Recently, object tracking has been widely studied as a binary classification problem. Semi-supervised learning is particularly suitable for improving classification accuracy when large quantities of unlabeled samples ...
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This paper investigates the problem of stability for a class of linear uncertain Markovian jump systems over networks via the delta operator approach. The sensor-to-controller random network-induced delay and arbitrar...
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In this paper we present predictive self-programming thermostat which controls the heating of a smart environment based on occupancy prediction. We present results of 290 days-long simulation as well as limits of simi...
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In this paper we present predictive self-programming thermostat which controls the heating of a smart environment based on occupancy prediction. We present results of 290 days-long simulation as well as limits of similar systems for energy savings.
This paper presents a challenging industrial benchmark for implementation of control strategies under realistic working conditions. The developed control strategies should perform in a plug & play manner, i.e. ada...
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This paper presents a novel decoupling method for multivariable systems with disturbances. In this method, the undesirable coupling parts in each loop are treated as the output disturbances. These disturbances, as wel...
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This paper focuses on the structure identification problem for a class of networked systems, where the interaction among components or agents is described through logical maps. In particular, agents are heterogeneous ...
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ISBN:
(纸本)9781467320658
This paper focuses on the structure identification problem for a class of networked systems, where the interaction among components or agents is described through logical maps. In particular, agents are heterogeneous cooperating systems, i.e. they may have different individual dynamics and different interaction rules depending on input events. While we assume that the individual agents' dynamics are known, each agent has partial knowledge of the logical map encoding the interaction of another agent with its neighbors. Based on the so-called algebraic normal form for binary functions, we present a technique by which the network structure described by a logical function can be dynamically estimated as its truth table is observed. The estimated map is a lower approximation of the real one, which coincides with it as soon as the truth table is entirely observed. Application of the proposed technique to a real system, where agents are mobile robots whose motion depends on logical conditions on their neighborhood, is finally presented.
The usage of Wireless Sensor Networks (WSNs) in industrial environments has been steadily increasing, due to the reduced deployment and maintenance costs, when compared to the use of wired networks for connecting sing...
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This paper represents using Case-Based Reasoning (CBR) in order to support undergraduate controlengineering students for learning nonlinear systems and control applications. CBR is an experience-based problem solving...
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This paper represents using Case-Based Reasoning (CBR) in order to support undergraduate controlengineering students for learning nonlinear systems and control applications. CBR is an experience-based problem solving methodology; thus, it is used for helping controlengineering students to design conventional PID controllers for nonlinear systems. For this purpose, GUNT pH neutralization process is used as the experimental setup and a GUI is designed for cased-based reasoning in MATLAB. It is observed that undergraduate students can learn and increase their experience on nonlinear systems using the proposed new CBR-GUI.
We deal with the design problem of minimum entropy ℋ ∞ filter in terms of linear matrix inequality (LMI) approach for linear continuous-time systems with a state-space model subject to parameter uncertainty that belo...
We deal with the design problem of minimum entropy ℋ ∞ filter in terms of linear matrix inequality (LMI) approach for linear continuous-time systems with a state-space model subject to parameter uncertainty that belongs to a given convex bounded polyhedral domain. Given a stable uncertain linear system, our attention is focused on the design of full-order and reduced-order robust minimum entropy ℋ ∞ filters, which guarantee the filtering error system to be asymptotically stable and are required to minimize the filtering error system entropy (at s 0 = ∞ ) and to satisfy a prescribed ℋ ∞ disturbance attenuation performance. Sufficient conditions for the existence of desired full-order and reduced-order filters are established in terms of LMIs, respectively, and the corresponding filter synthesis is cast into a convex optimization problem which can be efficiently handled by using standard numerical software. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.
As vehicle emission standards become more stringent, there is an increasing need for continual monitoring of benzene content in gasoline. Since the on-line analyzers are often unavailable, and laboratory analyses are ...
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As vehicle emission standards become more stringent, there is an increasing need for continual monitoring of benzene content in gasoline. Since the on-line analyzers are often unavailable, and laboratory analyses are infrequently obtained, soft sensors for the estimation of benzene content of light reformate are developed. Soft sensors are developed using system identification methods. Experimental data is acquired from the refinery distributed control system (DCS) and include continuously measured variables and analyzer assays available on-line. In the present work, the development of a Finite Impulse Response (FIR) model, an Output Error (OE) model and an Auto-Regressive Model with Exogenous Inputs (ARX) model are presented. To overcome the problem of selecting the best model parameters by trial and error, genetic algorithm was used. Based on developed soft sensors, it is possible to entirely replace on-line analyzers with soft sensors by embedding the model in a DCS on-site.
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