In this paper a new technique for computing and ray tracing point based geometry is presented. It uses a novel point primitive that is called "Spherical Patch Point" (SPP) to approximate the vicinity of a su...
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In this paper a new technique for computing and ray tracing point based geometry is presented. It uses a novel point primitive that is called "Spherical Patch Point" (SPP) to approximate the vicinity of a surface point. Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer primitives for the cost of a few additional bytes per point and thus makes a significant reduction in rendering time. During pre-process,important attributes are added to each SPP for the purpose of ray tracing. During rendering, an intersection algorithm different from previous ones has been demonstrated to get satisfied results. The proposed technique makes it possible to render high quality ray traced images with global illumination using SPPs. It offers a higher ray tracing speed in comparison with previous methods.
In this paper a new point-based rendering method for ray tracing is presented. An oriented spherical patch that passes a surface point is used to approximate the vicinity of that *** this paper the spherical patch tog...
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In this paper a new point-based rendering method for ray tracing is presented. An oriented spherical patch that passes a surface point is used to approximate the vicinity of that *** this paper the spherical patch together with the surface point is called "Spherical Patch Point" (SPP). Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer points. This paper defines new point attributes for the purpose of efficiently locating the intersection between incoming ray and ***, an algorithm of intersecting a ray with point geometry is proposed. The algorithm can achieve a higher rendering speed in comparison with previous methods. The presented technique deals well with shadow, reflection and refraction.
Large scale terrain visualization with high-resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method b...
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A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN ...
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A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent over fitting the training data just like decision tree learning. DGNN's prune rule is based on the distance threshold. DGNN has some advantages: learning not only is stable because it grows under certain conditions; but also it is faster than back-propagation rules and favorable learned predictive accuracy in small, noisy, online or offline data sets. Three classes of simulations are performed on the primary benchmarks: circle-in-the-square and two-spirals-apart benchmarks are used to check DGNN's supervised learning and compare it with ARTMAP and BP neural networks; DGNN's unsupervised learning ability is checked on UCI Machine Learning Archive's Synthetic Control Chart Time Series data set
In recently years there has been plenty of interest in Random Constraint Satisfaction Problem, both from an experimental and a theoretical point of view. In this paper we study and analyze the four popular problem ins...
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In recently years there has been plenty of interest in Random Constraint Satisfaction Problem, both from an experimental and a theoretical point of view. In this paper we study and analyze the four popular problem instance generating models, and present the extended model B+ based on the most used model B, which has the different domains and constraint tightness meeting some probability distribution function. In the subsequent section we give the relation matrix version of backtracking integrated forward checking algorithms, and introduce the implementation of instances generator and solver based on the new model. Finally we show the experiment results and conclude the paper, point that our extended model B+ has the common phase transition region with the transitional models and it has the advantage of being suited to the testing of heuristic based constraint solving algorithms, such as variables selection heuristic algorithms.
In Containing Order Rough Set Methodology (CORS),terminologies on rules or rules set, such as robust, minimality,completeness, mutuality degree, and conflict are discussed. The rules generation algorithm IGRs is given...
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In Containing Order Rough Set Methodology (CORS),terminologies on rules or rules set, such as robust, minimality,completeness, mutuality degree, and conflict are discussed. The rules generation algorithm IGRs is given and the details of algorithm IGRs are studied. Heuristic knowledge, which is mutuality degree of a condition item with a decision part, is used to choose condition item when generating rules. In primary and modified IGRs, two kinds of mutuality degree,simple and weighted mutuality are introduced respectively. In addition, the variable precision method is used to solve the conflict problem in modified IGRs. By experiments, the effects of two kinds heuristic knowledge and different weight values in synthetic mutuality on algorithms properties are shown,such as time consumption, calculation precision etc. The performances of IGRs with the primary and new conflict solution are compared by experiments.
The conclusion is that the weighted mutuality degree is more sound and the choice of appropriate weight values in it are important to optimize the quality of rules set. The variable precision method for dealing with conflict when generating rules is more reasonable. Both two modifications to primary IGRs make the performance of IGRs enhanced and the quality of rules set better. Algorithm IGRs still need further improvement.
A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational dat...
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In Containing Order Rough Set Methodology (CORS), ordered attribute 'criterion' is introduced. Criterion is related semantically with decision attributes, which results in producing more rational and significa...
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ISBN:
(纸本)1424404754
In Containing Order Rough Set Methodology (CORS), ordered attribute 'criterion' is introduced. Criterion is related semantically with decision attributes, which results in producing more rational and significant rules utilizing dominance relations. In this paper, some terminologies and properties on rules or rules set, such as robust, minimal, complete are discussed, the state of art in algorithms on rules generation are analyzed, and algorithms GRs and IGRs are proposed. We compare these two algorithms by experiments in time complexity, rules count and accuracy. We find that GRs can generate all minimal rules, but time complexity is high and IGRs' efficiency is better, but quality of rules are inferior to that of GRs. We also discuss the completeness of rules set and present a viewpoint that there are three hierarchies for completeness. In accuracy calculation, three formulas for accuracy calculation and two approaches for experimental test are given.
A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Lab...
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A hybrid discrete particle swarm algorithm is presented in this paper to solve open-shop problems. The operations are redefined in the discrete particle swarm algorithm. To improve the performance the simulated anneal...
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