A new technique is presented for computing continuous shape transformations between polyhedral objects. The polyhedron shape transformations can be divided into polyhedron metamorphosis and bi-directional local rigid ...
A new technique is presented for computing continuous shape transformations between polyhedral objects. The polyhedron shape transformations can be divided into polyhedron metamorphosis and bi-directional local rigid body rotation transformation. By decomposing two objects into sets of individual convex sub-objects respectively, and establishing the matching between two subsets, the approach can solve the metamorphosis problem of two non-homotopic objects (including concave objects and holey objects). Compared with other methods, this metamorphosis algorithm can be executed automatically for arbitrary polyhedrons and no need user interaction. The user has the ability to choose an automatic matching or to select interactively pairs of corresponding matching convex subsets to obtain special effects. Experiments show that this method can generate natural, high-fidelity, eye-pleasing metamorphosis results with simple computation.
We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul...
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It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper co...
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It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied.
Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visu...
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Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action ...
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The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme lea...
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The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter γ, the polynomial kernel parameter q and the mixing weight coefficient η. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.
Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey...
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This paper investigates the problem of global coordinated tracking of a multi-agent system with input additive uncertainties and disturbances via bounded control inputs. Scheduled low-and-high gain feedback-based dist...
This paper investigates the problem of global coordinated tracking of a multi-agent system with input additive uncertainties and disturbances via bounded control inputs. Scheduled low-and-high gain feedback-based distributed coordinated tracking protocols are developed. It is shown that, under the assumptions that each agent is asymptotically null controllable with bounded controls and the network is connected, global coordinated tracking of the multi-agent system can be achieved. We finally show some numerical simulations to verify and illustrate the theoretical results.
All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendl...
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Speckle is a granular noise that inherently exists in all types of coherent imaging systems. This paper presents a quantitative study on five despeckling methods such as frost filter, kuan filter, speckle reducing an ...
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Speckle is a granular noise that inherently exists in all types of coherent imaging systems. This paper presents a quantitative study on five despeckling methods such as frost filter, kuan filter, speckle reducing an isotropic diffusion, homomorphic filter and wavelet filter. We select six objective evaluation parameters, such as signal-to-ratio, contrast signal-to-noise ratio, figure of merit, least absolute error, edge protection factor, signal-to-minimum mean square error ratio, to quantify the performance of these filters. The comparative study will provide some parameter comparative reference for selecting a suitable filter in the ultrasound imageprocessing.
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