To address the issues of high cost,limited scalability,and system complexity in tunable laser-based optical frequency domain reflectometry(OFDR)systems,a non-tunable laser-based OFDR temperature demodulation system is proposed.By modulating a standard non-tunable laser with piezoelectric ceramic(PZT)to generate linear frequency-swept light and applying cross-correlation algorithms,distributed temperature measurement is achieved.In standard single-mode fiber experiments,results show a linear relationship between data shift and temperature,with a 0.929 fitting coefficient and demodulation errors below 0.5℃.For fiber Bragg grating(FBG)tests,the system achieves a 0.912 fitting coefficient and errors under 0.8℃.The results confirm that the system ensures high precision and sta-bility while significantly reducing costs,providing a cost-effective solution for distributed fiber-optic sensing in industrial monitoring and related fields.
This paper presents an in-pixel histogramming time-to-digital converter (hTDC) based on octonary search and 4-tap phase detection, aiming to improve frame rate and reduce distance error. The proposed hTDC is a 12-bit two-step converter consisting of a 6-bit coarse quantization and a 6-bit fine quantization, which achieves a time resolution of 120 ps without a GHz reference frequency and supports multiphoton counting up to 2 GHz. The proposed hTDC is designed in 0.11 μm CMOS process with an area consumption of 6900 μm . Timestamp sequences from a behavioral-level model are imported to the hTDC circuit for simulation verification. The post-simulation results show that the proposed hTDC achieves about 0.8% depth precision in 9-m range for short-range system design specifications and about 0.2% depth precision in 48-m range for long-range system design specifications. Under 30 klux background light conditions, the proposed hTDC can be used for SPAD-based flash LiDAR sensor to achieve a frame rate to 40 fps with 200-ps resolution in 9-m range.
Distributed measurement of underground rock and soil deformation is an important part of geological hazard monitoring. Using parallel helical transmission lines as sensors and combining frequency domain reflection (FDR) analysis enables continuous distributed measurement of larger deformation quantities. It is known that the characteristic impedance of the parallel helical transmission line increases with the increase of tensile force. Through FDR, the measurement and location of the tensile point can be determined. This paper explains the principle of FDR measurement of tensile deformation in parallel helical transmission lines. In response to practical application issues of FDR in parallel helical transmission lines, this paper analyzes the influence of sweep range, frequency, and number of sampling points on measurement results through experimental data. The paper also measures different incident signals under different circumstances, identifies and analyzes factors that may affect measurement results, and selects a better measurement scheme. The proposed method is used to simulate the actual use of parallel helical transmission lines and obtain actual measurement data, which is analyzed for better understanding.
The sensitivity mechanism of a thermal oscillator-type biaxial MEMS angular velocity gyro was revealed. The temperature field inside the sensitive structure was calculated based on the biaxial sensitivity principle, the vibration mode of the thermal oscillator and the gyroscopic effect. The results show that: (1) A stable temperature field is formed inside the sensitive structure after 1.8s of power-on. (2) When there is angular velocity loading, the thermal oscillator moves with the input angular velocity, causing the temperature field to shift, and the temperature difference $\Delta T_{\mathrm{Y}}(\Delta T\mathrm{x})$ between the two hotlines set symmetrically in the two orthogonal $\mathrm{Y}(\mathrm{X})$ directions shows a linear growth with the increase of the input angular velocity $a_{x}(a_{y})$ , and the average temperature sensitivity of X and Y axes is 121 mK/°/s. (4) According to the input-output $\omega_{x}-VY\text{out}$ and $\omega_{y}-VX\text{out}$ characteristic curves, the mathematical model is obtained, and the sensitive mechanism is revealed. The average sensitivity of X and Y axes is 0.091mV/°/s, the average nonlinearity is 1.86%, and the average cross-coupling is 2.3%. This paper provides a practical theoretical foundation for the optimized structure.
Tailings reservoir seepage refers to the flow of fluid in the pore medium of tailings sand soil. When the rainfall is too large or there is a large water source near the tailings pond, it is easy to form seepage, so that the tailings pond has the risk of dam break. In order to detect the real flow velocity of seepage in tailings reservoir and not change the flow state during seepage detection, a seepage detector based on the principle of thermal temperature difference is designed in this paper. The temperature difference signals collected by the upstream and downstream temperature sensors are converted into electrical signals by the acquisition circuit and sent to MCU for data processing. Through corresponding numerical conversion, the current seepage velocity can be observed on Ali Cloud and the upper computer. The detector device is small in size, easy to install, no need to take over more external equipment, can be buried in the tailings pond for long-term real-time monitoring, and will not cause pollution to the surrounding environment.
Eddy current testing is one of the common nondestructive testing of metal structure strain. It plays an irreplaceable role in metal testing and it can show the strain of metal structure directly. Comparing the existing eddy current detection technology, the traditional eddy current detection can only be carried out by artificially controlling the eddy current detection probe when measuring structural strain and stress deformation, large area detection is complicated, so a blanket eddy current detection probe is developed. The blanket eddy current detection can be used for real-time online monitoring and display the deformation condition and position of metal plate directly and accurately. Aiming at the problem of strain measurement of metal plate, different types of blanket eddy current testing are designed and carried out to test the structural strain condition, compare the structural deformation generating the phenomenon of induced voltage change, analyze the influence of coil with different turns, position and magnetic field direction on the structural strain. This paper provides a reference for the selection of non-metallic material structure monitoring methods and has great reference value for further research and development of new strain sensors and measuring instruments.
The relay node is the transit center of the sensor Internet of Things communication, and the rationality of its selection is of great significance to ensure the stability of the Internet of Things communication and information throughput. This paper proposes a method of selecting relay nodes based on optical fiber to maximize the throughput of the sensing Internet of Things. By building a sensor Internet of Things model, find the main lobe in the target fiber sensor base station, and identify the signal transmitted by the light source node. Select the relay nodes that can transmit the known optical channel status to other nodes in the candidate nodes, remove the nodes that cannot maximize the throughput, obtain the average received signal to noise ratio of the sensor Internet of Things based on statistical independent distribution, and select the appropriate fiber sensor antenna to complete the relay node selection. Simulation results show that the outage probability of the proposed method approaches zero when the number of nodes is 22;When the power of the light source node is 30 dBm, the network throughput reaches 98%.
使用深度学习的语音增强技术能够提升听者的言语识别率,但因神经网络的规模较大难以应用于边缘设备中.因此,提出了一种可用于助听器等边缘设备的循环神经网络语音增强加速器.该加速器将神经网络的计算用独立矩阵乘法硬件实现,并在多层神经网络的层之间实现硬件级的流水操作,通过并行和流水降低了计算延时.实验表明,与带噪语音相比,在volvo、factory2、babble噪声环境下,所提算法的信噪比分别平均提升了 17.302 dB、8.412 dB和 4.732 dB;短时语音可懂度分别平均提高了 1.4％、0.8％和 0.4％;语音质量感知评估平均提高了 1.498、0.504 和 0.234;这三项指标均高于所对比的传统语音增强算法与神经网络算法.当时钟频率为 10 MHz时,加速器的处理延时为 9.2 ms,可以满足边缘侧应用的实时性需求.
基于微机电系统(MEMS)的加速度计和陀螺仪广泛用于姿态测量.加速度计和陀螺仪间的数据融合通常采用互补滤波(CF)实现.CF具有计算复杂度低的优势,但CF一般仅通过试凑进行参数整定.为使CF的参数选取与传感器噪声特性相吻合,提出一种用于姿态估计的离散时间互补滤波器(DTCF)并推导了其参数整定方法.仿真表明所提出的DTCF及其参数整定方法是可行的.用于无人机姿态估计时,所提出的DTCF能达到与目前常用的Mahony型CF相同的姿态精度,但DTCF在单片机上所需的运行时间仅为Mahony型CF的50％左右,可提高姿态估计的实时性.
目标尺度变化和低分辨率的复杂场景往往会影响目标跟踪算法的性能进而导致跟踪精度下降.针对此问题,提出了一种基于深度像素级特征的孪生网络目标跟踪方法.引入像素级特征融合方法对目标模板和搜索区域的多层特征进行融合、设计基于残差网络和拓扑结构的特征深层提取模块、依据判据筛选历史信息得到合适模板特征进行模板更新.实验结果表明,所提改进算法在VOT2018数据集上比基础算法的EAO值提升了5.31％,准确率提升了0.83％,鲁棒性提升了3.85％;在OTB100数据集上,所提算法精确率为91.4％,成功率为71.7％,与基础算法相比,精确率提升了3.28％,成功率提升了5.13％.
针对目前光纤光栅解调系统算法复杂,需要借助上位机进行解调的不足,设计了一种基于卡尔曼滤波算法的嵌入式光纤光栅解调系统.该系统在STM32嵌入式平台上采用卡尔曼滤波算法,结合趋势判断算法实现了FBG中心波长的快速解调.实验结果表明,系统的测温精度可达±0.1℃,对FBG温度传感器中心波长的解调稳定性可达±3 pm,温度与波长线性变化的拟合度可达0.998以上,能够满足实际工程应用的需求.
槽罐车安全阀作为罐体的主要泄压部件对罐车的安全运输起着十分重要的作用.为提高槽罐车安全阀校验的自动化程度,提出了一种槽罐安全阀在线校验的实现方法,完成了在线校验装置的设计与校验试验,给出了详尽的在线校验装置机械结构设计与原理说明,对装置中采用的步进电机、拉力与位移传感器的选型和测量设计进行了详细阐述.装置的下位机程序实现了对步进电机的控制、数据采集与无线传输;上位机程序实现了将数据的接收,并在显示界面绘制拉力和位移曲线,测量数据的显示与储存等.针对设计的安全阀在线校验装置对选取的安全阀进行了离线和在线校验试验,得到了校验装置校验安全阀的整定压力、拉力和位移变化曲线,离线校验的实验误差均在2％左右,在线校验的实验误差随着初始压强增大而增大,对在线校验产生的实验误差进行了深入分析.
滚动轴承在故障诊断过程中,存在着单一特征诊断准确率较低且无法充分表征故障信号所包含信息的问题.提出一种基于局部线性嵌入算法(Locally Linear Embedding,LLE)结合熵权法(the Entropy Weight Method,EWM)的多特征融合方法,结合引力搜索算法(Gravitational Search Algorithm,GSA)改进支持向量机(Support Vector Machine,SVM)实现滚动轴承的故障诊断.首先采用LLE-EWM对提取到的48维故障特征进行筛选融合,然后结合GSA-SVM模型对提取到的融合特征进行诊断,从而实现对滚动轴承变负载条件下的故障诊断.通过凯斯西储大学滚动轴承实测振动信号,对所提故障融合诊断方法的有效性进行验证.在特征筛选阈值设定为60％时,滚动轴承故障诊断的准确率达到99.7％.对比不同模型,所提方法具有最高的诊断准确率.试验结果表明,所提方法能够实现对故障信号特征信息的深度提取及提高故障诊断精度.
脑电信号和眼电信号存在频谱混叠,目前的单通道脑电信号中眼电伪迹去除方法容易造成脑电信号失真.提出一种基于经验小波变换(EWT)和改进的自适应噪声完备经验模态分解(ICEEMDAN)的单通道脑电信号眼电伪迹去除算法.首先使用EWT将单通道脑电信号分解为δ频段和高频段信号,再用ICEEMDAN将δ频段信号自适应分解为多维本征模态函数(IMFs),设置样本熵阈值自动去除眼电伪迹信号,最后重构得到滤波后的脑电信号.基于半模拟脑电数据和真实脑电数据开展实验,结果表明所提算法相比于已有算法能够在去除眼电伪迹的同时更好地保留原始脑电信息.
随着穿戴技术的发展,可穿戴监测设备逐渐成为人类健康的守护者.然而,传统的监测设备使用的银/氯化银电极,采用凝胶导电成分,贴肤舒适性差,并不符合穿戴设备长时间监测人体体征或健康的应用需求.设计和开发多种刺绣结构的织物电极,并测试了基于不锈钢导电纱线用电脑刺绣柔性电极的性能.实验结果表明:作为电脑刺绣最常用的针型,"他他米"针型以其均匀的纱线接触点和空隙,大大降低了织物电极的电阻(0.65 MΩ～4.9 MΩ)和电极与人体皮肤之间的阻抗(2.455 MΩ),穿戴舒适性好,可满足长时间心电监测的需求.
传统分水岭算法受到噪声干扰时容易出现过分割现象,为了抑制噪声的同时尽可能多地保留住图像边缘信息,提出了一种基于各向异性扩散的分水岭分割算法.首先对原始图像进行滤波操作,传统P-M算子通常人为设定固定边界阈值,容易丢失细节信息,应用梯度模的变化设定阈值并连结图像结构张量形成一个扩散函数,边缘处沿切线方向扩散易于保留边缘细节,平坦处具有各向同性易于平滑噪声,这样保证了良好的分水岭结构.其次对图像的梯度信息进行计算,为了使梯度信息得到补偿,采用数学形态学的开闭运算对图像梯度信息进行处理.然后运用形态学极小值标定方法标记处理后的图像局部极小值,最后用分水岭算法对图像进行分割.实验对无噪声图像和加噪声图像进行分割,结果表明该方法具有良好的分割效果,尤其对噪声图像有较好的鲁棒性.
外调制OFDR系统可通过减小数据处理过程中的滑动窗宽来提高空间分辨率.然而,窗宽过小会使传感定位结果出现"假峰",进而降低定位准确度,即传感空间分辨率和定位准确度之间存在相互制约关系.提出频域插值和窗宽优化方法,在数据处理流程中利用频域插值的方法提高系统的定位准确度,通过综合评估系统的定位误差,权衡窗宽和插值位数规模,实现在兼顾定位准确度的前提下进一步提升系统空间分辨率.阐述了OFDR系统传感原理,研究了影响系统空间分辨率各关键参数,搭建了外调制OFDR温度定位传感系统,在1000 m传感光纤上进行了实验验证.实验结果表明,在同等测量条件下,采用频域插值和窗宽优化方法将系统空间分辨率由3 m提升至了1.5 m,能有效提升OFDR系统的定位空间分辨率.
为克服移动荷载识别过程受应变测试分辨率影响大的难题,研发了一种高分辨率应变测试装置.该装置将不同刚度比和长度比的两种部件进行组合,将有效距离内的变形集中至某一部件上,从而实现对结构应变的放大作用.推导了基于部件参数刚度比、长度比的高分辨率应变求解公式,提高了对结构应变的测试分辨率,可识别的应变达到0.01με.分析了温度及体系变形所引起的测试误差,推导了此类误差的计算公式.所提出的方法可广泛适用于桥梁荷载试验、健康监控及基于动应变识别移动荷载等技术领域.
多极磁感应角位移测量系统被广泛应用于精密测试及制造领域,传统的多极磁感应角位移测量系统为单通道模式,不具备绝对零位,为实现绝对零位功能可将其设计为双通道模式,但这种双通道设计模式会增加系统的体积、成本及复杂性.提出了一种集成光电传感器的新型绝对零位测角方案,这种方案创新性地将光电传感器与多极磁感应角位移测角系统在结构上进行了集成,通过将光电传感器安置在多极磁感应角位移测角系统的某一个对极空间内,巧妙地利用光电传感器触发特性将这个对极的零位作为系统的绝对零位,从而避开了光电传感器重复性不高的缺陷,得以实现高重复精度的绝对零位.重点论述了零位功能及信号调理部分的技术路线,并构建了实验测试平台对零位功能及重复性进行了验证,实验测试绝对零位的重复性误差不超过0.1″,随后对系统测角精度进行标定可达到±2.7″,进而验证了本文所涉及绝对零位及高精度测角方案的可行性.
针对传统方法标定复杂环境下含野值的MEMS三轴磁力计会出现精度较差的问题,提出了一种基于鲁棒列文伯格-马夸尔特(Robust Levenberg-Marquardt,RLM)的三轴磁力计标定方法.首先,对三轴磁力计进行误差模型分析,建立了基于模值估计的误差参数方程;然后利用误差参数方程,设计鲁棒列文伯格-马夸尔特的标定方法,实现对误差参数的估计;最后,通过仿真与实验测试对所提方法进行验证.结果表明,所提方法标定含野值的磁力计数据相比于传统方法模值标准差减小了近90％,在标定正常环境下的结果与传统方法则很接近,有效提高了多环境下标定结果的稳定性与准确性.
