Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.     

Human–machine shared control for lane departure assistance based on hybrid system theory

To improve the human–machine cooperation of lane departure assistance system (LDAS), a human–machine shared control strategy based on hybrid system theory was proposed. By considering vehicle’s discrete and continuous states and time-varying longitudinal speed, the hybrid system was formalized as hybrid automaton, and the shared control strategy was built to govern the human–machine interaction. Robust gain-scheduling energy-to-peak method was adopted to design the assistance system controller. The D-stability of the system was also studied and guaranteed by solving the linear matrix inequality (LMI). The proposed human–machine shared control method was evaluated via the co-simulation of CarSim/Simulink and the hardware-in-loop (HIL) experiment. The results showed that the proposed approach can effectively keep the vehicle in lane and a good human–machine coordination was demonstrated.     

Context-driven mean residual life estimation of mining machinery

Maintenance is crucial to ensure production/output and customer satisfaction in the mining sector. The cost of maintenance of mechanised and automated mining systems is very high, necessitating efforts to enhance the effectiveness of maintenance systems and organisation. For effective maintenance planning, it is important to have a good understanding of the reliability and availability characteristics of the systems. Determining the Mean Residual Life (MRL) of systems allows organisations to more effectively plan maintenance tasks. In this paper, we use a statistical approach to estimate MRL and consider a Weibull proportional hazard model (PHM) with time-independent covariates to model the hazard function so that the operating environment could be integrated into the reliability analysis. The paper explains our methods for calculating the conditional reliability function and computing the MRL as a function of the current conditions. The model is verified and validated using data from the hydraulic system of LHD equipment in a Swedish mine. The results are useful to estimate the remaining useful life of such systems; the method can be used for maintenance planning, helping to control unplanned stoppages of highly mechanised and automated systems.     

二层沟槽织构对机床导轨表面润滑特性的影响

通过构建二层沟槽织构模型，仿真研究沟槽织构表面的流体动力效应。结果表明：沟槽容积相同时，与单层沟槽相比，二层沟槽的平均油膜压力可以达到单层沟槽的2.31倍；保持二层沟槽的量纲一总深度[β]1、第一层沟槽量纲一宽度[α]1不变时，随着第二层量纲一深度[β]2的增大，平均油膜压力先增大后减小，[β]2为4.4时，平均油膜压力达到最大值；第二层沟槽的量纲一宽度[α]2从0.25增大到0.45时，沟槽内的旋涡区也随之增大，平均油膜压力逐渐减小。     

基于ANSYS及加速寿命试验台对油缸单耳环活塞杆杆头断裂进行设计改进

该文对某工程机械油缸单耳环活塞杆杆头断裂进行失效分析,提出了在毛坯锻造工艺满足设计要求的条件下,单耳环活塞杆杆头变截面处应力集中导致残余应力过大,是单耳环活塞杆杆头断裂的主要原因。从这方面出发,优化了单耳环活塞杆杆头变截面处的残余应力,通过ANSYS软件对优化后的结构进行应力分析,并用油缸加速寿命可靠性试验台做2F台架实验和整机多批次小批量试装来验证改进措施的有效性,最终提高了油缸单耳环活塞杆杆头在极限工况下抗疲劳的强度。     

基于机器视觉的玻璃缺陷检测

随着科学技术的进步,高端显示屏产品对平板玻璃的质量要求越来越高,玻璃的表面缺陷检测技术也因此备受关注。传统的人眼检测方法工作量大且准确率低,已经无法满足生产实际要求。研究了一种基于机器视觉的玻璃质量检测系统,采用先进的CCD成像技术和背光式照明获取图像,用MATLAB图像处理工具箱对采集到的图像进行灰度值化、滤波降噪和阈值分割处理,实现对缺陷区域的特征提取和识别。最后用BP神经网络对玻璃表面的三种缺陷进行分类,该神经网络识别的平均误差率为9.84%,表明此检测方法具有一定的应用价值。     

基于模糊故障树的数控龙门铣床加工误差分析与试验

针对数控龙门铣床加工中出现的平面度误差问题进行研究与改进,分析工艺系统因素产生的误差,建立加工平面误差的模糊故障树,通过下行法对其进行简化分析,定性分析影响加工平面度误差的最小割集,运用模糊隶属函数定量分析顶事件的发生概率及底事件的发生概率重要度,确定主要因素为工作台形位误差、定位元件误差、热变形等。使用激光测量技术对安装的工作台平面进行矩形布点测量,根据采集数据建立基于最小二乘法的数学模型,运用Matlab软件定性分析平面度的评定,并对超差区域进行改进,最终实现了机床装配与使用要求。     

基于模糊数和AHP的数控机床可靠性分配方法

针对层次分析法(AHP)处理可靠性分配问题时存在的局限性,将直觉梯形模糊数与层次分析法相结合,提出了一种新的数控机床可靠性分配方法。在确定影响机床可靠性分配的重要因素种类及分配原则后,利用层次分析法确定数控机床可靠性分配问题的层次模型和可靠性分配中影响因素的重要性权重。运用直觉梯形模糊数准确表达出模糊信息和专家意见,最终确定子系统在可靠性分配中的分配比例。以一数控机床为例,通过和层次分析法进行对比,证明了该方法的有效性。     

基于混合特征LLE融合与SVM的质量异常模式识别

针对生产过程中存在的异常模式识别的问题,提出基于LLE融合与支持向量机的质量异常模式识别方法。首先从动态数据流中提取其原始特征、统计特征、几何特征并将其进行混合,形成动态数据流的混合特征,然后利用LLE算法对混合特征进行降维,将降维后的特征集作为MSVM分类器的输入进行训练,同时采用粒子群算法对MSVM分类器进行参数寻优。最后用训练好的模型对动态数据流进行异常模式的识别。并将所提方法与单一类型特征方法、混合特征方法的识别模型进行比较,仿真结果和应用实例表明,所提方法的识别精度较高,可用于生产过程的质量异常模式识别中。     

高效方坯连铸生产工艺技术

连铸连轧是现在正在发展的技术之一,高温连铸坯控制是连铸连轧的基础。根据韶钢炼钢厂方坯连铸机高效工艺技术改进情况和国内外先进企业高效方坯连铸机的技术特点,分析了影响方坯连铸机拉速的各种因素,提出了提升方坯连铸拉速的措施。通过改进,炼钢厂方坯连铸最高拉速从2.8提升至3.5 m/min,满足连铸连轧的需求。对比国内外高速连铸机业绩,找出了提升方坯连铸机拉速的关键控制技术,为高效方坯连铸生产工艺技术奠定了基础。