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Biomechanics And Motor Control Of Human Movement

A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.

A survey of deep learning-based network anomaly detection

Bifurcation control deals with modification of bifurcation characteristics of a parameterized nonlinear system by a designed control input. Typical bifurcation control objectives include delaying t...

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Bifurcation control: theories, methods, and applications

Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications

Switching LPV control designs using multiple parameter-dependent Lyapunov functions

The problem caused by imbalance in rotating machinery can be solved using actively controlled magnetic bearing systems. There are two methods to solve this problem using feedback control: 1) compensate for the imbalance forces by generating opposing forces on the bearing surface; and 2) make the rotor rotate around its axis of inertia so no imbalance forces will be generated. The dynamics of the magnetic bearing are described in state-space form using airgap displacement, velocity, and airgap flux as state variables. The system which is unstable in nature is stabilized using the Q-parameterization theory. To compensate for the imbalance disturbance forces, the controller Q-parameter is chosen such that rejection of sinusoidal disturbances is achieved. To achieve automatic balancing, the imbalance is assumed as a sinusoidal noise in the measured signal, and the controller Q-parameter is chosen such that rejection of sinusoidal noise is achieved. Simulation results are presented and show the robustness of the proposed controllers and that the rejection of sinusoidal disturbances is achieved. The rotation of the rotor around its axis of inertia is also achieved. >

Imbalance compensation and automation balancing in magnetic bearing systems using the Q-parameterization theory

Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources

In digital signal processing (DSP),
Nyquistrate sampling completely describes a signal by exploiting its
bandlimitedness. Compressed Sensing (CS), also known as compressive sampling,
is a DSP technique efficiently acquiring and reconstructing a signal completely
from reduced number of measurements, by exploiting its compressibility. The
measurements are not point samples but more general linear functions of the
signal. CS can capture and represent sparse signals at a rate significantly
lower than ordinarily used in the Shannon’s sampling theorem. It is interesting
to notice that most signals in reality are sparse; especially when they are
represented in some domain (such as the wavelet domain) where many coefficients
are close to or equal to zero. A signal is called K-sparse, if it can be
exactly represented by a basis, , and a set of coefficients , where only K
coefficients are nonzero. A signal is called approximately K-sparse, if it can
be represented up to a certain accuracy using K non-zero coefficients. As an
example, a K-sparse signal is the class of signals that are the sum of K
sinusoids chosen from the N harmonics of the observed time interval. Taking the
DFT of any such signal would render only K non-zero values . An example of
approximately sparse signals is when the coefficients , sorted by magnitude,
decrease following a power law. In this case the sparse approximation
constructed by choosing the K largest coefficients is guaranteed to have an
approximation error that decreases with the same power law as the coefficients.
The main limitation of CS-based systems is that they are employing iterative
algorithms to recover the signal. The sealgorithms are slow and the hardware
solution has become crucial for higher performance and speed. This technique
enables fewer data samples than traditionally required when capturing a signal
with relatively high bandwidth, but a low information rate. As a main feature
of CS, efficient algorithms such as -minimization can be used for recovery.
This paper gives a survey of both theoretical and numerical aspects of
compressive sensing technique and its applications. The theory of CS has many
potential applications in signal processing, wireless communication,
cognitive radio and medical imaging.

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Compressive Sensing Algorithms for Signal Processing Applications: A Survey

Conical magnetic bearings with radial and thrust (axial) control are discussed. First a model of the conical bearing is made in state variable form. Airgap flux, airgap displacement, and velocity are used as state variables. A control method based on recent developments in control theory is proposed. The method is called Q-parameterization theory and is used to design a stabilizing controller for the system which, without control, is unstable in nature. The controller parameters are chosen using console (a software tandem for interactive optimization-based design) so that all design requirements are satisfied. Digital simulation was used to verify the proposed control method. Transient responses, forced responses in all three directions (vertical, horizontal, and axial), and the interactions between them are obtained. The results obtained are satisfactory and suggest the robustness of the proposed technique. >

Conical magnetic bearings with radial and thrust control

Nonlinear oscillations in magnetic bearings caused by gyroscopic effects at high speeds are analyzed. First a nonlinear model for the magnetic bearing is set in state-variable form using airgap flux, gap displacement, and velocity as state variables. The system, which is unstable in nature, is stabilized locally around the equilibrium point of zero speed using an optimal robust servo controller. It is shown that as the speed changes the system undergoes Hopf bifurcation to periodic solutions around some critical speed. The periodic solutions are shown to be unstable, so the methods of nonlinear bifurcation control are used to stabilize them. An easily implemented nonlinear feedback control of quadratic order is derived to control the Hopf bifurcation occurring in the system. The transient response of the system with and without nonlinear feedback is obtained to show the effectiveness of nonlinear feedback. >

Abdelfatah M. Mohamed

Papers

Imbalance compensation and automation balancing in magnetic bearing systems using the Q-parameterization theory

Optimized energy management strategy for grid connected double storage (pumped storage-battery) system powered by renewable energy resources

Compressive Sensing Algorithms for Signal Processing Applications: A Survey

Conical magnetic bearings with radial and thrust control

Nonlinear oscillations in magnetic bearing systems