This paper addresses the tuning problem of a proportional-integral-derivative(PID) controller with notch filter for flexible space structure model based on the particle swarm optimization (PSO). For flexible structure...
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The conventional A* algorithm may suffer from the infinite loop and a large number of search data in the process of motion planning for manipulator. To solve the problem,an improved A* algorithm is proposed in this pa...
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The conventional A* algorithm may suffer from the infinite loop and a large number of search data in the process of motion planning for manipulator. To solve the problem,an improved A* algorithm is proposed in this paper by the means of selecting middle points and applying variable step segments searching during the searching process. In addition,a new method is proposed for collision detection in the workspace. In this paper,the MOTOMAN MH6 manipulator with 6-DOF is applied for motion plan. The algorithm is based on the basis of the simplification for the manipulator and obstacles by cylinder enveloping. Based on the analysis of collision detection,the free space can be achieved which makes it possible for the entire body to avoid collisions with obstacles. Compared with the Conventional A*,the improved algorithm deals with less searching points and performs more efficiently. The simulation developed in VC + + with OpenGL and the actual system experiments prove effectiveness and feasibility of this improved method.
Fast single image dehazing has been a challenging problem in many fields, such as computer vision and real-time applications. The existing image dehazing algorithms cannot achieve a trade-off between the dehazing perf...
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Fast single image dehazing has been a challenging problem in many fields, such as computer vision and real-time applications. The existing image dehazing algorithms cannot achieve a trade-off between the dehazing performance and the computational complexity. The proposed approach first applies the mean filter twice to estimate airlight, which include pixel-based dark channel and bright channel constraints. And then the relationship between channel values of the restored image and atmospheric light is qualitatively analyzed to give the optimum estimate of atmospheric light. Using the airlight and atmospheric light, we can easily restore the scene radiance via the atmospheric scattering model. Compared with others, the main advantage of the proposed approach is its high speed and significant visibility improvement even in the sky and white areas. This speed allows the enhanced haze image to be applied in real-time processing applications. A comparative study and quantitative evaluation are proposed with a few other state of the art algorithms which demonstrates that similar or better quality results are obtained.
Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, a...
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ISBN:
(纸本)9781509061839
Transfer learning has attracted more and more attention, and many scholars proposed some useful strategies. Boosting is the main strategy for transfer learning. In boosting, resampling is preferred over reweighting, and it can be applied to any base learner. In this paper, we propose a weighted-resampling method for transfer learning, called TrResampling. Firstly, resampling is applied to the data with heaven weight in the source domain, and the resampled data is used with the target data as the training data to build a classifier. Then the TrAdaBoost algorithm is used to adjust the weights of source data and target data. We discuss decision Tree, Naive Bayes, and SVM as the base learner in TrResampling, and choose the suitable for TrResampling. In order to illustrate the performance of the proposed algorithm, we compare TrResampling with the state-of-the-art algorithm TrAdaBoost and the base learner decision Tree, experimental results on UCI data sets indicate that TrResampling is superior to TrAdaBoost and decision Tree on many data sets.
Scale space generation is a fundamental problem in almost all feature extraction algorithms. Often, it is a critical prior step of most image/video analytic applications that are based on the invariance or co-invarian...
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ISBN:
(纸本)9781467374439
Scale space generation is a fundamental problem in almost all feature extraction algorithms. Often, it is a critical prior step of most image/video analytic applications that are based on the invariance or co-invariance of local features, such as SIFT based recognition, matching, and tracking applications. However, it is still quite a challenging problem to enable real-time applications of the extraction of local features due to the fact that scale space generation has a rather large computational complexity. This paper proposes the optimal FPGA design for acceleration of scale space generation. First, in order to derive the mathematical model for scale space generation that fits best in with the FPGA, we have discarded the conventional template convolution based Gaussian filtering scheme and adopted a novel IIR filter based recursive Gaussian blurring algorithm. Then, an approach based on the Retiming technique, which could find the minimal operational period for any given IIR filter, is used to finalize the overall design. For 1024×768 video, the proposed design is able to generate scale spaces at almost 400 fps, which is fast enough to support most real-time applications like object recognition, object matching, and 3D reconstruction.
The integrated navigation system can improve the navigation accuracy utilizing the redundant *** Extended Kalman Filter(EKF) is commonly used in the integrated navigation *** usually takes only one-order of Taylor exp...
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ISBN:
(纸本)9781467383196
The integrated navigation system can improve the navigation accuracy utilizing the redundant *** Extended Kalman Filter(EKF) is commonly used in the integrated navigation *** usually takes only one-order of Taylor expansion and needs to calculate the Jacobian matrix,which will affect the accuracy and numerical stability of the system *** in this paper the application of Square-Root Cubature Kalman Filter(SCKF) method was proposed to solve the above problems in the SINS/CNS integrated navigation *** simulation results show that the position,velocity and attitude errors are reduced effectively compared with the EKF *** SCKF method is more suitable for the state estimation problems in integrated navigation system.
Dealing with time delayed measurements is a key problem of cooperative navigation for multiple autonomous underwater vehicles(MAUVs).In this work,in order to solve the invalidation of location due to the time delayed ...
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ISBN:
(纸本)9781467383196
Dealing with time delayed measurements is a key problem of cooperative navigation for multiple autonomous underwater vehicles(MAUVs).In this work,in order to solve the invalidation of location due to the time delayed measurements,an algorithm which based on the augmented extended Kalman filter(AEKF) is *** algorithm expands the variable dimension of slave AUV according to the time delayed measurements,and infers the fundamental equations of *** to the traditional EKF method,simulation results prove that AEKF has higher positioning accuracy.
The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye's visual mechanism, unartful 3D scene structure...
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The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye's visual mechanism, unartful 3D scene structure design, or bad viewing condition always emerges poor depth perception experience or even physiological discomfort during the watching time, which is often sub-optimal for mass high-quality 3D display productions. To solve this problem, we propose a novel 3D display parallel system for depth sense optimization and it empirically guides how the light field should be re-rendered. Structurally, the parallel system consists of an artificial perception measurement system, a display evaluation model and a light field display rendering system, which includes the display calibration, scene capture, light field data processing and display. Particularly, the system can systematically analyze and model various factors affecting the depth sense which learned through the measurement system, like scene structure, objects’ speeds in 3D video and so on. And those sense factors can be personally modified or increased according to the viewer's demands or technical improvement. Moreover, the light field could be real-time re-rendered, based on some image processing technology, optical flow analysis and object segmentation (or tracking) (especially the one-shot video segmentation). Theory and algorithms are developed and experimental validation results show a superior performance.
A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status ...
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ISBN:
(纸本)9781509024100
A mount of recent researches on scene parsing and semantic labeling, while few focus on obtaining joint semantic motion labeling. In this paper, we propose an approach to infer both the object class and motion status for each pixel of images. First, we extract and match sparse image features to estimate ego-motion between two consecutive stereo images, the result of feature points grouping is used to segment moving object in U-disparity map. Second, a Fully Convolutional Neural Network is employed for semantic segmentation. Moreover, semantic cues are utilized to remove pixels have no potential to be moved in motion mask. Finally, we use a fully connected CRF to integrate motion into semantic segmentation. To validate the effectiveness of the proposed algorithm, we present experimental results with KITTI stereo images that contain moving objects.
A flatness based robust active disturbance rejection control technique is proposed for the trajectory tracking problem of a special under-actuated system known as four-wheel-steering car. The under-actuated problem of...
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A flatness based robust active disturbance rejection control technique is proposed for the trajectory tracking problem of a special under-actuated system known as four-wheel-steering car. The under-actuated problem of such nonlinear coupled system consisting of three degrees of freedom is settled by exploiting its flatness property to obtain an input-output map. Active disturbance rejection control (ADRC), which shows many advantages in estimating and compensating the lumped effects is then utilized. Several simulations are carried out to show the improvements in vehicle handling and stability of the flat based high-order linear ADRC scheme.
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