Efficient Sports Video Retrieval Based on Index Structure
视频片断检索是视频领域的研究热点，为了提高查询效率，利用高维索引结构Vector-Approximation File（VA-File）来组织视频子片段，并采用新的相似度模型和基于限定性滑动窗口的高效视频检索算法进行视频片段检索．提出的子片段的分割算法能够较好地区分运动的细节动作，且相似度模型充分考虑了对应子片段之间的视觉相似性以及时间顺序关系，因此对于运动视频的检索十分有效．实验证明，对于运动视频片段检索不仅具有较高的查询效率，而且能够得到较高的查全率和准确率．

title: Semantic Information Extraction of Video Based on Ontology and Inference
abstract: In this paper, a new ontology-based composite concept detection method is proposed, which adopts Bayesian network to construct the ontology and uses the inference rules to perform the composite concept detection providing the concrete concepts in a phrase of video. Furthermore, the probability instead of binary value is gained through the inference pattern of Bayesian network, which can obtain more precise results. The main contribution of this paper is that semantic concept ontology is constructed using Bayesian network and the constructed ontology represents the hierarchical relationship between the concepts which can be used for inference. The method narrows the influence of "Semantic Gap" in some extent and achieves good performance in composite concept detection.

title: A Novel Video Searching Model Based on Ontology Inference and Multimodal Information Fusion
abstract: Video comprises multiple types of textual, audio and visual information, and each of them contains abundant semantic information. Therefore multimodal features query and fusion are necessary in video retrieval. In this paper, we propose a new video retrieval model, which adopts multi-model including text, image, semantic concept and camera motion to query video. Then relation algebra expression is advanced to fuse multimodal information instead of traditional linear fusion method. In semantic concept detection model, Bayesian network based ontology is proposed to extract concepts. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared with exiting key approaches of video retrieval by multimodal information fusion.

title: Topic Distributions over Links on Web
abstract: It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify the strength of the influence of a web page on another if there is a link between the two linked web pages. In this paper, we focus on the problem of link semantics analysis, and propose a novel supervised learning approach to build a model, based on a training link-labeled and link-weighted graph where a link-label represents the category of a link and a link-weight represents the influence of one web page on the other in a link. Based on the model built, we categorize links and quantify the influence of web pages on the others in a large graph in the same application domain. We discuss our proposed approach, namely Pairwise Restricted Boltzmann Machines (PRBMs), and conduct extensive experimental studies to demonstrate the effectiveness of our approach using large real datasets.

title: An Effective and Efficient Data Cleaning Technique in Large Databases

title: Index Support for Visualizing Large, Distributed, High-Dimension Data Sets

title: Flexible Feature Processing Mechanism of Radar Target Signal
abstract: Extracting effective and reliable features is fundamental and critical for constructing target recognition systems. Flexible Feature Processing Mechanism emphasizes the integration of feature extraction arithmetic and the self-adaptation of feature selection. On one hand, it would try to dig up the valuable information on as many as possible aspects, so as to construct the feature extraction method sets. On the other hand, it emphasizes to select the distinct features and eliminate the redundant features. The series of feature processing methods have been applied in multiple radar recognition projects successfully. According to the observation and analysis on a large number of real-life data, and taking the thinking process and the description methods of radar operators for reference, a set of feature extraction and selection methods basing on visual effects for dynamic radar target echo series have been introduced. The effectiveness, robustness and extendibility have been proved by large amount of tests and applications.

title: Externality Identification and Quantification of Transmission Construction Projects
abstract: This paper provides the evaluation method and model of the externality of the transmission projects, and identifies both negative and positive externalities, as well as makes quantitative and qualitative analysis of externality. In order to quantify the impact of transmission construction projects on the power flow distribution, power supply and demand, as well as the power price of the neighboring power grid, this paper operates the optimal power flow calculation on the Matlab platform through using the IEEE 30-Node Prototype System, and figures out the construction project's influence on the economic benefits of different power market participants. Based on the existing environmental evaluation study, this paper analyzes how the transmission construction project affects the natural resource and the environment and the daily life of the inhabitants. Taking into account the externality and quantifying the externality when evaluating the investment project can help the government and the investors to make the investment decision which is beneficial to the whole society.