450A接触电流测试仪测试网络和数据采集的设计

本文介绍了450A接触电流测试仪关键技术内容。     

EMD与神经网络在气液两相流流型识别中的应用

本文提出了EMD与Elman神经网络相结合的气液两相流流型识别的新方法.将压差波动信号经验模态分解(EMD)后的固有模态函数(IMF)进行分析、提取IMF能量作为Elman神经网络的输入特征向量,对水平管内的气液两相流流型进行识别.实验结果表明:该方法优于BP网络且稳定、识别率高,具有可行性.     

科学仪器工作状态监测装置的设计与实现

建立了支持网络通讯的科学仪器工作状态监测装置。该检测装置以单片机系统为核心,运用信号检测技术获取仪器的工作状态信息,实现有效机时的记录。基于自制编码表对数据进行BASE64编码,保证了记录信息的安全;支持嵌入式互联网技术将被监测仪器的实时状态上传到远程服务器,实现大型科学仪器的网络化监测。     

机器学习在信道建模中的应用综述

信道建模是设计无线通信系统的基础,传统的信道建模方法无法自动学习特定类型信道的规律,特别是在针对特殊应用场景,如物联网、毫米波通信、车联网等,存在一定的局限性.此外,机器学习具有有效处理大数据、创建模型的能力,基于此,探讨了机器学习如何与信道建模进行有机融合,分别从信道多径分簇、参数估计、模型的构造及信道的场景识别展开...     

地市级地面数字电视发射机常见故障分析

随着中国加快推进地面数字电视（DTMB）无线覆盖工程的建设,提高技术人员对地面数字电视发射系统设备的维护维修水平、故障判断排除能力刻不容缓。本文结合由包头市微波总站建设的市级地面数字电视单频网项目,对其运行中常见的发射机故障进行了分析和总结。     

Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection. The aim of this paper is to propose a simple and constrained convolutional neural network for speaker recognition tasks and to examine its robustness for recognition in emotional speech conditions. We examine three quantization methods for developing a constrained network: floating-point eight format, ternary scalar quantization, and binary scalar quantization. The results are demonstrated on the recently recorded SEAC dataset.     

CT and MRI Medical Image Fusion Using Noise-Removal and Contrast Enhancement Scheme with Convolutional Neural Network

Medical image fusion (MIF) has received painstaking attention due to its diverse medical applications in response to accurately diagnosing clinical images. Numerous MIF methods have been proposed to date, but the fused image suffers from poor contrast, non-uniform illumination, noise presence, and improper fusion strategies, resulting in an inadequate sparse representation of significant features. This paper proposes the morphological preprocessing method to address the non-uniform illumination and noise by the bottom-hat–top-hat strategy. Then, grey-principal component analysis (grey-PCA) is used to transform RGB images into gray images that can preserve detailed features. After that, the local shift-invariant shearlet transform (LSIST) method decomposes the images into the low-pass (LP) and high-pass (HP) sub-bands, efficiently restoring all significant characteristics in various scales and directions. The HP sub-bands are fed to two branches of the Siamese convolutional neural network (CNN) by process of feature detection, initial segmentation, and consistency verification to effectively capture smooth edges, and textures. While the LP sub-bands are fused by employing local energy fusion using the averaging and selection mode to restore the energy information. The proposed method is validated by subjective and objective quality assessments. The subjective evaluation is conducted by a user case study in which twelve field specialists verified the superiority of the proposed method based on precise details, image contrast, noise in the fused image, and no loss of information. The supremacy of the proposed method is further justified by obtaining 0.6836 to 0.8794, 0.5234 to 0.6710, and 3.8501 to 8.7937 gain for QFAB, CRR, and AG and noise reduction from 0.3397 to 0.1209 over other methods for objective parameters.     

涡激响应尾流双振子模型的参数反演方法

尾流双振子模型是研究圆柱结构涡激振动响应的重要模型,模型参数的准确确定对悬浮隧道设计理论具有重要意义.首先通过降阶法将多变量二阶非线性常微分方程组的尾流双振子模型变换为一阶方程组.然后给出一种新型的圆柱结构水槽试验设计方案,其中试验模型的刚度能够较好反映悬浮隧道等实际工程结构的刚度,基于相机动态捕捉和视频识别计算机程序...     

结构光三维视觉检测系统的标定方法研究

提出了一种新的结构光三维视觉检测系统的标定方法,将BP神经网络和线性标定方法结合起来,用线性标定方法标定结构光系统的线性部分,用BP神经网络来描述该结构光系统的其他部分.结果表明:该方法结合了神经网络和线性标定方法的优点,不仅给出了结构光三维检测系统中CCD相机的内部和外部参数,而且利用神经网络的非线性逼近能力,补偿由于镜头径向畸变、切向畸变等因素引起的系统非线性误差,并且精度高、抗噪声能力强及鲁棒性好.     

OCDMA环网的码字消除功能及其性能分析

分析了一种光码分多址(OCDMA)环网中码字消除结构,使用马尔科夫链模型和状态转移概率矩阵得出了环中各用户的误码率(BER)的表达式,计算表明,该码字消除结构虽然能够有效地把码字从环网中消除掉,避免码字在环网中循环产生干扰累加,但是会明显恶化经过码字消除结构的其它用户的BER性能;讨论了环网中用户的BER性能与码长、判决阈值的关系;针对码字消除对OCDMA环网的重要性,提出了一种基于BER监测及反馈的方法来降低码字消除结构造成的BER恶化,仿真结果表明,该方法能有效改善用户的BER性能.