In contrast with UKF, the standard CKF can solve high-dimensional nonlinear filter problems. However, when the nonlinear systematic dimension increases, the accuracy of CKF will decline and the computational cost will...
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ISBN:
(纸本)9781479967339
In contrast with UKF, the standard CKF can solve high-dimensional nonlinear filter problems. However, when the nonlinear systematic dimension increases, the accuracy of CKF will decline and the computational cost will increase rapidlly. Two-Stage Kalman filter can solve this problem, but it only applies to linear systems. This paper proposes a two-stage Cubature Kalman filter (TSCKF) which can solve high-dimensional nonlinear systems with random bias. The estimate of the TSCKF can be expressed as the output of the bias free filter and bias filter. The bias free filter doesn't consider the bias and its output is corrected by the bias filter. In comparison with Augmented state Cubature Kalman Filter (ASCKF), the TSCKF avoids dimension disaster effectively and has little computation to solve high-dimensional nonlinear filter problem.
Multi-terminal VSC-HVDC transmission is a promising solution to transport energy from the generating plants to the distribution stations, and the number and size of the corresponding power grids is expected to increas...
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ISBN:
(纸本)9781467360890
Multi-terminal VSC-HVDC transmission is a promising solution to transport energy from the generating plants to the distribution stations, and the number and size of the corresponding power grids is expected to increase in the next years. Such a system presents challenging control problems that, so far, have been approached with classical control techniques. However, large scale systems of interconnected nodes fall within the framework of the emerging field of complex networks. This paper analyzes the stability of multi-terminal VSC-HVDC systems from a complex dynamical network perspective, and provides sufficient conditions to ensure bounded synchronization of its trajectories. The obtained results are validated via numerical simulations.
Results of a steady-state analysis performed for a class of distributed parameter systems described by hyperbolic partial differential equations defined on a one-dimensional spatial domain are presented. For the case ...
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Results of a steady-state analysis performed for a class of distributed parameter systems described by hyperbolic partial differential equations defined on a one-dimensional spatial domain are presented. For the case of the system with two state variables and two boundary inputs, the analytical expressions for the steady-state distribution of the state variables are derived, both in the exponential and in the hyperbolic form. The influence of the location of the boundary inputs on the steady-state response is demonstrated. The considerations are illustrated with a practical example of a shell and tube heat exchanger operating in parallel-and countercurrent-flow modes.
Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated ...
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ISBN:
(纸本)9781479951192
Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ignoring the local distribution of each data point. This objective does not hold for k-nearest neighbors search. In this paper, we firstly propose a new adaptive similarity measure which is consistent with k-NN search, and prove that it leads to a valid kernel. Then we propose a hashing scheme which uses binary codes to preserve the kernel function. Using low-rank approximation, our hashing framework is more effective than existing methods that preserve similarity over arbitrary kernel. The proposed kernel function, hashing framework, and their combination have demonstrated significant advantages compared with several state-of-the-art methods.
The software of electric / electronic vehicle control systems is static in current series vehicles. Most of the systems do not allow maintenance or functional updates, especially in the field of driver assistance syst...
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The software of electric / electronic vehicle control systems is static in current series vehicles. Most of the systems do not allow maintenance or functional updates, especially in the field of driver assistance systems. Main causes are the testing effort for a software release and the wide variety of different configurations in different vehicle models. In this paper we take a closer look at the requirements for a middleware which allows such updates, verifies new software versions, and adds reconfiguration mechanisms for singular control units and distributed sets of control units. To derive the requirements we consider the general vehicular context with limitations in space, electric power, processing power, and costs together with four exemplary road vehicle control applications (cruise control, automatic parking, stability control, force feedback), and a full x-by-wire target vehicle for implementing these applications. The analysis of these three different sources of requirements results in desired middleware functionalities and requirements, especially concerning runtime timings and update timings. The requirements cover an update functionality with integrated verification, the exchange of applications on singular control units, and the degradation of functionality by switching between control units.
In this paper we present NeuViz, a data processing and visualization architecture for network measurement experiments. NeuViz has been tailored to work on the data produced by Neubot (Net Neutrality Bot), an Internet ...
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In this paper we present NeuViz, a data processing and visualization architecture for network measurement experiments. NeuViz has been tailored to work on the data produced by Neubot (Net Neutrality Bot), an Internet bot that performs periodic, active network performance tests. We show that NeuViz is an effective tool to navigate Neubot data to identify cases (to be investigated with more specific network tests) in which a protocol seems discriminated. Also, we suggest how the information provided by the NeuViz Web API can help to automatically detect cases in which a protocol seems discriminated, to raise warnings or trigger more specific tests.
This paper considers the identification of stochastic Wiener dynamic systems, that is linear dynamic systems with process noise, where the measurable output signal is a nonlinear function of the output from the linear...
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ISBN:
(纸本)9781467360890
This paper considers the identification of stochastic Wiener dynamic systems, that is linear dynamic systems with process noise, where the measurable output signal is a nonlinear function of the output from the linear system corrupted with additive measurement noise. It is shown how stochastic Wiener system identification can be viewed as a particular non-linear model errors-in-variables problem, for which there exists a large literature. We compare the maximum likelihood method with prediction error minimization methods based on the conditional mean predictor for Wiener systems. Related methods have previously been studied in the framework of identification of non-linear error-in-variables models. We extend these results by taking the input signal to the Wiener system into consideration. For example, the input will affect the variance of the prediction errors. Hence, a prediction error method with a variance weighting is derived to obtain more reliable parameter estimates. An advantage with the prediction error method is that for certain special cases we can avoid numerical integration. We also discuss how the unscented transform can be used to obtain an approximate predictor for the prediction error method. The numerical evaluation of these methods is performed on a simple first order FIR system with a cubic nonlinearity, for which some illustrative analytic properties are derived.
In this paper, the optimal denial-of-service (DoS) attack strategies on Bayesian quickest change detection are developed. Specifically, a sensor monitoring an environment that may change randomly sends its observation...
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ISBN:
(纸本)9781467360890
In this paper, the optimal denial-of-service (DoS) attack strategies on Bayesian quickest change detection are developed. Specifically, a sensor monitoring an environment that may change randomly sends its observation at each time via a wireless channel to a remote center. Upon receiving the data from the sensor, the remote center decides sequentially whether a change of the environment takes place or not and the remote center aims to detect such a change as soon as possible subject to a certain false alarm rate. An attacker eavesdropping the wireless channel can launch jamming attacks (e.g., block the wireless channel) between the sensor and the remote center for at most N times. To make the detection cost (a linear combination of detection delay and the probability of false alarm) for the center as large as possible, the attacker needs to decide when to implement such DoS attacks. We solve this problem by formulating it as an infinite horizon MDP problem. The asymptotic lower and upper bound of the expected detection delay at the center, when the probability of false alarm goes to zero, of such an attack is also investigated. A numerical example is shown to illustrate the main results.
Oscillations usually propagate to other loops with the delivery of mass and energy, then cause plant-wide oscillation and affect the performance of whole control system in complex chemical process. DTF (Directed Trans...
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Oscillations usually propagate to other loops with the delivery of mass and energy, then cause plant-wide oscillation and affect the performance of whole control system in complex chemical process. DTF (Directed Transfer Function) method, which has been widely used to analyze information flow in the brain structures in biomedical area, is applied to the disturbance propagation analysis of complex chemical process in this paper. Based on MVAR (Multivariate Autoregressive) model, DTF can analyze the multivariate causality simultaneously and calculate the causality quantitatively. Based on the DTF value, one can draw the causality graph, get the disturbance propagation path and finally locate fault sources. The results of simulation on TEP (Tennessee Eastman Process) are presented to illustrate the effectiveness of the proposed approach.
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