Echocardiography is the most commonly used imaging technique in clinical cardiology. Due to the high demand for this type of examination and the small number of specialists, there is a need to support the examination ...
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The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on ...
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The purpose of this paper is to design a coherent feedback controller for a Markovian jump linear quantum system suffering from a fault signal. The control objective is to bound the effect of the disturbance input on the output for the time-varying quantum system. We prove the relation between the H ∞ control problem, the dissipation properties, and the solutions of Riccati differential equations, by which the H ∞ controller of the Markovian jump linear quantum system is given by the solutions of Linear Matrix Inequalities (LMIs).
Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. He...
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ISBN:
(数字)9781728162157
ISBN:
(纸本)9781728162164
Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.
It is observed that certain convex envelopes of Wightman type functionals corresponding to scalar, stochastically positive quantum fields consist of Wightman type functionals only .This leads to the construction of a ...
Iterative learning control can be applied to systems that repeatedly execute the same finite duration task. The distinguishing feature of this form of control action is that all data generated on a previous execution ...
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Iterative learning control can be applied to systems that repeatedly execute the same finite duration task. The distinguishing feature of this form of control action is that all data generated on a previous execution of the task is available to compute the control action for the next execution. This paper uses the 2D systems setting to design a dynamic controller, with particular emphasis on enhancing the controller with filters to suppress noise build up from execution to execution. The design is experimentally validated on a 3D crane laboratory based facility.
This paper describes development of a control system for a heavy self-balancing two-wheeled robot. The development process includes: model identification, model tuning, design and tuning of a Model Predictive control ...
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This paper describes development of a control system for a heavy self-balancing two-wheeled robot. The development process includes: model identification, model tuning, design and tuning of a Model Predictive control (MPC) algorithm. Although a simple linear state-space model with only two state variables is used, the results of laboratory experiments clearly indicate that the MPC algorithm based on such a model works well, i.e. the algorithm is able to effectively stabilise the robot.
This work reports implementation of the Dynamic Matrix control (DMC) algorithm using a Programmable Logic controller (PLC). In contrast to typical industrial implementations of the DMC algorithm with relatively long t...
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This work reports implementation of the Dynamic Matrix control (DMC) algorithm using a Programmable Logic controller (PLC). In contrast to typical industrial implementations of the DMC algorithm with relatively long time constants (and sampling times), in this work very fast processes are considered, characterised by very short sampling times. The DMC algorithm is implemented in its most computationally efficient version in which the values of the manipulated variables are calculated from explicit formulas. Thanks to that it is shown that the DMC algorithm is able to successfully control a laboratory process with the sampling time equal to 5 ms. The first process configuration has one input and one output whereas the second one has two inputs and two outputs. The influence of the process horizon of dynamics on the algorithm execution time and PLC's memory usage is discussed.
Iterative Learning control (ILC) is a very powerful control technique that iteratively improves the transient behaviour of systems that are repetitive in nature. In this paper it is shown how ILC algorithm is designed...
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Iterative Learning control (ILC) is a very powerful control technique that iteratively improves the transient behaviour of systems that are repetitive in nature. In this paper it is shown how ILC algorithm is designed and implemented to improve the tracking trajectory performance of mobile robot with a differential drive. Two step design procedure is proposed where a feedback controller is chosen as a classical PID controller and involves some performance specification to attenuate non-repetitive disturbances and noises. Then, as the second step, the learning filter is determined by an appropriate application of a plant inversion method. It turns out that convergence and learning performance of this ILC scheme can be obtained for a physical system and hence practical usefulness of the scheme is verified experimentally on Lego EV3-based mobile robot.
This work discusses tuning of Model Predictive control (MPC) algorithms by means of some global optimisation methods. For test purposes the Dynamic Matrix control (DMC) algorithm applied to a Multiple-Input Multiple-O...
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This work discusses tuning of Model Predictive control (MPC) algorithms by means of some global optimisation methods. For test purposes the Dynamic Matrix control (DMC) algorithm applied to a Multiple-Input Multiple-Output (MIMO) process with 4 manipulated and 3 controlled variables is considered. The tuned parameters include prediction and control horizons as well as the weights of the minimised MPC cost-function. Four global optimisation methods are considered: the Particle Swarm Optimisation method, the Firefly Algorithm, the Grey Wolf Optimiser and the Jaya algorithm. They are compared in terms of the ability to find the best solution and convergence. The obtained results show that global optimisation methods can be successfully used in this type of tasks.
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