视频内容分析技术

概述了基于内容的视频检索的方法和工作过程,研究的重点是突变镜头探测(基于像素的方法、模板匹配法、基于直方图的方法、基于视频特征的方法),渐变镜头探测(双重比较法、基于模型的方法、基于压缩域的方法),关键帧提取的关键技术(基于颜色特征的方法,基于镜头边界的方法、基于镜头的方法、基于运动分析的方法等),在综合分析了各类方法的工作机理和优缺点的基础上,提出了一个优化的视频内容分析检索框架.     

虚拟场景中基于空间域的碰撞检测算法研究

从空间域角度,详细分析了基于空间分割、基于层次包围盒和基于GPU的碰撞检测算法。对于基于空间分割的碰撞检测算法,详细介绍了八叉树和BSP算法;对于基于层次包围的碰撞检测,详细介绍了包围球、AABB、OBB和K-DOP算法;基于GPU的碰撞检测则分析了基于图像空间和基于GPU流计算的算法。最后,对各算法的优缺点进行了分析和比较。     

一种格上基于身份的环签名方案

基于身份的环签名是基于身份密码学和环签名的结合,具有较高的实际应用价值.现有的基于身份的环签名方案大多基于双线性对问题.然而,双线对问题在量子环境下是不安全的.为了设计量子环境下安全的基于身份的环签名方案,本文基于格困难假设,提出一种标准模型下基于身份的格上环签名方案.该方案的安全性基于格中标准的小整数解(SIS)困难假设.与其他标准模型下基于身份的格上环签名方案相比,该签名方案的计算效率进一步提高.     

基于软件的虚拟现实关键技术研究

首先,从软件和硬件方面,它探讨了构建虚拟现实系统的技术体系;分析了基于软件和硬件构建虚拟现实系统的优点和缺点,从而得出基于软件构建虚拟环境是目前研究的热点。其次,虚拟现实建模技术是基于软件的虚拟现实关键技术。从基于图像的方法和基于建模的方法出发,对虚拟现实的关键技术作了详尽的探讨,其中重点研究了基于建模的方法。最后,对基于图像的方法和基于建模的方法的融合来构建虚拟现实系统,进行了深入研究,得出了构建基于软件的虚拟现实体系结构。     

椭圆曲线同源的有效计算研究进展

由于Shor算法可以在多项式时间内解决大整数分解以及离散对数问题,使得基于这些问题设计的经典的密码体制不再安全.目前涌现出许多后量子密码体制的研究,如基于格、基于编码、基于多变量和基于椭圆曲线同源的密码系统.相比于其他后量子密码体制,基于椭圆曲线同源的密码系统具有密钥尺寸短的优势,然而其实现效率不占优势.以两类基于超奇...     

软件安全性检测技术综述

该文阐述了网络软件安全检测的重要性,介绍了现有的主要检测方法,包括形式化安全测试、基于模型的安全功能测试、语法测试、基于故障注入的安全测试、基于属性的测试、模糊测试、基于风险的安全性测试、基于故障树的安全性测试以及基于渗透的安全性测试。     

软件安全性检测技术综述

该文阐述了网络软件安全检测的重要性,介绍了现有的主要检测方法,包括形式化安全测试、基于模型的安全功能测试、语法测试、基于故障注入的安全测试、基于属性的测试、模糊测试、基于风险的安全性测试、基于故障树的安全性测试以及基于渗透的安全性测试。     

信息集成技术在数字图书馆中的应用研究

分析讨论了数字图书馆中的信息集成技术：基于CORBA的集成,基于XML的集成,基于OAI的集成和基于知识的智能集成,并指出了各自的优缺点;给出了一个基于中介结构实现的数字图书馆集成系统。     

面向对象软件的类测试技术

主要介绍了面向对象软件的类测试技术.从基于服务的、基于状态的、基于流图的以及基于规约的四个方面论述了类测试的思想和方法.     

基于视觉的目标定位技术的研究进展

基于视觉的目标定位是目前计算机视觉领域的研究热点。就现阶段基于视觉的定位技术进行了综述,着重介绍了基于单目视觉定位、基于双目视觉定位和基于全景视觉定位3类定位技术的研究现状,并分析了各自的优缺点。最后就基于视觉定位方法的发展趋势做了简要分析,以期为以后基于视觉定位问题的研究提供参考。     

A patent intelligence system for strategic technology planning

Patent intelligence—the transformation of content found in multiple patents into technical, business, and legal insight—is considered a key factor in gaining a competitive advantage in technologically competitive business environments. Although keyword-based patent intelligence tools are widely used due to their simplicity and ease of use, they are limited in that they cannot represent key technological concepts and inventive knowledge by relying only on the frequency of occurrence of defined keywords. As a remedy, this paper proposes a Subject–Action–Object (SAO)-based patent intelligence system. SAO structures that can be extracted from textual patent information are known as the expertise and inventive findings of the relevant patent. On the basis of semantic analysis of patent SAO structures, our proposed intelligence system constructs patent maps and patent networks. Building on the maps and networks, the system provides specific functionalities including identification of technology trends and significant patents, detection of novel technologies, and identification of potential infringement. This paper describes the architecture of our proposed patent intelligence system in detail, and illustrates the system’s functionalities using case studies. We anticipate that our proposed system will be incorporated into the technology planning process to assist experts in the formulation of technology strategies.     

A fact-oriented ontological approach to SAO-based function modeling of patents for implementing Function-based Technology Database

Function-Oriented Search (FOS) has been proposed as a tool for use in searching patent databases to find existing solutions to new problems. To implement FOS effectively, a well-structured Function-based Technology Database (FTDB) is required. An FTDB is a data repository of technology information represented as “function”. To implement an FTDB, four features should be addressed: continual data updating, limited area searching, function generalization, and semantics handling. In this paper, we consider these features to suggest a fact-oriented ontological approach to implementing an FTDB by Subject–Action–Object (SAO)-based function modeling of patents. The proposed approach uses fact-oriented ontology modeling of SAO structures extracted from patent documents, and implements an FTDB, which is an SAO-based patent retrieval system to support FOS. We also verify the feasibility of the proposed approach to by using it to conduct case studies of patent retrieval.     

Automated patent landscaping

Patent landscaping is the process of finding patents related to a particular topic. It is important for companies, investors, governments, and academics seeking to gauge innovation and assess risk. However, there is no broadly recognized best approach to landscaping. Frequently, patent landscaping is a bespoke human-driven process that relies heavily on complex queries over bibliographic patent databases. In this paper, we present Automated Patent Landscaping, an approach that jointly leverages human domain expertise, heuristics based on patent metadata, and machine learning to generate high-quality patent landscapes with minimal effort. In particular, this paper describes a flexible automated methodology to construct a patent landscape for a topic based on an initial seed set of patents. This approach takes human-selected seed patents that are representative of a topic, such as operating systems, and uses structure inherent in patent data such as references and class codes to “expand” the seed set to a set of “probably-related” patents and anti-seed “probably-unrelated” patents. The expanded set of patents is then pruned with a semi-supervised machine learning model trained on seed and anti-seed patents. This removes patents from the expanded set that are unrelated to the topic and ensures a comprehensive and accurate landscape.     

Identification of promising patents for technology transfers using TRIZ evolution trends

Technology transfer is one of the most important mechanisms for acquiring knowledge from external sources to secure innovative and advanced technologies in high-tech industries. For successful technology transfer, identification of high-value technologies is a fundamental task. In particular, identifying future promising patents is important, because most technology transfer transactions are aimed at acquiring technologies for future uses. This paper proposes a new approach to identification of promising patents for technology transfer. We adopted TRIZ evolution trends as criteria to evaluate technologies in patents, and Subject–Action–Object (SAO)-based text-mining technique to deal with big patent data and analyze them automatically. The applicability of the proposed method was verified by applying it to technologies related to floating wind turbines.     

基于SAO结构的非分类关系抽取研究

针对非分类关系抽取中的关系识别问题,提出利用SAO结构和依存句法分析相结合的识别方法。该方法将中文专利领域的非分类关系抽取问题转化为符合SAO结构的识别问题,通过SAO结构中的动词信息可以解决关系识别的问题,并在此基础上,利用依存句法分析得到的依存关系强度结合传统的特征,分别对新特征、词特征、上下文特征、距离特征的有效性进行验证分析。实验结果表明,该方法优于传统方法,也验证了依存句法分析在非分类关系抽取中的可行性。     

An information entropy and latent Dirichlet allocation approach to noise patent filtering

Defining valid patents in a particular technological field is an indispensable step in patent analysis. To minimise the risk of missing valid patents, domain experts manually exclude irrelevant patents, known as noise patents, from an initial patent set derived using a loose retrieval query. However, this task has become time-consuming and labour intensive due to the increasing number of patents and rising complexity of technological knowledge. This study proposes a semi-automated approach to noise patent filtering based on information entropy theory and latent Dirichlet allocation. The proposed approach comprises four discrete steps: (1) structuring patents using a term-weighting method; (2) recommending noise patent seeds based on the information quantity of patents in terms of focal keyword groups; (3) measuring text similarities for patent clustering using latent Dirichlet allocation; and (4) identifying potential noise patent clusters with respect to the noise patent seeds. Our case study confirms that the proposed approach is valuable as a complementary noise patent filtering tool that will enable domain experts to focus more on their own knowledge-intensive tasks such as prior art analysis and research and development (R&D) strategy formulation.     

Structure clustering for Chinese patent documents

This paper aims to cluster Chinese patent documents with the structures. Both the explicit and implicit structures are analyzed to represent by the proposed structure expression. Accordingly, an unsupervised clustering algorithm called structured self-organizing map (SOM) is adopted to cluster Chinese patent documents with both similar content and structure. Structured SOM clusters the similar content of each sub-part structure, and then propagates the similarity to upper level ones. Experimental result showed the maps size and number of patents are proportional to the computing time, which implies the width and depth of structure affects the performance of structured SOM. Structured clustering of patents is helpful in many applications. In the lawsuit of copyright, companies are easy to find claim conflict in the existent patents to contradict the accusation. Moreover, decision-maker of a company can be advised to avoid hot-spot aspects of patents, which can save a lot of R&D effort.     

A rough set based approach to patent development with the consideration of resource allocation

Patent strategy is the overriding mechanism that helps direct investment, resource allocation, expectations, and policy development within an organization. Much studies of patent, for example, patent classification, patent analysis, patent management, patent strategy planning have been made. Due to the high cost of devoting to the research and development for a new patent application, it is essential for a company to develop the patent portfolio based on analyzing related information for fitting with cost constraint and maximizing the benefit. However, only few research attempts to develop new patents with the consideration of resource allocation, for example, optimizing budget utilization. In addition, the previous studies did not derive significant technologies and induct rules for resource allocation through patent analysis. In some cases, the patent analysis may process qualitative information that is difficult to analyze by standard statistical techniques. The rough set approach, which is suitable for processing qualitative information, is required to induct decision rules to derive critical technologies of patents. In this paper, a systematic approach to analyze existing patent information based on rough set theory with the consideration of resource allocation is developed. A case study is presented to demonstrate the contribution of the proposed approach which assists on decision-making in patent reform or invention with constraint resource.     

Knowledge-based vector space model for text clustering

This paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM.     

Development of a patent document classification and search platform using a back-propagation network

In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.