):  
Tai-Lin Chang ◽  
Shun-Feng Tsai ◽  
Chun-Lung Chen
Keyword(s):  
Wind Energy ◽  
Drag Force ◽  
Wind Turbines ◽  
Rural Areas ◽  
Large Scale ◽  
Design Method ◽  
Angular Position ◽  
Vertical Axis ◽  
Blade Profile ◽  
Main Concept

Since the affirming of global warming, most wind energy projects have focused on the large-scale Horizontal Axis Wind Turbines (HAWTs). In recent years, the fast-growing wind energy sector and the demand for smarter grids have led to the use of Vertical Axis Wind Turbines (VAWTs) for decentralized energy generation systems, both in urban and remote rural areas. The goals of this study are to improve the Savonius-type VAWT’s efficiency and oscillation. The main concept is to redesign a Novel Blade profile using the Taguchi Robust Design Method and the ANSYS-Fluent simulation package. The convex contour of the blade faces against the wind, creating sufficient lift force and minimizing drag force; the concave contour faces up to the wind, improving or maintaining the drag force. The result is that the Novel Blade improves blade performance by 65% over the Savonius type at the best angular position. In addition, it decreases the oscillation and noise accordingly. This study achieved its two goals.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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