预测帧编码中基于场景信息的宏块模式跳过算法*

在对场景信息进行分析的基础上,提出了H.264/AVC中预测帧的一种快速算法.对于未发生场景切换的预测帧,采用INTRA模式跳过算法(IMSA),跳过该帧中所有的INTRA模式的决策过程.对于发生场景切换的预测帧,跳过INTER模式的决策过程(PMSA).仿真结果显示,与采用完整的率失真优化(RDO)决策过程相比,未发生场景切换时,所提方法能减少30%～60%的编码时间;发生场景切换时,能减少60%～70%的编码时间,并且未导致明显的图像质量下降和码率的增加.     

线激光--机器视觉测量系统中的多传感器融合技术研究

针对逆向工程中直接从线激光扫描的数据云图中提取物体边界不仅繁琐而且精度也不好保证，为此提出建立线激光—机器视觉测量系统，先利用三日视觉获得物体初始边界，然后利用曲线延拓的方法并考虑影响延拓误差的因素，从而在满足一定精度要求的前提下实现不同传感器信息的融合。     

高温熔融-电感耦合等离子体原子发射光谱法测定含铁尘泥中5种有害元素

准确、快速地测定含铁尘泥中有害元素,对含铁尘泥节能降废项目研究及综合利用有着重要的指导意义。实验针对含铁尘泥试样因含碳量高,导致常规酸溶解方法如果不增加除碳步骤则很难完全分解试样的问题,采用高温熔融法将试样制成熔融液后倾倒于稀酸中溶解的方法,解决了碳含量高难溶解的问题。采用四硼酸锂-碳酸锂混合熔剂在高温下将试样熔融,熔融液高温倾倒于稀酸中制成澄清溶液,由于骤冷,试样炸裂成微小玻璃珠,无迸溅,操作简单,使得后续溶解过程快速完成,从而建立了电感耦合等离子体原子发射光谱(ICP-AES)测定P、Pb、K、Na、Zn共5种有害元素的方法。按照实验方法测定含铁尘泥中P、Pb、K、Na、Zn,测定值的相对标准偏差(RSD,n=9)在1.4%～5.7%之间,加标回收率在96%～110%之间。研究结果表明方法能够满足含铁尘泥样品中5种元素的定量分析要求。     

Microstructures and mechanical properties of welded Fe-12Cr-20Mn austenitic stainless steel

The effects of welding on the tensile and fatigue properties of fully annealed and cold-worked Fe-12Cr-20Mn austenitic stainless steel were evaluated. Room temperature and 500 °C tensile tests and room temperature cantilever beam fatigue tests were accomplished on specimens that contained autogenous bead-on-plate welds. The tensile and fatigue properties of the fully annealed material were not significantly influenced by welding. The tensile properties of the welded cold-worked material were also not significantly affected by the presence of a weldment. However, welding caused a large reduction of the fatigue life of the cold-worked material. Fatigue cracks preferentially initiated at large Mn-Si inclusions that formed in the fusion zones of the weldments.     

数据融合在电梯群控系统中的应用

将数据融合技术引入电梯群控系统中,建立了基于数据融合技术的模糊联结聚合神经网络模型,对影响电梯调度的各因素进行充分有效的数据处理后,得到的电梯控制参数作为电梯调度单元的输入.应用实例表明了该方法是方便且有效的.     

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 多传感器信息融合是一个新的研究领域，它应用到故障诊断领域还是近几年的事。文章主要论述了多传感器信息融合技术的基本概念、工作原理及常用方法，同时还介绍了其在故障诊断中的应用实例。     

对苯基苯酚技术现状及应用

介绍了对苯基苯酚的主要用途,国内外对苯基苯酚主要的工艺路线及技术现状,通过对主要生产工艺路线的比较,对今后我国的对苯基苯酚的生产发展提出了建议。     

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Automatic detection of a user's interest in spoken dialog plays an important role in many applications, such as tutoring systems and customer service systems. In this study, we propose a decision-level fusion approach using acoustic and lexical information to accurately sense a user's interest at the utterance level. Our system consists of three parts: acoustic/prosodic model, lexical model, and a model that combines their decisions for the final output. We use two different regression algorithms to complement each other for the acoustic model. For lexical information, in addition to the bag-of-words model, we propose new features including a level-of-interest value for each word, length information using the number of words, estimated speaking rate, silence in the utterance, and similarity with other utterances. We also investigate the effectiveness of using more automatic speech recognition (ASR) hypotheses (n-best lists) to extract lexical features. The outputs from the acoustic and lexical models are combined at the decision level. Our experiments show that combining acoustic evidence with lexical information improves level-of-interest detection performance, even when lexical features are extracted from ASR output with high word error rate.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.