This paper provides an approach to solve the system optimal dynamic traffic assignment problem for networks with multiple O-D pairs. The path-based cell transmission model is embedded as the underlying dynamic network...
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This paper provides an approach to solve the system optimal dynamic traffic assignment problem for networks with multiple O-D pairs. The path-based cell transmission model is embedded as the underlying dynamic network loading procedure to propagate traffic. We propose a novel method to fully capture the effect of flow perturbation on total system cost and accurately compute path marginal cost for each path. This path marginal cost pattern is used in the projection algorithm to equilibrate the departure rate pattern and solve the system optimal dynamic traffic assignment. We observe that the results from projection algorithm are more reliable than those from method of successive average algorithm (MSA). Several numerical experiments are tested to illustrate the benefits of the proposed model. (C) 2014 Elsevier Ltd. All rights reserved.
New algorithms for estimation of the frequencies of oscillating waveform signals are described. A model of the signals is presented in the form of a linear difference equation with unknown coefficients, which define t...
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New algorithms for estimation of the frequencies of oscillating waveform signals are described. A model of the signals is presented in the form of a linear difference equation with unknown coefficients, which define the frequencies and amplitudes. Coefficients are estimated utilizing the property of the persistence of excitation of oscillating signals. Exponentially damped and oscillating signals are described in a unified framework. A property of excitation is proved for an exponentially damped signal that contains a single frequency via diagonal dominance of an information matrix. Two applications of this frequency estimation technique are considered. The first one is filtering of the wind speed signal in wind turbine control applications, and the second one is the frequency estimation of exponentially damped signals motivated by the engine knock detection applications.
This study proposes an adaptive interval type-2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy system with a supervisory mode to control and stabilise a certain class of non-linear fractional order systems. In this study, a fract...
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This study proposes an adaptive interval type-2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy system with a supervisory mode to control and stabilise a certain class of non-linear fractional order systems. In this study, a fractional order adaptation law is derived which adjusts the free parameters and bounds them by utilising a projection algorithm. The global Mittag-Leffler stability of the closed-loop system is proved in the sense that all the involved signals are uniformly bounded. Moreover, if the non-linear system tends to be unstable, a supervisory controller starts cooperating with the adaptive IT2 TSK fuzzy controller to guarantee the stability of the closed-loop system. In addition, a new inference mechanism for the adaptive IT2 TSK fuzzy system is introduced for which the antecedent part is chosen as a type-2 fuzzy set and the consequent parameters are represented as interval sets. According to the practical nature of the proposed inference equation, it would be applicable in online and real-time applications. Numerical simulations show the validity and effectiveness of the introduced control strategy for stabilisation and control of a general class of non-linear fractional order systems perturbed by disturbance and uncertainty.
In this paper, adaptive multi-layer neural network (MNN) control is developed for a class of discrete-time non-affine nonlinear systems in nonlinear auto regressive moving average with eXogenous inputs (NARMAX) model....
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In this paper, adaptive multi-layer neural network (MNN) control is developed for a class of discrete-time non-affine nonlinear systems in nonlinear auto regressive moving average with eXogenous inputs (NARMAX) model. By using implicit function theorem, the existence of the implicit desired feedback control (IDFC) is proved. MNNs are used as the emulator of the desired feedback control. projection algorithms are used to guarantee the boundedness of the neural network (NN) weights, which removes the need of persistent exciting (PE) condition for parameter convergence. Simulation results show the effectiveness of the proposed control scheme. (C) 2004 Elsevier B.V. All rights reserved.
LiDARs are one of the key sources of reliable environmental ranging information for autonomous vehicles. However, segmentation of 3D scene elements (roads, buildings, people, cars, etc.) based on LiDAR point clouds ha...
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LiDARs are one of the key sources of reliable environmental ranging information for autonomous vehicles. However, segmentation of 3D scene elements (roads, buildings, people, cars, etc.) based on LiDAR point clouds has limitations. On the one hand, point- and voxel-based segmentation neural networks do not offer sufficiently high speed. On the other hand, modern labeled datasets primarily consist of street scenes recorded for driverless cars and contain little data for mobile delivery robots or cleaners that must work in parks and yards with heavy pedestrian traffic. This article aims to overcome these limitations. We have proposed a novel approach called DAPS3D to train deep neural networks for 3D semantic segmentation. This approach is based on a spherical projection of a point cloud and LiDARspecific masks, enabling the model to adapt to different types of LiDAR. First of all, we have introduced various high-speed multi-scale spherical projection segmentation models, including convolutional, recurrent, and transformer architectures. Among them, the SalsaNextRecLSTM architecture with recurrent blocks showed the best results. In particular, this model achieved the 83.5% mIoU metric for the SemanticKitti dataset with joint categories. Secondly, we have proposed several original augmentations for spherical projections of LiDAR data, including FoV, flip, and rotation augmentation, as well as a special T-Zone cutout. These augmentations increase the model's invariance when dealing with changes in the data domain. Finally, we introduce a new method to generate synthetic datasets for domain adaptation problems. We have developed two new datasets for validating 3D scene outdoor segmentation algorithms: the DAPS-1 dataset, which is based on the augmentation of the reconstructed 3D semantic map, and the DAPS-2 LiDAR dataset, collected by the on-board sensors of a cleaning robot in a park area. Particular attention is given to the performance of the developed models, demonst
We propose and test a specific correction factor which improves both the resampling and projection algorithm for approximating the minimum volume ellipsoid (MVE) estimator. Simulations show that a high-breakdown-point...
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We propose and test a specific correction factor which improves both the resampling and projection algorithm for approximating the minimum volume ellipsoid (MVE) estimator. Simulations show that a high-breakdown-point GM estimator, based among other things on these improved MVE-estimates of location and scatter (i) is little less efficient than OLS if data are free from outlying observations, but in most cases is much more efficient if outliers corrupt the data, (ii) is always more efficient than Rousseeuw's least median of squares (LMS) estimator, and (iii) is always superior to both LMS and OLS if both precision and efficiency are considered. (C) 1997 Elsevier Science B.V.
We propose a hierarchical (BV, G) variational decomposition model for multiscale texture extraction in this paper, which can offers a hierarchical, separated representation of image texture in different scales. The pr...
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We propose a hierarchical (BV, G) variational decomposition model for multiscale texture extraction in this paper, which can offers a hierarchical, separated representation of image texture in different scales. The proposed hierarchical decomposition is obtained by replacing the fixed scale parameter of the A(2)BC model with a varying sequence. Some properties of this hierarchical decomposition are presented and its convergence is proved. We adopt Euclidean projection algorithm to solve this hierarchical decomposition model numerically. In addition, we use this hierarchical decomposition to achieve the multiscale texture extraction. The performance of the proposed model is demonstrated with both synthetic and real images. (C) 2015 Elsevier GmbH. All rights reserved.
The application of conventional wind turbines on board a vessel creates a number of new challenges for turbine control systems. Thrust force is used in marine applications as an additional control variable for propuls...
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The application of conventional wind turbines on board a vessel creates a number of new challenges for turbine control systems. Thrust force is used in marine applications as an additional control variable for propulsion of the vessel. Variability of the wind speed and wind direction, together with the yaw rate and range constraints, impose strong requirements on the turbine control system. A new projection algorithm that projects the turbine thrust force on the heading direction of vessel is proposed in this paper for controlling propulsion. Variations in the wind direction and wind speed are counteracted via turbine yaw angle, making the turbine thrust force always aligned with the heading direction of the vessel. The conventional speed controller is modified for varying yaw offset and a combined algorithm for simultaneous control of the turbine speed and thrust force is proposed. Stability of the speed controller for varying yaw offset is proved via the Lyapunov method. The controller also takes into account the constraints on the yaw rate and minimizes the turbine gyroscopic effects via a proper choice of the virtual upper bound of the input voltage of the yaw motor. All of the results are illustrated by simulations using measurement data acquired from the Hono turbine.
In order to eliminate mesh folding in 3D image registration problem, we propose a 3D diffeomorphic image registration model with Cauchy-Riemann constraint and lower bounded deformation divergence. This model preserves...
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In order to eliminate mesh folding in 3D image registration problem, we propose a 3D diffeomorphic image registration model with Cauchy-Riemann constraint and lower bounded deformation divergence. This model preserves the local shape and ensures no mesh folding. The existence of solution for the proposed model is proved. Furthermore, an alternating directional projection 3D image registration algorithm is presented to solve the proposed model. Moreover, numerical tests show that the proposed algorithm is competitive compared with the other four algorithms.
While the original classical parameter adaptive controllers do not handle noise or unmodelled dynamics well, redesigned versions have been proven to have some tolerance;however, exponential stabilization and a bounded...
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While the original classical parameter adaptive controllers do not handle noise or unmodelled dynamics well, redesigned versions have been proven to have some tolerance;however, exponential stabilization and a bounded gain on the noise are rarely proven. Here we consider a classical pole placement adaptive controller using the original projection algorithm rather than the commonly modified version;we impose the assumption that the plant parameters lie in a convex, compact set, although some progress has been made at weakening the convexity requirement. We demonstrate that the closed-loop system exhibits a very desirable property: there are linear-like convolution bounds on the closed-loop behaviour, which confers exponential stability and a bounded noise gain, and which can be leveraged to prove tolerance to unmodelled dynamics and plant parameter variation. We emphasize that there is no persistent excitation requirement of any sort;the improved performance arises from the vigilant nature of the parameter estimator.
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