Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Quantitative assessment of sensory functions after 3 surgical approaches for trigeminal neuralgia by current perception threshold measurement

Objective： To quantitatively identify and grade trigeminal sensory functions after 3 major surgical procedures of trigeminal neuralgia using a newly developed quantitative sensory testing technique, current perception threshold measurement （CPTM）. Methods： In the current study, there were 48 trigeminal neuralgia patients without history of prior surgical treatment. These patients received one of the following 3 surgical procedures, microvascular decompression （MVD）, peripheral nerve block with alcohol （PNB）, or percutaneous radiofrequency thermocoagulation （PRFT）. The quantitative sensory testing measurement, CPTM, and conventional qualitative sensory testing measurements were performed preoperatively and postoperatively to evaluate and grade the trigeminal sensory functions All 3 major cutaneous sensory fiber types, large myelinated fibers （A beta）, small myelinated fibers （A delta） and unmyelinated fibers（C） were allowed to quantitatively evaluate and grade by CPTM. The results of the measurements were statistically analyzed using a one-way analysis of variance （single factor）. Each subject was his/her own control for comparison of the preoperative to postoperative state on the asymptomatic and symptomatic sides. Subjects were tested 48 h preoperatively and 4 weeks postoperatively. Results： PNB with alcohol and PRFT caused significant sensory dysfunction postoperatively in every fiber type, indicating damage to all fibers. On the contrary, the sensory function in all 3 fiber types was unchanged after MVD management. Conclusion： Among the 3 major surgical procedures tested, only MVD preserves sensory function in trigeminal system. CPTM is of quantitative nature on the evaluation of sensory functions of nerve fibers

Subsynchronous oscillation monitoring and alarm method based on phasor measurements

Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.

A 16-Channel high-resolution time and charge measurement module for the external target experiment in the CSR of HIRFL

High precision time measurement is required in the readout of the neutron wall and TOF walls in the external target experiment of the Cooling Storage Ring(CSR) project in the Heavy Ion Research Facility in Lanzhou(HIRFL).Considering the time walk correction,both time and charge are measured in the readout electronics.In this 16-channel measurement module,time and charge information are digitized by TDCs at the same time based on the Time-Over-Threshold(TOT) method;meanwhile,by employing high-density ASIC chips,the electronics complexity is effectively reduced.Test results indicate that this module achieves a time resolution better than 25 ps and a charge resolution better than 5%over the input amplitude range from 50 mV to 3V.

Research on EMI Noise Measurement Technology Based on Wavelet Analysis

Aiming at the problem that it is difficult to measure the electromagnetic radiation produced by the equipment at present,this paper presents a method for measuring the noise of electromagnetic interference(EMI)based on wavelet analysis.The technique uses time frequency localization features of the wavelet transform,based on threshold function filtering method to filter the test signal,which makes it possible in open space or noisy environment for measurement of electromagnetic interference of equipment.Simulation and experimental results show that the technique is able to eliminate or attenuate the noise in the frequency band of 30Hz^1000MHz.

3D Depth Measurement for Holoscopic 3D Imaging System

Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual feature detector for integration of 3D information to locate objects in the scene. The AFE descriptor plays a key role in simplifying the detection of both edge-based and region-based objects. The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm. This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients) in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map (3DM) according to a correlation analysis of region contours. This automatically combines the exploitation of the available depth estimation technique with edge-based feature shape recognition technique. The application area consists of two varied domains, which prove the efficiency and robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects in the scene. Experimental results show that the proposed 3DM technique is performed efficiently compared to the state-of-the-art algorithms.

数字经济助推高等教育高质量发展:理论机制与实证检验

基于2011—2021年中国31个省份的面板数据,采用双向固定效应模型、面板门槛模型和空间杜宾模型检验数字经济对高等教育高质量发展的影响。研究发现:数字经济对高等教育高质量发展具有正向促进作用,具体看对高等教育要素、能力和功能等三个维度的影响依次减弱;数字经济对东部地区高等教育高质量发展有积极影响,对中西部地区无显著影响;数字经济发展水平对高等教育高质量发展具有边际效应递增的非线性影响;数字经济对本地高等教育高质量发展有正向影响,但对邻近地区存在负向空间溢出效应。为此,应强化数字化战略布局,促进高等教育与数字经济产业深度合作;通过打造合作示范区、制定地区发展规划、加大数字基础设施建设投资、开发数字教育资源共享平台等方式发挥东部地区优势,增强数字经济发达地区的辐射带动作用。

Methods for predicting the remaining useful life of equipment in consideration of the random failure threshold

The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

A time-over-threshold technique for PMT signals processing

A novel front-end circuit designed for PMT signals processing considering the solution of "Time Walk" correction is discussed in this paper. We are trying to apply the TOT (Time over Threshold) technique to our research. Different from traditional ways, where amplitude is measured, time width is measured for slew correction here, which takes the advantage of TDC. Expensive fast ADCs are abandoned and the whole time measurement electronics design becomes more effective and economical. Test boards have been developed and a convenient method is introduced to evaluate our TOT technique. Results have shown that a 10ps slew correction resolution is achieved throughout the amplitude range from -108mV to -2000mV for negative signals of both 5 ns leading and trailing edge with 10 ns 50%-50% pulse width.

Multi-Threshold Algorithm Based on Havrda and Charvat Entropy for Edge Detection in Satellite Grayscale Images

Automatic edge detection of an image is considered a type of crucial information that can be extracted by applying detectors with different techniques. It is a main tool in pattern recognition, image segmentation, and scene analysis. This paper introduces an edge-detection algorithm, which generates multi-threshold values. It is based on non-Shannon measures such as Havrda & Charvat’s entropy, which is commonly used in gray level image analysis in many types of images such as satellite grayscale images. The proposed edge detection performance is compared to the previous classic methods, such as Roberts, Prewitt, and Sobel methods. Numerical results underline the robustness of the presented approach and different applications are shown.

不均匀光照下的合作目标图像分割方法

针对复杂光照条件下目标测量中,合作目标图像很难被快速、准确分割这一难题,将模糊积分引入图像分割领域,改进传统的局部自适应阈值算法。首先,采用面积求和表(SAT)算法求出合作目标图像的积分图像;其次,根据模糊测度和所选择的离散模糊积分公式计算模糊积分图像;最后,根据模糊积分图像计算每个像素点的阈值,对原图像进行二值化。实验结果表明,改进后的基于模糊积分的局部自适应阈值算法分割的平均结构相似性(SSIM)指标相较于改进前普遍提升了10%~30%,分割效率大幅提升,最终成功地分割合作目标图像。

基于新型SQM的GNSS欺骗式干扰检测技术

GNSS在基础设施应用的定位、导航、授时领域具有重要的战略意义.鉴于GNSS信号落地功率极低、民码结构透明等特点,民用卫星信号极易受到干扰和欺骗攻击,使得GNSS在获得广泛应用的同时也受到严重的干扰问题.本文针对传统信号质量监测(signal quality monitoring,SQM)检测量检测概率低和可靠性差的问题,提出一种无需使用跟踪支路载波相位信息的新型SQM检测量,将检测数据值进行滑动平均处理,减少异常波动值造成检测虚警率增高的情况,并推导了检测量的统计分布特性,设置在无先验信息下的计算门限与测量门限,能够避免出现在欺骗与真实导航卫星信号相对载波相位为π的整数倍时,检测失效的情况.在美国德克萨斯大学奥斯汀分校的公共数据库TEXBAT(texas spoofing test battery)的实采信号上进行的对比实验结果表明,所提基于I、Q支路能量变化的双重门限SQM检测算法在不同实采信号场景下均能够在较短的时间内以较优的准确度检测出欺骗攻击.研究成果对未来抗欺骗通信设备开发具有一定的参考价值,不仅能够有效提高检测性能,且无需对接收机硬件结构进行大规模的改变.进行大规模的改变.

基于VisuShrink小波阈值变换的甲烷检测降噪技术

在利用可调谐半导体激光吸收光谱技术探测甲烷气体泄漏时,复杂的环境光干扰会降低浓度检测信号的信噪比使波形失真。为了抑制噪声,增强电信号与浓度值的线性相关度,提出一种以VisuShrink方法改善小波阈值函数的小波阈值去噪算法。该算法针对小波阈值去噪算法的软硬度不能平衡信号,存在失真的问题,利用统计方法对阈值进行计算,弱化间断点阈值跳变影响。通过仿真计算对比未加噪声前的信号与滤波后的信号,提出算法的信噪比为28.405 30,优于四阶巴特沃斯滤波器。VisuShrink方法在SNR和NCC参数上性能远优于巴特沃斯滤波器,与软阈值函数相近,在MSE均方误差分析上远小于软阈值精度,与巴特沃斯相近,具有较好的平滑特性。实际搭建检测平台对多个浓度样本采样进行多次采样,采用VisuShrink方法设计的滤波器将系统误差精度从4.14%提升至2.472%,算法可有效用于甲烷检测系统提高光谱数据的平滑性和准确性。

U型渠道中直角三角形量水槽体型设计

量水槽体型设计是量水槽应用于灌区的重要环节,目前尚无完善的体型设计标准可依。为解决直角三角形量水槽实际应用中的体型设计问题,提出以淹没度、壅水高度和上游弗劳德数的限制条件为标准的量水槽体型设计准则。在2种型号、3种坡度的U型渠道上,利用钢模板修建5座直角三角形量水槽进行灌区试验;建立了包含渠道坡度的流量公式,拟合了淹没阈值、壅水高度和上游弗劳德数的经验公式。研究表明:含有坡度的流量公式精度较高,平均相对误差仅为1.88%,相对误差的最大值为5.56%,相对误差在±5%范围以内的数据比例为98%;拟合得到的3个经验公式描述了淹没阈值、壅水高度和上游弗劳德数与量水槽喉口宽度之间的定量关系,可用于量水槽体型设计。基于体型设计准则和经验公式,建立了最大流量和最小流量下淹没度小于相应淹没阈值、最大流量下壅水高度小于渠道岸顶超高、最小流量下上游弗劳德数小于0.5的直角三角形量水槽体型设计方法。研究成果可为类似量水槽体型设计提供参考。

高精度管道漏风量测试仪的设计及实验研究

现有的国产漏风量测试仪普遍存在测量精度低、自动化水平低的问题,为此,基于漏风量测试仪的原理,开展了漏风量测试仪的结构设计、系统设计及整机实验研究。首先,对漏风量的测量过程、喷嘴流量测量原理进行了研究,推导了喷嘴流量测量公式,并根据相关标准设计了角接取压方式的喷嘴结构,基于仪器操作便捷性要求,对整机结构进行了设计;其次,采用空气密度补偿公式完成了高精度流量自适应测量,设计了基于自锁式电磁阀的换向调零电路,采用换向调零法对压差传感器零点漂移进行了补偿,使用小波阈值去噪算法消除了信号噪声,提高了流量的测量精度;采用抗积分饱和比例积分微分(PID)算法对流量进行了闭环控制;最后,研制了管道漏风量测试仪样机,开展了压差与流量测量精度与重复性实验。实验及研究结果表明:该高精度漏风量测试仪工作状态稳定,压差测量精度为±0.4%,流量测量精度为±2%;该漏风量测试仪的测量精度优于国内同类仪器,符合国家标准要求,具有良好的市场前景。

基于动态阈值分割法的接触轨图像关键特征点提取方法研究

在地铁系统中,接触轨的状态对于牵引供电系统的安全性至关重要,一旦接触轨几何参数超出正常范围,不仅会导致集电靴无法正常取流,进而影响运营车辆的顺畅运行,而且在极端情况下,还可能引发严重的安全事故。传统的接触轨几何参数测量方法在采集图像时通常面临背景复杂、干扰图像多等问题,以致检测数据量少、检测密度低,从而难以精确提取接触轨的关键特征信息。针对这一问题,文章提出一种新的方法,即结合动态阈值分割法、直线拟合以及投影计算的方式,有效解决接触轨特征点提取的难点,实现几何参数的精确测量。该方法不仅能够准确捕捉到接触轨的细微变化,还能够确保测量结果的可靠性和准确性。为进一步提高测量的便捷性和实用性,研究过程中同步研制出一套接触轨几何参数的便携式测量装置,并在地铁线路上进行现场应用示范,为实际运营中的接触轨状态监测提供有力的技术支持。

基于非接触式电压测量的电压监测系统

非接触式电压测量方法不直接与线路的金属导体部分接触,能适应多种应用场景的电压监测。设计一种利用改进的非接触式电压测量技术对线路电压进行测量并将测量得到的电压波形用于线路故障电压诊断的系统。对传统的非接触式电压测量技术进行拓扑分析并对测量电路拓扑进行改进,能够不受耦合电容影响并准确地测量线路上的电压。由于目前单一的故障特征提取方法具有局限性,为了利用由非接触式电压测量技术测量得到的电压波形实现准确地对线路故障电压进行识别诊断,提出了基于集成学习的故障电压状态识别系统,利用多种特征提取方法提取非接触式电压测量得到的电压波形特征,其识别结果用于对线路故障进行预警和处理。针对该电压监测系统,设计了测量精度和故障识别测试,得到稳态平均误差为0.9%,故障识别准确率最高可达到93%,表明该电压监测系统具有较高的精度和故障识别准确率。

光纤光栅干涉传感器位移测量误差高精度校准方法

在光纤光栅干涉传感器的位移测量中,受外界的振动或电磁等环境干扰因素的影响,对传感器的测量结果造成波动或误差。为了提高光纤光栅干涉传感器位移测量的效率,保证测量的准确性,提出光纤光栅干涉传感器位移测量误差高精度校准方法。通过分析光纤光栅干涉传感器的位移测量原理,获取位移测量数据;采用小波阈值方法,对位移测量数据去噪和平滑处理;利用反向传播(Back Propagation,BP)神经网络,对去噪后位移测量数据中误差数据高精度校准。实验结果表明,所提方法在降低误差校准计算复杂度的同时,能够有效提高误差校准精度、效率和运行稳定性。

分布式光纤温度传感信号小波去噪方法

在分布式光纤拉曼温度传感系统中,反斯托克斯光包含了温度信息,但其强度相对较弱,易受到噪声干扰。传统的信号去噪方法往往会滤除系统所具有的原始特征。针对这一问题,本文提出了改进的小波阈值方法,将3sigm准则与传统算法相结合。在该算法中,对信号进行小波分解,再对分解的各层小波系数进行阈值处理,使用3sigm值来区分有用信号和噪声,然后进行小波重构,实现信号去噪。在7 km长的多模光纤上进行温度测量实验,使用改进的算法对数据进行分析。结果表明,改进的方法处理后的温度波动平均为0.991℃,相较于传统算法得到的2.422℃,提高了1.431℃。与传统算法相比,3sigm准则算法提高了测温精度,并为阈值的选择提供了新的思路,使阈值的选择更具统计意义。