High penetration of wind energy in the modern power system exposes the need of smoothing the fluctuating output power in an effective and conducive way. In this context, this paper proposes two novel ...control strategies that utilize the self-capability of permanent magnet synchronous generator-based wind turbine to realize power smoothing. The first strategy pursues to offer power smoothing support via simultaneous utilization of dc-link voltage control, rotor speed control, and pitch angle control. The second control strategy seeks to coordinate the three concerned individual control schemes in a hierarchical manner, where the power smoothing tasks are allocated to individual control modules or their combinations dynamically in line with WT's operation status. Both two strategies are able to provide power smoothing support by fully exploiting wind turbine's self-capability, whereas the second strategy has the merits on 1) reducing the activation frequency of pitch angle control, and 2) enhancing wind energy harvesting. Case studies of the proposed control strategies are carried out to compare and verify their effectiveness in achieving power smoothing.
In the realm of five-axis Computer Numerical Control (CNC) machining, the challenge of minimizing velocity, acceleration, and jerk fluctuations has prompted the development of various local corner ...smoothing methods. However, existing techniques often rely on symmetrical spline curves or impose pre-set transition length constraints, limiting their effectiveness in reducing curvature and maintaining velocity at critical corners. To comprehensively address these limitations comprehensively, a novel approach for local smoothing of five-axis linear toolpaths is presented in this paper. The proposed method introduces two asymmetrical B-splines at corners, effectively smoothing both the tool-tip position in the workpiece coordinate system and the tool orientation in the machine coordinate system. To fine-tune transition curve scales and minimize velocity disparities between adjacent corners, an overlap elimination scheme is employed. Furthermore, the two-step strategy emphasizes the synchronization of tool-tip position and tool orientation while considering maximal approximation error. The outcome is a blended five-axis toolpath that significantly reduces curvature extremes in the smoothed path, achieving reductions ranging from 36.28 % to 45.51 %. Additionally, the proposed method streamlines the interpolation process, resulting in time savings ranging from 5.68 % to 8.78 %, all the while adhering to the same geometric and kinematic constraints.
This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the ...generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.
Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods ...can yield superior performance over the local ones. However, local methods usually run much faster than the global ones. In this paper, we propose a new global method that embeds the bilateral filter (BLF) in the least squares (LS) model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the BLF and the LS model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the BLF with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications.
Fast Scale-Adaptive Bilateral Texture Smoothing Ghosh, Sanjay; Gavaskar, Ruturaj G.; Panda, Debasisha ...
IEEE transactions on circuits and systems for video technology,
07/2020, Volume:
30, Issue:
7
Journal Article
Peer reviewed
In the classical bilateral filter, a range kernel is used together with a spatial kernel for smoothing out fine details while simultaneously preserving edges. More recently, it has been demonstrated ...that even coarse textures can be smoothed using joint bilateral filtering. In this paper, we demonstrate that the superior texture filtering results can be obtained by adapting the spatial kernel at each pixel. To the best of our knowledge, spatial adaptation (of the bilateral filter) has not been explored for texture smoothing. The rationale behind adapting the spatial kernel is that one cannot smooth beyond a certain level using a fixed spatial kernel, no matter how we manipulate the range kernel. In fact, we should simply aggregate more pixels using a sufficiently wide spatial kernel to locally enhance the smoothing. Based on this reasoning, we propose to use the classical bilateral filter for texture smoothing, where we adapt the width of the spatial kernel at each pixel. We describe a simple and efficient gradient-based rule for the latter task. The attractive aspect is that we are able to develop a fast algorithm that can accelerate the computations by an order without visibly compromising the filtering quality. We demonstrate that our method outperforms classical bilateral filtering, joint bilateral filtering, and other filtering methods, and is competitive with the optimization methods. We also present some applications of texture smoothing using the proposed method.
Weak convergence of a distribution does not imply the density converges with respect to the L1 metric. We prove that strong convergence can be established by showing that a smoothed and non-smoothed ...sequence of the densities converge to each other.
Edge-preserving image smoothing is an important step for many low-level vision problems. Though many algorithms have been proposed, there are several difficulties hindering its further development. ...First, most existing algorithms cannot perform well on a wide range of image contents using a single parameter setting. Second, the performance evaluation of edge-preserving image smoothing remains subjective, and there is a lack of widely accepted datasets to objectively compare the different algorithms. To address these issues and further advance the state of the art, in this paper, we propose a benchmark for edge-preserving image smoothing. This benchmark includes an image dataset with ground truth image smoothing results as well as baseline algorithms that can generate competitive edge-preserving smoothing results for a wide range of image contents. The established dataset contains 500 training and testing images with a number of representative visual object categories, while the baseline methods in our benchmark are built upon representative deep convolutional network architectures, on top of which we design novel loss functions well suited for the edge-preserving image smoothing. The trained deep networks run faster than most of the state-of-the-art smoothing algorithms with leading smoothing results both qualitatively and quantitatively. The benchmark will be made publicly accessible.
The gradient smoothing method(GSM) is used to approximate the derivatives of the meshfree shape function and it usually generates the smoothing domain by connecting the midpoints of sides and ...centroids of elements around the interested node. In order to simplify the generating process and geometric computation of smoothing domain, a local gradient smoothing method (LGSM) has been proposed in which the local smoothing domains corresponding to the nodes are independent of each other. The proposed method is very flexible to generate the smoothing domain, which can have a simple and regular shape. The derivatives of the meshfree shape function approximated by the proposed method was applied to the governing strong from of system equations at all nodes of problem domain. The convergence and accuracy of the proposed method according to the size of smoothing domain are examined. Numerical examples to some typical benchmark problems illustrated the efficiency of the proposed method.
•A local smoothing domain whose creation method is very simple, is constructed.•The gradients of meshfree shape function are approximated in local smoothing domain.•Convergence and accuracy are studied according to the size of local smoothing domain.•The proposed method can reduce the CPU time.
Recent studies have demonstrated that a bilateral filter can increase the quality of edge-preserving image smoothing significantly. Different strategies or mechanisms have been used to eliminate the ...brute-force computation in bilateral filters. However, blindly decreasing the processing time of the bilateral filter cannot further ameliorate the effectiveness of filter. In addition, even when the processing speed of the filter is increased, inherent problem occurred in the Gaussian range kernel when facing a noise filtering input and its effect on edge-preserving image smoothing operation are barely discussed. In this letter, we propose a novel Gaussian-adaptive bilateral filter (GABF) to resolve the aforementioned problem. The basic idea is to acquire a low-pass guidance for the range kernel by a Gaussian spatial kernel. Such low-pass guidance lead to a clean Gaussian range kernel for later bilateral composite. The results of experiments conducted on several test datasets indicate that the proposed GABF outperforms most existing bilateral-filter-based methods.
In this article, the adaptive online state smoothing problem is studied for a Markov jump system where the measurement noise covariance matrix (MNCM) is unknown. To address this problem, two adaptive ...IMM online smoothing algorithms are proposed to jointly estimate the target state and the unknown MNCM. Specifically, the article quantitatively examines the impact of noise covariance matrix mismatch on state estimation and theoretically demonstrates that the joint posterior distribution of the target state and the MNCM cannot be analytically obtained using the original variational Bayesian (VB) approach. To overcome this limitation, an approximate VB method is introduced, which utilizes the approximated state distribution obtained through the moment-matching method to update the MNCM. In addition, the convergence criterion of the proposed adaptive smoothing algorithms is designed. Finally, the estimation consistency of MNCM is analyzed. A maneuvering target tracking simulation example is presented to evaluate the effectiveness and applicability of the proposed adaptive algorithms.
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