In recent years, rapid development of minimally invasive surgery has taken place. Virtual reality simulator enables the trainees to obtain the core catheter/guide wire handling skills and to decrease the error rate of...
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ISBN:
(纸本)9781479937097
In recent years, rapid development of minimally invasive surgery has taken place. Virtual reality simulator enables the trainees to obtain the core catheter/guide wire handling skills and to decrease the error rate of operation prior to performing them on a real patient. In this paper, we present and evaluate a collision response algorithm and a force feedback computing method for simulating a catheter/guide wire in the interactive 3D virtual realty simulator based on a robotic catheter/guide wire operating system. In order to provide a real-time virtual environment, a multi-threading technology is used to accelerate the medical simulation procedure. Finally, we test the virtual catheter/guide wire with a complex and realistic 3D vascular model, which is generated from computed tomography angiography(CTA) series in DICOM datasets captured in a actual patient. The results show that the collision response algorithm in the system is effective and promising.
In this paper, we develop a data-driven iterative adaptive dynamic programming algorithm to learn offline the approximate optimal control of unknown discrete-time nonlinear systems. We do not use a model network to id...
ISBN:
(纸本)9781479914821
In this paper, we develop a data-driven iterative adaptive dynamic programming algorithm to learn offline the approximate optimal control of unknown discrete-time nonlinear systems. We do not use a model network to identify the unknown system, but utilize the available offline data to learn the approximate optimal control directly. First, the data-driven iterative adaptive dynamic programming algorithm is presented with a convergence analysis. Then, the error bounds for this algorithm are provided considering the approximation errors of function approximation structures. To implement the developed algorithm, two neural networks are used to approximate the state-action value function and the control policy. Finally, two simulation examples are given to demonstrate the effectiveness of the developed algorithm.
This paper describes a recognition method for the initial and end points of lap joints based on the image processing techniques. The edge detection technique is used in order to find the edges of the image. And the im...
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This paper describes a recognition method for the initial and end points of lap joints based on the image processing techniques. The edge detection technique is used in order to find the edges of the image. And the image processing techniques are employed in order to link the broken edges and find T-junctions of the image which are determined as candidates for the initial and end points of the weld seam. The T-junctions that match the conditions of angles checking and coordinate comparing will be detected as initial and end points of weld seam. The proposed method mainly insists of the 4 steps namely 1) smoothing the image and extracting edges of the image by using Canny operator 2)linking broken edges 3)detecting T-junctions and 4)analysing the initial and end points of weld seams. The experimental results showed that the initial and end points of the lap joints can be precisely recognized using the methods proposed in this paper.
In Still-to-Video (S2V)face recognition, only a few high resolution images are enrolled for each subject, while the probe is videos of complex variations. As faces present distinct characteristics under different scen...
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In Still-to-Video (S2V)face recognition, only a few high resolution images are enrolled for each subject, while the probe is videos of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. In this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenarios (still, video), and further pursue a common discriminant space based on these mappings. Concretely, by modeling each video as a set of local models, we form the scenario-oriented mapping learning as an Image-Model discriminant analysis framework. The learning objective is formulated by incorporating the intra-class compactness and inter-class separability for good discrimination. Moreover, a weighted learning scheme is introduced to concentrate on the discriminating information of the most confusing samples and then further enhance the performance. Experiments on the COX-S2V dataset demonstrate the effectiveness of the proposed method.
In this paper, we introduce a metric learning approach for the classification process in the recognition procedure for P300 waves in electroencephalographic (EEG) signals. We show that the accuracy of support machine ...
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ISBN:
(纸本)9781479946037
In this paper, we introduce a metric learning approach for the classification process in the recognition procedure for P300 waves in electroencephalographic (EEG) signals. We show that the accuracy of support machine vector (SVM) classification is significantly improved by learning a similarity metric from the training data instead of using the default Euclidean metric. The effectiveness of the algorithm is validated through experiments on the dataset II of the brain-computer interface (BCI) Competition III (P300 speller).
In this paper, the problem of parameter estimation is investigated for a class of discrete-time linear systems with missing outputs and outliers. It is assumed that the unknown parameters are constant and the evolutio...
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In this paper, the problem of parameter estimation is investigated for a class of discrete-time linear systems with missing outputs and outliers. It is assumed that the unknown parameters are constant and the evolution of the systems is subject to some unknown but bounded noises. To solve such a problem, a recursive algorithm based on set membership identification is proposed and employed. Besides, the data validation is processed and outliers are set aside. The effectiveness of the algorithm is verified by performing some numerical simulations.
This paper studies the stability of state estimation for a discrete-time linear stochastic system, the states of which are measured by multiple sensors and transmitted over multiple wireless channels. Random packet lo...
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ISBN:
(纸本)9781479947249
This paper studies the stability of state estimation for a discrete-time linear stochastic system, the states of which are measured by multiple sensors and transmitted over multiple wireless channels. Random packet loss process introduced by each wireless channel is modeled by an independent and identically distributed(i.i.d.) Bernoulli process. The estimation strategy designed in this paper is based on Covariance Intersection fusion of local state estimates of the observable subsystem of each sensor. The sufficient conditions, imposing constraint on the packet success probability of each channel, are established by taking into account each observable subsystem structure to guarantee the expectation of the trace of estimation error covariance matrices is exponentially bounded, and the upper bound is given. Simulation examples are provided to demonstrate the effectiveness of the results.
An online reinforcement learning algorithm is proposed in this paper to directly utilizes online data efficiently for continuous deterministic systems without system parameters. The dependence on some specific approxi...
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ISBN:
(纸本)9781479945511
An online reinforcement learning algorithm is proposed in this paper to directly utilizes online data efficiently for continuous deterministic systems without system parameters. The dependence on some specific approximation structures is crucial to limit the wide application of online reinforcement learning algorithms. We utilize the online data directly with the kd-tree technique to remove this limitation. Moreover, we design the algorithm in the Probably Approximately Correct principle. Two examples are simulated to verify its good performance.
Visualization model of the coronary vasculature is of utmost importance for the diagnosis of the coronary heart diseases, as well as the planning and navigation of the intravascular surgery. To protect the cardiologis...
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Visualization model of the coronary vasculature is of utmost importance for the diagnosis of the coronary heart diseases, as well as the planning and navigation of the intravascular surgery. To protect the cardiologists and operation staff against the ionizing radiation, surgical robots are designed and come to assist the practitioners during the interventional procedure. Robotic surgical simulation aims to provide effective, economic and convenient support for the learners with the surgical details as real as possible. In building this system, the geometric model of the blood vessels especially the coronary arteries are the key part of the virtual anatomic scenario. Because of the complex topologies,the segmentation of the coronary arteries is full of challenges. We developed in this paper a semi-automatic approach for this challenging work. The approach is based on an improved geodesic active contours model called ***, the region that contains the whole heart in the original images are completely extracted. Secondly, the extracted volumetric data is smoothed and thresholded in order to remove noises and irrelevant details. Next the image features are generated by calculating the gradients pixel-wisely, while the initial contours are generated by a modified fast marching method. Then the contour evolution is provoked to segment the boundaries of the coronary arteries. Finally the surface model is visualized after information is organized by using the marching cubes method. Experimental results showed the capability of the proposed approach in the segmentation of the coronary arteries tree.
The paper investigates a path planning and following algorithm of observing targets for an underwater vehicle-manipulator system(UVMS). The algorithm including the operational task assignment and path following for th...
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The paper investigates a path planning and following algorithm of observing targets for an underwater vehicle-manipulator system(UVMS). The algorithm including the operational task assignment and path following for the UVMS is proposed. The operational task assignment consists of path planning and task assignment. Path planning provides planned trajectory distance between any two targets for task assignment, and task assignment produces a suitable task order for the minimum of the UVMS sailing distance. In path planning, the shortest path considering UVMS nonholonomic constraints-Dubins curve, is employed. It is implemented by matrix transformation. Then a digraph is generated by marking the shortest distance between any two targets. Hence the task assignment problem is considered as a traveling salesman problem, which can be achieved by the genetic algorithm. Finally a path-following guidance method is employed. Simulation results show the effectiveness of the proposed method.
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