This paper aims to present the design of a cloud computing-based equipment monitoring system (EMS), called CCEMS, for the CNC machine tool industry to illustrate the paradigm shift of EMSs from basing on the Internet ...
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In this paper we will describe the development of models for prediction of powertrain performance. Our goal is to develop a library of components to model combustion, gas dynamics and mechanical response. We will also...
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This paper considers the problem of estimating the parameters of a signal using time-varying thresholded noisy one-bit measurements. The problem is shown to be deterministically identifiable under reasonable condition...
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ISBN:
(纸本)9781509021048
This paper considers the problem of estimating the parameters of a signal using time-varying thresholded noisy one-bit measurements. The problem is shown to be deterministically identifiable under reasonable conditions on the signal and thresholds. A spectral sensing application is considered, and two sparse methods are presented. In addition to the standard ℓ 1 norm based approach, a “zero-norm” approximation via the logarithm function is used, and shown to yield sparser estimates in a numerical example.
The problem of improved performance adaptive control (IPAC) of a class of linear and nonlinear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the "stan...
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The problem of improved performance adaptive control (IPAC) of a class of linear and nonlinear systems is considered. A method for its solution is presented, the main feature of which lies in augmenting the "standard" model reference adaptive controller by a properly designed signal compensating for the effect of plant parameter uncertainty on the output error. One of the main performance improvement characteristics of the proposed IPAC is that the zero-state output error can be made arbitrarily small under standard model reference adaptive control (MRAC) assumptions in the case of linear systems while a similar result holds for a class of linearizable systems as well. The exponential convergence of the output and parameter errors in the presence of sufficiently rich reference inputs, remains a valid property of this controller which also achieves improved robustness in the presence of bounded disturbances and/or unmodeled dynamics as well as in the case of an adaptation switch-off.
This paper describes a theoretical framework for the design of controllers to satisfy probabilistic safety specifications for partially observable discrete time stochastic hybrid systems. We formulate the problem as a...
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ISBN:
(纸本)9781479901777
This paper describes a theoretical framework for the design of controllers to satisfy probabilistic safety specifications for partially observable discrete time stochastic hybrid systems. We formulate the problem as a partial information stochastic optimal control problem, in which the objective is to maximize the probability that the state trajectory remains within a given safe set in the hybrid state space, using observations of the history of inputs and outputs. It is shown that this optimal control problem, which has a multiplicative payoff structure, is equivalent to a terminal payoff problem when the state space is augmented with a binary random variable capturing the safety of past state evolution. This allows us to derive a sufficient statistic for the probabilistic safety problem as a set of Bayesian filtering equations updating a conditional distribution on the augmented state space, as well as an abstract dynamic programming algorithm for computing the maximal probability of safety and an optimal control policy.
For a class of nonlinear systems affine in controls and with unknown high frequency gain, we develop a hybrid control strategy that guarantees (practical) global input-to-state stability (ISS) with respect to measurem...
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For a class of nonlinear systems affine in controls and with unknown high frequency gain, we develop a hybrid control strategy that guarantees (practical) global input-to-state stability (ISS) with respect to measurement noise. We provide a design procedure for the hybrid controller and apply it to Freeman’s counterexample and minimum-phase relative degree one systems.
The adaptability of devices can be significant for a customer that inserts them in an industrial production line. The ability to modify an object bought along with a machine that can be personalized with its features ...
The adaptability of devices can be significant for a customer that inserts them in an industrial production line. The ability to modify an object bought along with a machine that can be personalized with its features can change how they want to do measurements for different reasons, like predictive maintenance. Fog computing local centers already exist in the market, but they are usually on-the-shelf products with no margin of change for any user. However, with the usage of Docker and containers, this can change. This paper describes a fog computing local central called Concentrator, which can not only execute its essential functions built-in by the producer but also be customized by the user to add in the elaborations on other external sensors, expanding its capabilities and usage. We wanted to improve the device already tested on a Linux PC on a Raspberry Pi and try its performance and characteristics, seeing if it could be transformed into an embedded architecture and an industrial feature.
A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ...
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A benchmark study of two self-organizing artificial neural network models, ART2 and DIGNET, is conducted. The architecture differences and learning procedures between these two models are compared. The performance of ART2 and DIGNET on data clustering and signal detection problems with noise or interference is investigated by comparative simulations. It is shown that DIGNET generally has faster learning and better clustering performance on the statistical pattern recognition problems. DIGNET has a simpler architecture, and the system parameters can be analytically determined from the self-organizing process. The threshold value used in DIGNET can be specifically determined from a given lower bound on the desirable signal-to-noise ratio (SNR). The networks discussed in this paper are applied and benchmarked against clustering and signal detection problems.
In this paper, we propose an efficient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-ba...
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