Aiming at the difficulty of achieving interactive simulation between soft tissue and suture, a method to simulate suturing procedure in virtual surgery was proposed. Firstly, mass-spring model was used as the physical...
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Aiming at the difficulty of achieving interactive simulation between soft tissue and suture, a method to simulate suturing procedure in virtual surgery was proposed. Firstly, mass-spring model was used as the physical model for soft tissue. In order to perform the relative motion between two masses of soft tissue realistically, an internal friction coefficient was introduced first. Secondly, the interaction of suture and soft tissue was aralyzed. A simple linear mass-spring model with following the leader algorithm was used to model the suture. A stress aralysis was carried out, when the suture touch the soft tissue, so the interaction process was close to physical reality. The movement of the whole suture computed by following the leader algorithm gave good performance in suture's rigidity characteristics. Finally, the interaction of the two deformable objects in a continuous pattern suture was considered, for each node of the soft tissue model, the node that attached to it was stored. This information can be used to compute the soft tissue deformation well. Experiment shows that it is realistical to simulate the soft tissue deformation and suture. meanwhile, the simulation is in real time.
Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platfo...
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In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves th...
In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN (Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.
With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influ...
With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.
Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in differ...
Given that analysis on the vulnerability of functions is helpful to the detection and improvement of software security, this paper aims to propose an efficient methods to identify the vulnerable nodes (ITVN) in different software by the interdependence of functions. First, the dynamic software execution process was constructed as Software Execution Dependency Network (SEDN) based on Complex network theory. Second, by analyzing the dependency relationship among functions, the algorithm calVulAndScoOfNodes (CVSN) was designed to compute the vulnerability and the affected scope of each node for further analysis. Third, in order to measure the functions vulnerability in the whole software network, the algorithm calVulDegreeOfNodes (CVDN) was put forward to calculate the vulnerable degree of each node. Finally, the Vulnerable Nodes in different software were obtained by ITVN. Experimental results show that the vulnerable nodes selected as important nodes are well-reasoned in software network by testing different software, and the measures are effective for evaluating nodes vulnerability.
Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the ...
Pick and place (PAP) skill learning is a fundamental ability of intelligent robots, such as home service robot. Due to the NP-hard nature of the PAP problem, it takes a long time for an intelligent robot to learn the PAP skill based on current methods. In order to improve the learning efficiency of robot PAP skills, this paper proposes a Soft-DDQN-based PAP skill learning method. Firstly, the Soft-DDQN is proposed by introducing maximum entropy into robot DDQN framework, and the learning goal of Soft-DDQN is to maximize reward and information entropy. Secondly, PAP problem is modelled as a discrete form and Soft-DDQN is applied to solve the PAP problem. Finally, in order to verify the efficiency of Soft-DDQN-based PAP skill learning, comparisons have been given from two standard perspectives and shown that Soft-DDQN improves efficiency of PAP skill learning evidently.
Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the ...
Object tracking is a hot topic in computer vision. In recent years, a large number of trackers has been proposed, in which the deep learning tracker has achieved excellent performance. The real-time capability of the deep learning tracker is not good enough due to the high-complexity of the network structures. This paper proposed an innovative tracking method to solve this problem. There are three important differences between this tracker and the other deep learning trackers. Firstly, the overcomplete basis in the deep learning tracker results in heavy computational cost. In order to reduce the complexity of the network, fewer units are used in the first hidden layer to replace the overcomplete basis. Secondly, a training method combining two observation models is used in the tracking process. The denoising automatic encoder is used in the first layer and the backpropagation is used in the other layers. This can avoid the diffusion of gradients which is caused by BP and adapt to the change of the targets easier. Thirdly, this tracker using adaptive particle filter to track targets. The number of particles is dynamic changes in tracking process. In this paper, we use different kinds of unlab.lled datum to train network and initialize observation model. The observation model uses the samples collected in the tracking to adjust dynamically so as to adapt to the target appearance and complex environment. Compared with the existing methods, the results of experiments in different video sequences show that this tracker has a higher speed and the similar accuracy compared.
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