问答中的问句意图识别和约束条件分析

意图识别和约束条件分析是口语理解(SLU)中的两个重要过程。前者是分类问题,判断话语意图;后者可以看作序列标注问题,给关键信息标特定标签。该文提出了一种LSTM联合模型,同时结合了CRF和注意力机制。在ID问题上,将所有词语输出层向量的加权和用于分类;在SF问题上,考虑标签之间的转移,计算标签序列在全局的可能性。在中文数据集和ATIS英文数据集上的实验验证了该文所提方法的有效性。     

发动机舱对流换热CFD模拟的基础问题

为合理选择进出口尺寸及布置发动机舱各部件,进一步降低舱内最高温度,对比标准k-ε模型、RNG k-ε模型和可实现的k-ε模型等3种湍流模型及2种壁面函数对温度场模拟的精度.由模拟与实验对比可知：可实现的k-ε湍流模型、增强型壁面函数更适合模拟热流场.利用CFD计算不同进出口尺寸及换热器与发动机间距对舱内温度场的影响,结果表明进出口尺寸对舱内最高温度影响较大.     

基于 LabVIEW2011 对心音信号 CWT 滤波的实现

目的：为除去杂波,同时克服sn叮对分辨率的限制,提高心音分析的准确性和实时性。方法：引入CWT滤波的理论,基于该理论构造算法,利用LabVIEW2011编程在应用上进行实现。结果：利用CWT滤波的方法成功对杂波滤除。结论：采用上述方法能够实现对于心音信号优化处理。     

基于Actran某车型后视镜风噪声计算

随着汽车发动机与轮胎路面噪声控制方法的研究与应用,汽车风噪声对车内声环境的影响越来越显著.风噪声对车内的作用分为声壁面脉动压力、湍流壁面脉动压力2种机理.选择一款车型,采用通用CFD软件与Actran相结合的方法,对其车内风噪声影响进行计算,并在后处理中分析2种机理导致的声源对车内驾驶员人耳处的噪声贡献率.     

基于改进人工势场法的智能无人车路径规划仿真研究

传统的人工势场法由于存在局部极小值问题,使智能无人车无法到达目标点。本文提出一种角度偏移的改进人工势场方法来进行避障的路径规划。介绍传统人工势场模型,详细介绍改进人工势场方法,并且对改进人工势场法进行仿真,实验证明方法的有效性。     

基于Modbus总线的智能烘房温度监控系统的设计

为能实时方便地监控烘房的温度变化情况,设计了一套基于Modbus总线和组态王的温度监控系统。该系统可将每个烘房的温度值通过Modbus总线实时传送到上位机进行显示、存储、打印等,上位机采用组态王设计监控画面。监控系统运算处理后给出控制信号,通过Modbus总线传送给输出量控制模块,由该模块控制烘房电加热箱和风机工作情况。该监控系统提高了烘房温度监控的准确性、便捷性和工艺流程的自动化水平。     

Facial hexahedral mesh transferring by volumetric mapping based on harmonic fields

Hexahedral mesh has obvious mechanical advantages over tetrahedral mesh, but it is no trivial task to generate hexahedral mesh for complex object shapes such as individual faces. This paper presents a novel method to generate patient-specific hexahedral meshes of facial soft tissue models, based on a volumetric cross-parameterization mapping from a standard hexahedral mesh to the individual model. The volumetric parameterization is constructed based on triple of the volumetric harmonic fields, which are adapted to be as close to mutually orthogonal as possible, to achieve some quasi-conformal effect. In addition, some piecewise constraints on the harmonic fields are added to ensure anatomical feature correspondence. Experimental results show that our approach works efficiently for facial soft tissue modeling, avoids element flipping and preserves mesh element angles to a significant extent.     

Colour and rotation invariant textural features based on Markov random fields

A visual appearance of natural materials significantly depends on acquisition circumstances, particularly illumination conditions and viewpoint position, whose variations cause difficulties in the analysis of real scenes. We address this issue with novel texture features, based on fast estimates of Markovian statistics, that are simultaneously rotation and illumination invariant. The proposed features are invariant to in-plane material rotation and illumination spectrum (colour invariance), they are robust to local intensity changes (cast shadows) and illumination direction. No knowledge of illumination conditions is required and recognition is possible from a single training image per material. The material recognition is tested on the currently most realistic visual representation - Bidirectional Texture Function (BTF), using CUReT and ALOT texture datasets with more than 250 natural materials. Our proposed features significantly outperform leading alternatives including Local Binary Patterns (LBP, LBP-HF) and texton MR8 methods.     

Frequency domain regularization of d-dimensional structure tensor-based directional fields

The present work is intended to address two of the major difficulties that can be found when tackling the estimation of the local orientation of the data in a scene, a task which is usually accomplished by means of the computation of the structure tensor-based directional field. On one hand, the orientation information only exists in the non-homogeneous regions of the dataset, while it is zero in the areas where the gradient (i.e. the first-order intensity variation) remains constant. Due to this lack of information, there are many cases in which the overall shape of the represented objects cannot be precisely inferred from the directional field. On the other hand, the orientation estimation is highly dependent on the particular choice of the averaging window used for its computation (since a collection of neighboring gradient vectors is needed to obtain a dominant orientation), typically resulting in vector fields which vary from very irregular (thus yielding a noisy estimation) to very uniform (but at the expense of a loss of angular resolution). The proposed solution to both drawbacks is the regularization of the directional field; this process extends smoothly the previously computed vectors to the whole dataset while preserving the angular information of relevant structures. With this purpose, the paper introduces a suitable mathematical framework and deals with the d-dimensional variational formulation which is derived from it. The proposed formulation is finally translated into the frequency domain in order to obtain an increase of insight on the regularization problem, which can be understood as a low-pass filtering of the directional field. The frequency domain point of view also allows for an efficient implementation of the resulting iterative algorithm. Simulation experiments involving datasets of different dimensionality prove the validity of the theoretical approach.     

铁路无线列调系统的功能运用分析

列车无线调度通信系统,简称无线列调,是铁路无线通信的主要组成部分,是组织铁路运输、保障行车安全、提高生产效率的重要通信设施,其通信质量的好坏直接关系到铁路的行车安全。本论文主要介绍无线通信的特点和我国无线列调通信系统的现状、构成、作用、功能、制式及工作频率,同时提出本文研究的意义和文章组成。