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.
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.
In this paper, we propose a clustering based multiobjective evolutionary algorithm (CLUMOEA) to deal with the multiobjective optimization problems with irregular Pareto front shapes. CLUMOEA uses a k-means clustering ...
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In this paper, we propose a clustering based multiobjective evolutionary algorithm (CLUMOEA) to deal with the multiobjective optimization problems with irregular Pareto front shapes. CLUMOEA uses a k-means clustering method to discover the population structure by partitioning the solutions into several clusters, and it only allows the solutions in the same cluster to do the reproduction. To reduce the computational cost and balance the exploration and exploitation, the clustering process and evolutionary process are integrated together and they are performed simultaneously. In addition to the clustering, CLUMOEA also uses a distance tournament selection to choose the more similar mating solutions to accelerate the convergence. Besides, a cosine nondominated selection method considering the location and distance information of the solutions are further presented to construct the final population with good diversity. The experimental results show that, compared with some state-of-the-art algorithms, CLUMOEA has significant advantages on dealing with the given test problems with irregular Pareto front shapes.
This paper presents an improved method to teleoperate impedance of a robot based on surface electromyography (EMG) and test it experimentally. Based on a linear mapping between EMG amplitude and stiffness, an incremen...
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There is considerable interest in reducing the number of sensors/beacons involved in underwater positioning/navigation systems since this has the potential to drastically reduce the costs and the time spent in deployi...
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There is considerable interest in reducing the number of sensors/beacons involved in underwater positioning/navigation systems since this has the potential to drastically reduce the costs and the time spent in deploying, calibrating, and recovering acoustic equipment at sea. Motivated by these considerations, we address the problem of single underwater target positioning based on acoustic range measurements between the target and a moving sensor at the sea surface. In particular, the goal of the present work is to compute optimal geometric trajectories for the surface sensor that will, in a well defined sense, maximize the range-related information available for underwater target positioning and tracking. To this effect, the Fisher Information Matrix and the maximization of its determinant are used to determine the sensor trajectory that yields the most accurate positioning of the target, while the latter describes a preplanned trajectory. It is shown that the optimal trajectory depends on the velocity of the sensor, the velocity and trajectory of the target, the sampling time between measurements, the measurement error model, and the number of measurements used to compute the FIM. Simulation examples illustrate the key results derived.
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