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当前工业信息化飞速发展,很多生产设备更新换代效率低,同时某些关键位置的数据采集不适合采用数字化仪表,许多含有仪表的老式设备无法直接将表盘示数传入计算机中。因此,本文提出了基于卷积神经网络的工业仪表读数识别方法,利用核相关滤波算法确定表盘位置,利用卷积神经网络识别仪表读数。本方法的识别准确率可达96%,具有较高的收敛速度。 相似文献
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赵彬 《工业仪表与自动化装置》1984,(5)
为扩大仪表示值的分辨率,提高仪表测量精度,适应被测参数变化较大、要求测量精度较高的场合,我们用一台XCT—121型宽带调节动圈仪表进行了改制,使仪表自动进行量程切换,被测参数能够自动设定在适当的测量范围。仪表的量程自动切换是根据仪表指针在刻 相似文献
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1概述 差压表广泛适用于石油、化工、冶金、机械、电站、科研等行业工业流程中,直接测量系统两个测控点气体或液体压力的差值。2工作原理 当两个不同压力分别经仪表两个输入接口进入高、低压客室后,被测介质作用于测量元件两侧,其差压值使膜片产生直线位移,带动导杆部件变换成角位移,再通过导套从密封压力腔内输出,最后经仪表联杆、机芯两组放大(其它压力表为一组放大),在仪表表盘上显示出被测介质两个压力的差值。3两只1级压力表不能替代一只差压表 若用两只压力表分别测量系统中两个不同测控点的压力差,可能得出错误的结论… 相似文献
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1序言显示仪表在工业生产或试验现场中使用时的条件常常是很复杂的。被测量的参数往往被转换成微弱的低电平电压信号,并经过长距离传输到显示仪表,因此除有用的信号外,由于各种原因,经常会有一些与被测信号无关的电流或电压存在,这种无夫的电流或电压称为“干扰”。热噪声、温度效应、化学效应、振动等都可能给测量带来影响,它将歪曲测量的结果,严重时甚至会使仪表完全不能工作。2于扰的形式根据仪表输入端于扰的作用方式,可分为串模于扰和共模干扰。如图1所示,串模干扰(又称常态干扰、横向干扰、差模干扰)是叠加在被测信号上… 相似文献
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F1602数字显示仪表是分析仪器通用面板式数字电压表,它以数字的形式显示被测物质成份的含量或其它物理量。将分析仪器输出的直流电压成比例地转换成数字量。借改变模-数转换的比例系数来以被测物质成份含量的单位或其它物理量的单位来进行显示。该显示仪表 相似文献
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在热导式气体分析器中,从被分析气体含量变化到仪器指示出这一变化值,中间经过一系列的变换过程。这一过程可表示为:气体混合物中被测气体含量发生变化→混合气体导热率变化→敏感元件温度变化→敏感元件电阻值变化→测量对角线电压变化→显示仪表显示出被测气体的含量。仪器本身及外界条件的变化都会影响到这个变化过程,因而产生了分析仪器的误差。 相似文献
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基于激光三角法的零件表面粗糙度在线测量 总被引:1,自引:0,他引:1
介绍了一种零件表面粗糙度的激光在线测量方法,该测量方法具有测量速度快且能够显示被测表面的具体形貌等优点.在测量中引入激光三角测量系统,用无衍射激光光束作光源,用高精度的摄像机作位移传感器,通过计算机数据处理得到表面粗糙度值,使表面粗糙度在线检测成为可能。 相似文献
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钟经华 《世界仪表与自动化》2006,10(4):62-63
仪表测量技术是测量学的一个重要,分支。仪表测量技术随着科技的发展,已迈入一个全新领域。测量仪表和计算机之间的界限正逐步消失,没有测量就没有鉴别,科技就不能发展和前进。测量必须依据标准和规范按照正确测试方法进行.并以相关规定标准极限参数作为依据。测量学与测量仪表的发展大致经历4个阶段:模拟式、数宇化、智能型和虚拟型测量仪表。随着科学技术的发展.诞生了自动测试系统,它是将计算机、通信和检测技术有机地结合的新兴技术。它经历了3个时期: 相似文献
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压力传感器的输入输出特性大部存在非线性,且易受工作环境温度的影响。利用LabVIEW图形化编程语言,辅以多参量数据采集卡,采用了基于BP神经网络压力传感器非线性校正的模型、算法和实现方法,探讨了虚拟仪器系统中对压力传感器特性进行温度非线性校正。通过计算机仿真与应用,显示了在虚拟仪器系统中使用这种方法不但使压力传感器的性能得到了改善,而且计算时间短,准确度高,有实际应用价值。 相似文献
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Aiming at the problem of low quality in image reconstruction of traditional image reconstruction algorithm of electromagnetic tomography(EMT), an EMT image reconstruction algorithm based on autoencoder neural network of Restricted Boltzmann Machine (RBM) is proposed. Firstly, the basic principles of EMT system and autoencoder neural network are analyzed. Autoencoder neural network is a deep learning model, which contains two parts: encoder and decoder. The encoding process of the encoder is equivalent to the object field detection process in the EMT system; the decoding process of the decoder is equivalent to the image reconstruction process. On this basis, an autoencoder neural network model is built. In this model, the RBM is used for layer by layer pre-training to obtain the initial weight and offset, and the global weight and offset are adjusted by BP algorithm. The parameter file generated in the trained autoencoder neural network is used to construct a decoder. Finally, the detected voltage value output by the EMT system is input into the decoder network to obtain the reconstructed image of the EMT. Furthermore, data with Gaussian noise and data regarding flow pattern not in training dataset are used to test the generalization ability and practicability of the network, respectively. The experimental results show that the method in this paper is a kind of EMT image reconstruction method with higher accuracy, which also provides a new means for EMT image reconstruction. 相似文献
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网络化手持式测试仪器的研究 总被引:4,自引:1,他引:3
随着计算机、通讯、微电子技术的不断提高和网络的蓬勃发展,测试技术网络化成为大势所趋。本文提出的网络化手持式测试仪器是一种典型的嵌入式系统,它体积小、重量轻、便于携带,而且可以接入Internet,集测试功能和通讯功能于一体,所以极具推广价值。本文就其软、硬件结构进行了详细探讨。最后推荐了一种客户机─服务器结构的分布式网络测试系统,作为网络化手持式测试仪器的分析中心。 相似文献
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M. S. Packianather P. R. Drake 《The International Journal of Advanced Manufacturing Technology》2000,16(6):424-433
A decision tree using smaller more specialised modular neural networks for the classification of wood veneer by an automatic
visual inspection system was presented in Part 1 [1]. A key process in the design of a modular neural network is the use of
"normalised inter-class variation" in the selection of the most appropriate image features to be used for its particular specialised
classification task. At the root of the decision tree is a single large (holistic) neural network that initally attempts to
classify all of the image classes which include clear wood and 12 possible defects (13 classes). The initial design uses 17
features of the acquired image of the wood veneer as inputs. The selection (or more correctly pruning) of inputs for this
large neural network used not only "normalised inter-class variation", but also "normalised intra-class variation" in the
features and their "correlation" within the same class. This results in the elimination of 6 inputs. The revised smaller 11
input neural network results in a substantial reduction in classification time, for the computer implementation used here,
and at the same time the classification accuracy is improved. This is the root of the decision tree described in the previous
paper. 相似文献
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Image registration is a process of aligning two or more images taken at different times or using different sensors by transforming the same area into one coordinate system. Imaging conditions, image and area deteriorations from repeated sectioning, are serious impediments to successful image registration. The application of artificial neural networks for feature recognition is introduced to the field of metallurgy to assist in an automated approach to image registration of metallurgical microstructures. Low susceptibility to feature deterioration, often occurring during serial sectioning, is demonstrated and assessed. The process of image registration using an artificial neural network to aid in feature segmentation is performed using computer generated shapes and a metallurgical microstructure. 相似文献
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