12619937
Content-Based 3D Model Retrieval for Digital Museum
In this paper, we propose a new shape feature for shape-similarity search of 3D polygonal-mesh models in digital museum. The shape feature is an extension of the D2 shape functions proposed by Osada. Our proposed shape feature is a combination of geometry and texture which is also invariant to similarity transformation. Experiments showed that, our method achieved better performance improvement especially for appearance retrieval of 3d model.
7492825
Subdivision Interpolating Polygonization of Implicit Surfaces with Normal Meshes
Normal mesh is a new fundamental surface description, which is multiresolution mesh where each level can be written as a normal offset from a coarser version. In this paper, we present an algorithm to create normal subdivision triangulations approximating implicit surfaces. Our algorithm begins from a coarse base mesh created by a space-division based polygonization method for implicit surface, and the base mesh is then optimized by Laplacian smoothing operator. Subsequently, the faces of the base mesh are subdivided iteratively and the newly created vertices are located on the implicit surface along a certain direction, which depends on the normals of vertices of the faces. Finally, we obtain a piecewise-linear approximation of the surface, which is a normal mesh with subdivision connectivity and provides a multi-resolution description of the implicit surface naturally. Under the same error restriction, the mesh has much fewer data with respect to the traditional polygonization algorithms
207773102
Anisotropic Feature-Preserving Smoothing of 3D Mesh
4332072
A Component Retrieval Method Based on Facet-Weight Self-learning
Component-based development method has been a new software development paradigm. How to get the needed components quickly and accurately is one of the basic problems about reusing software component automatically. In this paper, an intelligent component retrieval model – FWRM. is proposed. Facet presentation is used to model query and component. Multiple types of facets are defined which extends traditional keyword-based facet presentation. Genetic algorithm based facet weight self-learning algorithm can change the facet weight dynamically in order to improve retrieval accuracy. Corresponding similarity functions are defined also. In addition, risk minimization-based component sampling method is used to solve the insufficiency of training data. All these algorithms and methods are integrated into FWRM’s three main implementation parts: Facet-Weight Optimize System, Component Retrieve System and Resource. The experimental results prove that this method is feasible and can improve component retrieval effectively.
4263841
Tree-Structured Conditional Random Fields for Semantic Annotation
The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarch-ical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchic-ally laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.
4275578
Semantic Similarity Based Ontology Cache
This paper addresses the issue of ontology caching on semantic web. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Ontology serves as the metadata for defining the information on semantic web. Ontology based semantic information retrieval (semantic retrieval) is becoming more and more important. Many research and industrial works have been made so far on semantic retrieval. Ontology based retrieval improves the performance of search engine and web mining. In semantic retrieval, a great number of accesses to ontologies usually lead the ontology servers to be very low efficient. To address this problem, it is indeed necessary to cache concepts and instances when ontology server is running. Existing caching methods from database community can be used in the ontology cache. However, they are not sufficient for dealing with the problem. In the task of caching in database, usually the most frequently accessed data are cached and the recently less frequently accessed data in the cache are removed from it. Different from that, in ontology base, data are organized as objects and relations between objects. User may request one object, and then request another object according to a relation of that object. He may also possibly request a similar object that has not any relations to the object. Ontology caching should consider more factors and is more difficult. In this paper, ontology caching is formalized as a problem of classification. In this way, ontology caching becomes independent from any specific semantic web application. An approach is proposed by using machine learning methods. When an object (e.g. concept or instance) is requested, we view its similar objects as candidates. A classification model is then used to predict whether each of these candidates should be cached or not. Features in classification models are defined. Experimental results indicate that the proposed methods can significantly outperform the baseline methods for ontology caching. The proposed method has been applied to a research project that is called SWARMS.
4365783
Research of Satisfying Atomic and Anonymous Electronic Commerce Protocol
Atomicity and anonymity are two important attributes for electronic commerce, especially in payment systems. Atomicity guarantees justice and coincidence for each participant. However traditional atomicity either bias to SELLER or CUSTOMER. Anonymity is also intractable, unanonymous or anonymity both leads to dissatisfying ending. Therefore, it is important to design a system in which satisfying atomicity and revocable anonymity are both enabled. In this paper, based on AFAP(Atomic and Fair Anonymous Protocol), we propose an approach to realize satisfying atomicity. In this method, not only SELLER’s satisfying atomicity but also CUSTOMER’s satisfying atomicity is supported. At the same time, based on Brands’ fair signature model, this method satisfies both anonymity and owner-trace or money-trace.
34234891
The Fuzzy PI Control for the DSTATCOM Based on the Balance of Instantaneous Power
The DSTATCOM regulates the voltage at the point of common coupling (PCC) by injecting reactive power to the PCC, making it meet the requirement of the voltage quality. First, this paper deduces from the balance theory of instantaneous power of the DSTATCOM system the direct output voltage control strategy, in which the current detection circuit is not demanded. Compared with the cascade control strategy, it has the merits of a simple structure and fast response, but it doesn't perform well when the parameters of the system are changed. So the paper proposes the fuzzy PI control to solve the problem. The validity and effectiveness of the control strategy has been verified by theoretical analysis and digital simulation.