Looking at the Smart Grid as a Cyber - Physical system of great complexity, the paper synthesizes the main IT security issues that may arise. Security issues are seen from a hybrid point of view, combining theory of i...
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ISBN:
(数字)9781728168432
ISBN:
(纸本)9781728168449
Looking at the Smart Grid as a Cyber - Physical system of great complexity, the paper synthesizes the main IT security issues that may arise. Security issues are seen from a hybrid point of view, combining theory of information with system theory. Smart Grid has changed dramatically over the past years. With modern technologies, such as Big Data or Internet of Things (IoT), the Smart Grid is evolving into a more interconnected and dynamic power network model.
This paper deals with the fault tolerant control design problem for a Takagi-Sugeno (T-S) fuzzy system. The exact discretization approach is employed to derive the linear matrix inequality (LMI)-based stability condit...
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This paper deals with the fault tolerant interval type-2 fuzzy integral sliding mode controller design for time-varying uncertain chaotic systems including actuator faults. The overall control system consists of drivi...
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In this paper, we propose a sampled-data fuzzy observer design technique for estimating the state variables of a nonlinear system with model uncertainty. It is assumed that the IF-THEN rules of the fuzzy system contai...
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Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applicati...
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ISBN:
(数字)9781728169262
ISBN:
(纸本)9781728169279
Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.
作者:
Sysala, TomasStuchlík, KarelNeumann, PetrTBU in Zlin
Dept. of Automation and Control Engineering Faculty of Appl. Informatics Nad Stranemi 4511 Zlin Czech Republic TBU in Zlin
Dept. of Electronics and Measurements Faculty of Applied Informatics Nad Stranemi 4511 Zlin Czech Republic
The article describes the bandsaw blade properties innovative method design and realization. The introduction contens individual bandsaw parts description, including their important parameters overview. The innovative...
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The problem of regulators (controllers) design is extremely relevant in connection with the penetration into all technological areas of the methods of precise control of objects with feedback. Such devices are being m...
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Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water...
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Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.
The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritizati...
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The paper discusses fuzzy comparison matrices, consistency check, weight prioritization methods and weight evaluation methods in fuzzy group analytic hierarchy process. There are various methods of weight prioritization, however, they are not critically evaluated. In the paper, two measures are introduced for the evaluation of the group weights. Then, a new method is proposed to improve the process of deriving weights and use it in an application compared to another common three methods. Our results show that the new method is a good method for deriving weights of indexes.
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