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 a high-speed vision system for table tennis robot is designed. The system architecture is designed firstly. Then a ball detection algorithm is proposed to improve the image processing speed up to 200 fra...
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In this paper a high-speed vision system for table tennis robot is designed. The system architecture is designed firstly. Then a ball detection algorithm is proposed to improve the image processing speed up to 200 frames per second. Besides, a novel ball trajectory reconstruction algorithm is established, in which the cameras in the stereo vision system needn't be synchronized. Experimental results verify the effectiveness and accuracy of the proposed method.
Probing nanostructures (e.g., nanoelectronics) requires accurate and precise nanopositioning. Furthermore, since measuring I-V data from DC to GHz typically takes more than a minute, little drift is tolerated during t...
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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.
Percutaneous coronary intervention is the gold standard to coronary diseases in the past decades due to much less trauma and quick recovery. However, due to the traits of minimal invasiveness, clinicians have to defea...
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ISBN:
(纸本)9781424479276
Percutaneous coronary intervention is the gold standard to coronary diseases in the past decades due to much less trauma and quick recovery. However, due to the traits of minimal invasiveness, clinicians have to defeat the difficulties in eye-hand coordination during the procedure, which also makes it a non-trivial task in the catheterization lab. The computer-aided surgical simulation is designed to provide a reliable tool for the early stage of the training of the procedure. In this simulation system, the surface model of the vessels contribute the major part in the virtual anatomic environment. On the other hand, heavy interactions between the virtual surgical tools and the model surface occur during the training. In order to achieve acceptable performances, the patient-specific vessel surface model needs further process to adapt to this situation. We proposed in this paper an approach to optimize the meshes that consist the surface model with its application in consideration. The connectivity of the surface model is firstly checked. Next a smooth processing is applied without modifying the geometry of the largest-connected surface. Then the quantities of the polygons consisting the model surface are eliminated both dramatically and appropriately. The resultant surface model is applied in the validation test interacting with the virtual guidewire.
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.
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.
The video surveillance system is widely used in various areas. However, it is an intractable problem to process a large number of video data manually. There are many intelligent systems developed to analyze human beha...
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ISBN:
(纸本)9781479947249
The video surveillance system is widely used in various areas. However, it is an intractable problem to process a large number of video data manually. There are many intelligent systems developed to analyze human behavior and detect unusual events, however, few concerns the cheating behavior in examinations. In this paper, we use video surveillance system to detect cheating behavior in the examination. The pose estimation method is presented to detect the cheating behavior, based on the analysis of the implementation environment. More in detail, the pictorial structure is adopted to model the human body and a required pose and skin color information is utilized to build the human appearance model. Finally, the belief propagation algorithm is used to infer the maximum a posterior pose. The experiment shows the effectiveness of our method.
Recently, the development of minimally invasive surgery has led to the emergence of the new research area: minimally invasive vascular surgery simulation. The goal of the surgery simulation is to provide a new way to ...
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Recently, the development of minimally invasive surgery has led to the emergence of the new research area: minimally invasive vascular surgery simulation. The goal of the surgery simulation is to provide a new way to enable the trainees to obtain the core skills of the techniques. One of the key component of the simulation for core skills training in minimally invasive vascular surgery is how to develop 3D real-time computer-based virtual environments. In this paper, we present the virtual environments targeting to simulate the motion of a catheter/guide wire with complex 3D vascular models. Experimental results show that the 3D virtual environments are effective and promising.
A lower limb rehabilitation robot, namely iLeg, has been developed recently. Since active exercises have been proven to be effective for neurorehabilitation and motor recovery, they are suggested to be implemented on ...
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A lower limb rehabilitation robot, namely iLeg, has been developed recently. Since active exercises have been proven to be effective for neurorehabilitation and motor recovery, they are suggested to be implemented on iLeg. To this goal, patients' motion intention should be recognized. Therefore, a method based on the dynamic model of the human-robot interface (HRI) is designed to recognize the human motion intention. This paper is devoted to modeling and identifying the dynamics of the HRI. Firstly, the dynamic model of the HRI is designed by combining the dynamic models of the human leg and iLeg, where the human leg dynamic model (HLDM) is mainly concerned. By considering the motion trajectories during the rehabilitation exercises provided by iLeg, the human leg can be taken as a manipulator with two degrees of freedom; meanwhile, the joint angles and torques of the human leg can be measured indirectly by using the position and torque sensors mounted on the joints of iLeg. As a result, an 8-parameter HLDM can be designed by using the Lagrangian method. Then, the dynamic model of the HRI is identified by respectively and independently identifying the undetermined dynamic parameters of iLeg and the HLDM, where the dynamic parameters of the HLDM are mainly considered. Finally, the feasibility of the dynamic model of the HRI is validated by experiments.
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