基于D-S证据的多语段融合语音情感识别

为获得更丰富的情感信息、有效识别长语音的情感状态,提出基于D-S证据理论的多粒度语段融合语音情感识别方法。采用2种分段方法对语音样本分段,用SVM对语段进行识别,再利用D-S证据理论对各语音段识别结果进行决策融合,得到2种分段方法下语音的情感识别结果,将这2个识别结果进一步融合得到最终结果。实验结果表明,该方法具有较好的整体识别性能,能有效提高语音情感的识别率。     

Multi-agent and driving behavior based rear-end collision alarm modeling and simulating

Distance-between-vehicle-measurement is the only factor in traditional car rear-end alarm system. To address the above problem, this paper proposes an alarming model based on multi-agent systems (MAS) and driving behavior. It consists of four different types of agents that can either work alone or collaborate through a communications protocol on the basis of the extended KQML. The rear-end alarming algorithm applies the Bayes decision theory to calculate the probability of collision and prevent its occurrence real-time. The learning algorithm of driving behavior based on ensemble artificial neural network (ANN) and the decision procedure based on Bayes’ theory are also described in this paper. Both autonomy and reliability are enhanced in the proposed system. The effectiveness and robustness of the model have been confirmed by the simulated experiments.     

基于WSRF的FPGA远程实验服务

介绍WSRF规范,结合FPGA实验特点和WSRF协议中状态管理,设计基于WSRF的远程实验架构。使用WSRF.NET设计与实现FPGA实验资源服务,通过实验验证了服务的正确性。实验结果表明,使用WSRF规范设计FPGA实验资源服务能解决实验的中间状态问题,实现实验时间、空间的开放。     

振动故障诊断的Petri网模型及系统实现

针对机械振动故障的传播特点,提出5种故障诊断的Petri网模型及其相应的故障诊断算法,用软件工程方法进行分析、设计和实现,得到机械振动故障诊断的Petri网系统。实践证明该方法能降低故障误诊率和漏诊率,解决复杂环境下机械振动故障诊断和故障预报的难题。     

CAN总线网络层协议栈开发测试

对ISO15765协议进行分析,依据协议在基于MC9S12DP512芯片的整车控制器上完成CAN网络层协议栈的开发,给出开发过程。在VC环境下开发测试程序,对协议栈进行测试。结果表明,实现的CAN网络层协议栈符合ISO15765协议的规定,可满足车辆故障诊断系统等具体应用中系统对CAN网络层通信的需求。     

整车控制器BootLoader的设计与实现

针对传统BDM工具下载或升级应用程序比较麻烦的问题,采用CCP在线更新应用程序,设计一种基于CAN总线的专用BootLoader。介绍BootLoader的设计及CCP在整车控制器端和PC端的实现。实车实验结果证明,BootLoader能够正确引导程序的运行,准确、方便地实现应用程序的下载和升级。     

基于最近邻过滤的数据流Skyline算法

在Skyline查询算法中,Lazy算法的数据处理能力较弱。针对该问题,利用最近邻过滤的思想,提出改进的NNSC算法。在预处理阶段,利用欧氏距离定义一个阈值,如果新插入数据对象的欧氏距离大于阈值,提前处理该对象,以提高算法利用率,减少资源的消耗。实验结果证明,NNSC可降低时空复杂度,对于正相关数据,性能提升效果明显。     

Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses

IntroductionAn important quality of association rules is novelty. However, evaluating rule novelty is AI-hard and has been a serious challenge for most data mining systems.ObjectiveIn this paper, we introduce functional novelty, a new non-pairwise approach to evaluating rule novelty. A functionally novel rule is interesting as it suggests previously unknown relations between user hypotheses.MethodsWe developed a novel domain-driven KDD framework for discovering functionally novel association rules. Association rules were mined from cardiovascular data sets. At post-processing, domain knowledge-compliant rules were discovered by applying semantic-based filtering based on UMLS ontology. Their knowledge compliance scores were computed against medical knowledge in Pubmed literature. A cardiologist explored possible relationships between several pairs of unknown hypotheses. The functional novelty of each rule was computed based on its likelihood to mediate these relationships.ResultsHighly interesting rules were successfully discovered. For instance, common rules such as diabetes mellitus?coronary arteriosclerosis was functionally novel as it mediated a rare association between von Willebrand factor and intracardiac thrombus.ConclusionThe proposed post-mining domain-driven rule evaluation technique and measures proved to be useful for estimating candidate functionally novel rules with the results validated by a cardiologist.     

基于TFIDF文本特征加权方法的改进研究

针对传统TFIDF方法将文档集作为整体来处理,并没有考虑到特征项在类间和类内的分布情况的不足,提出一种结合信息熵的TFIDF改进方法。该方法采用结合特征项在类间和类内信息分布熵来调整TFIDF特征项的权重计算,避免了那些对分类没有贡献的特征项被赋予较大权值的缺陷,能更有效计算文本特征项的权重。实验结果表明该方法提高了文本分类的精确度和召回率,是一种比较有效的文本特征加权方法。     

基于条件笔画密度提取的文本定位方法

为了解决视频检索中文本定位精确度不高的问题,提出一种基于条件笔画密度提取的文本定位方法。根据文本笔画的特征对视频图像中的文本进行初步定位,用基于条件密度的方法滤去一部分非文本区域,再利用动态形态学对提取的文本区域精确定位并对其优化得到最终的文本区域。实验结果表明,该方法能滤去大部分非文本信息,得到相对精确的文本区域,并且定位效果较好。