Auto-registration and Orthorecification Algorithm for the Time Series HJ-1A/B CCD Images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale,especially for multi-temporal image analysis.In this paper,an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multispectral data of small constellation for environmental and disaster mitigation(HJ-1A/B) which was launched by China in 2008.In this algorithm,the area-based matching method was used to automatically search tie points firstly,and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll,pitch and yaw direction.The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography,which are random errors in the images and cannot be corrected by the polynomial equation.Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm.The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels(the average residuals were 37.8 m,and standard deviations were 19.8 m).The accuracies of 45.96% validation points(VPs) were within 1 pixel and 90.33% VPs were within 2 pixels.The discussion showed that three main factors including the distortion patterns of HJ CCD images,percent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results.Although the influence of varying altitude of the satellite orbits is less than the other factors,it is noted that detailed satellite altitude information should be given in the future to get a more precise result.The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

偏置剪枝叠式自编码回声状态网络的时序预测

针对大多数模型对时间序列预测数据的预测准确率较低,为提升时间序列的预测精度,提出一种基于Biased Drop-weight的偏置剪枝叠式自编码回声状态网络(BD-AE-SGESN)的深度模型。以叠式ESN为多层深度网络框架,提出一种生成式AE算法生成每一层的输入权值,利用BD算法根据输入权重激活值进行剪枝。对比实验结果表明,该模型能够有效提升预测准确率,在3个不同的数据上,相比其它模型有着较小的预测误差和较高的稳定度。

基于动态规整与改进变分自编码器的异常电池在线检测方法

针对电池生产成组过程中,传统异常检测方法对混入的容量及压差异常电池检测精度低及生产结束后离线异常检测方法效率低等问题,该文提出一种集合长短期记忆变分自编码器与动态时间规整评价的锂电池异常在线检测方法(VAE-LSTM-DTW),实现了异常电池的在线检测,避免了离线异常检测所造成的时间和能源的浪费。该方法首先将长短期记忆网络(LSTM)引入变分自编码器(VAE)模型,训练电池时序数据重构模型;其次,在电池异常检测的度量标准中引入动态时间规整值(DTW),并基于贝叶斯寻优获得最优检测阈值,对每个单体电池重构数据的动态规整值进行异常辨别。实验结果表明,相较该领域传统异常检测方法,VAE-LSTM-DTW模型性能优越,查准率和F1值都得到了较大的提升,具有较高的有效性和实用性。

低压环境下RP-3航空煤油自燃特性研究

开展了低压环境下RP-3航空煤油的自燃特性研究,测试了101 kPa、80 kPa、60 kPa、40 kPa、20 kPa五种压力环境下航空煤油自燃点及着火延迟时间数据。结果表明,随着环境压力降低,自燃点升高,并基于谢苗诺夫热自燃理论,建立了耦合压力参数的自燃点预测模型。分析了环境压力对着火延迟时间的影响机制,建立了不同环境压力下的着火延迟时间预测模型。分析了GB/T 5332—2007标准在低压环境下测试存在的问题,并提出了相关改进方法。

空间自适应的被动声源定位系统的设计

传统的声源定位技术分为时延估计和空间定位两步,本文将几种经典的时延估计和空间定位方法加以整合与改进,提高了系统的实时性与抗干扰能力,创新性地提出并实现了根据声源位置变化而自适应调整麦克风阵列形态的定位系统,在不降低实时性的同时,有效地降低了定位误差。本文从定位精度、实时性和抗干扰能力三个方面对系统性能进行评估,实验证明:理想条件下,该声音定位系统的不确定度在4cm以内;对噪声和混响有较好的抵抗能力,在恶劣环境下不确定度不超过6cm;单次定位耗时0.5s,可以对运动声源或脉冲声源进行定位,有较强的实时性,可以适用于大部分应用场景。

基于冲击振动时延特性的异响源识别研究

异响影响汽车的整车NVH性能,汽车异响主要为冲击异响,识别冲击异响源对解决整车NVH性能问题有着重要意义。这里首先介绍了一种求解带阻尼多自由系统的强迫振动响应计算方法。然后利用带阻尼的五自由度质量弹簧系统介绍了冲击振动时延特性的定义,研究了质量、刚度以及阻尼对时延特性的影响。接着分析已知冲击振动源的实验数据,验证了其时延特性。最后,应用冲击振动时延特性,成功找到了某型号新下线车辆的坏路冲击异响源。因此,利用冲击振动的时延特性能寻找冲击异响源是一个行之有效的方法。

基于时间序列和神经网络的电力设备状态异常检测方法

为进一步提高电力设备异常检测方法对设备信息的利用率,发现更多潜在的设备故障,结合大数据分析技术和设备评估技术,提出了一种基于时间序列和神经网络的状态数据异常检测方法。通过时间序列自回归模型和自组织映射神经网络将连续的电力设备数据离散为单个序列,计算状态变量在时间轴上的转移概率,通过状态转移概率和聚类算法快速检测数据异常。通过实验对该方法的有效性进行验证。结果表明,该方法可以快速、有效地检测电力设备异常状态。

基于STC89C52的汽车灯具诊断电压监控装置

文章设计并制作基于STC89C52单片机的汽车灯具诊断引脚输出电压监控装置,其系统使用LMV331SE-7电压比较器,并结合单片机外部中断及定时器功能,实时监控汽车灯具可靠性试验中诊断引脚的输出电压情况,从而确认灯具的工作状态,保证汽车灯具试验的有效性。该装置结构简单,且成本低廉,可有效降低汽车灯具可靠性试验中的人工及设备成本。

OSAHS伴血压异常患者经鼻Auto-CPAP治疗对血压改变情况的研究

目的:研究OSAHS伴高血压患者,经鼻auto-CPAP治疗对其血压改变的情况。资料与方法:30例OSAHS伴高血压患者,其中包括已确诊高血压患者9例(维持既往口服降压药物治疗方案不变)及本次研究新发现高血压患者21例(既往无口服常规降压药物,暂不给予口服药物治疗)。给予患者每晚有效auto-CPAP(RESmart GII,北京怡和嘉业医疗科技有限公司)连续治疗,分别于治疗前及治疗第1、2、4周复查24 h ABP,比较治疗前后患者血压改变情况。结果:1)30例患者auto-CPAP治疗1周后,24 h ABP各时段血压均较治疗前明显降低,24 h SBP/DBP降低5.73/3.43 mm Hg,d SBP/DBP降低5.40/3.53 mmHg,n SBP/DBP降低7.03/2.73 mm Hg。2)9例既往高血压患者与21例本次研究新发现高血压患者相比,1周治疗后仅n SBP及n DBP的变化有统计学意义。3)部分患者延长治疗至2、4周,仅治疗1周与2周d SBP的变化有统计学意义。结论:1)auto-CPAP治疗OSAHS有助于患者血压的控制与改善。2)OSAHS伴高血压患者经atuo-CPAP治疗(1、2、4周)后,血压的降低主要出现在第1周的治疗上,临床上建议以第1周auto-CPAP治疗结束后的24 h ABP数据作为参考,这对于OSAHS伴有高血压患者的药物干预或调整具有一定的临床意义。3)在auto-CPAP对OSAHS伴高血压患者的治疗中,既往口服降压药物比无口服降压药物患者的夜间血压下降幅度更大。

Auto Hotkey在图书馆实时咨询工作中的应用

介绍了热键脚本语言Auto Hotkey,探讨了其在以腾讯QQ为工具的图书馆实时咨询中的应用。

基于VBA的Auto CAD实时协同系统开发

采用了以集中式及分布式相结合的混合结构,设计了一个实时的设计系统,通过SQL2000数据库系统构建了一个网络图形数据库,将协同设计者关于图形信息操作记录存储至数据库中,完成数据的统一。同时,在各协同者之间,构建通信发送操作信息,触发接收模块,然后读取数据库信息作图,实现协同设计的目标。

基于灰色时间序列随机组合模型在地下水埋深预测中的应用——以沧州市为例

地下水资源是支撑区域粮食生产的重要基础资源,为解决河北省浅层地下水水位持续下降问题,以河北省典型平原区沧州市为例,依据2007-2022年水位监测井观测数据,通过MATLAB软件结合灰色时间序列原理搭建地下水埋深灰色时间序列随机组合模型,揭示沧州市地下水埋深变化规律,为沧州市乃至河北省平原区地下水资源可持续开发利用提供依据。通过相关模拟方法证实该模型简洁实用,精度较高。研究结果表明:未来3年(2023-2025年),如果按照目前的发展趋势,沧州市地下水位会继续下降,平均年降幅为1.45 m左右。

基于ARMA模型的隧道变形预测及参数估计分析

以北京市海淀区某地铁站一体化棚户区改造项目为例,运用ARMA模型对高层建筑盖挖逆作法施工过程中邻近既有地铁隧道变形进行预测。以既有地铁隧道沉降实时监测数据为原始数据集,对原始数据集进行适当插补处理后,通过极大似然估计法对模型进行参数估计,给出了模型关键参数,构建了合理的预测模型。将模型预测结果与实测数据进行对比,显示预测结果与实测数据变化趋势高度吻合,充分验证了预测模型的可行性、有效性与稳定性。

Forecasting risk using auto regressive integrated moving average approach: an evidence from S&P BSE Sensex

The primary objective of the paper is to forecast the beta values of companies listed on Sensex,Bombay Stock Exchange(BSE).The BSE Sensex constitutes 30 top most companies listed which are popularly known as blue-chip companies.To reach out the predefined objectives of the research,Auto Regressive Integrated Moving Average method is used to forecast the future risk and returns for 10 years of historical data from April 2007 to March 2017.Validation accomplished by comparison of forecasted and actual beta values for the hold back period of 2 years.Root-Mean-Square-Error and Mean-Absolute-Error both are used for accuracy measurement.The results revealed that out of 30 listed companies in the BSE Sensex,10 companies’exhibits high beta values,12 companies are with moderate and 8 companies are with low beta values.Further,it is to note that Housing Development Finance Corporation(HDFC)exhibits more inconsistency in terms of beta values though the average beta value is lowest among the companies under the study.A mixed trend is found in forecasted beta values of the BSE Sensex.In this analysis,all the p-values are less than the F-stat values except the case of Tata Steel and Wipro.Therefore,the null hypotheses were rejected leaving Tata Steel and Wipro.The values of actual and forecasted values are showing the almost same results with low error percentage.Therefore,it is concluded from the study that the estimation ARIMA could be acceptable,and forecasted beta values are accurate.So far,there are many studies on ARIMA model to forecast the returns of the stocks based on their historical data.But,hardly there are very few studies which attempt to forecast the returns on the basis of their beta values.Certainly,the attempt so made is a novel approach which has linked risk directly with return.On the basis of the present study,authors try to through light on investment decisions by linking it with beta values of respective stocks.Further,the outcomes of the present study undoubtedly useful to academicians,researchers,and policy makers in their respective area of studies.

2.7-4.0 GHz PLL with dual-mode auto frequency calibration for navigation system on chip

A 2.7-4.0 GHz dual-mode auto frequency calibration(AFC) fast locking PLL was designed for navigation system on chip(SoC). The SoC was composed of one radio frequency(RF) receiver, one baseband and several system control parts. In the proposed AFC block, both analog and digital modes were designed to complete the AFC process. In analog mode, the analog part sampled and detected the charge pump output tuning voltage, which would give the indicator to digital part to adjust the voltage control oscillator(VCO) capacitor bank. In digital mode, the digital part counted the phase lock loop(PLL) divided clock to judge whether VCO frequency was fast or slow. The analog and digital modes completed the auto frequency calibration function independently by internal switch. By designing a special switching algorithm, the switch of the digital and analog mode could be realized anytime during the lock and unlock detecting process for faster and more stable locking. This chip is fabricated in 0.13 μm RF complementary metal oxide semiconductor(CMOS) process, and the VCO supports the frequency range from 2.7 to 4.0 GHz. Tested 3.96 GHz frequency phase noise is -90 d Bc/Hz@100 k Hz frequency offset and -120 d Bc/Hz@1 MHz frequency offset. By using the analog mode in lock detection and digital mode in unlock detection, tested AFC time is less than 9 μs and the total PLL lock time is less than 19 μs. The SoC acquisition and tracking sensitivity are about-142 d Bm and-155 d Bm, respectively. The area of the proposed PLL is 0.35 mm^2 and the total SoC area is about 9.6 mm^2.

Parameter Estimation of Time-Varying ARMA Model

The auto-regressive moving-average (ARMA) model with time-varying parameters is analyzed. The time-varying parameters are assumed to be a linear combination of a set of basis time-varying functions, and the feedback linear estimation algorithm is used to estimate the time-varying parameters of the ARMA model. This algorithm includes 2 linear least squares estimations and a linear filter. The influence of the order of basis time-(varying) functions on parameters estimation is analyzed. The method has the advantage of simple, saving computation time and storage space. Theoretical analysis and experimental results show the validity of this method.

Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis

The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SaS distribution model because of the presence of impulses.Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order(FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform(FLO-STFT), fractional lower order Wigner-Ville distributions(FLO-WVDs), fractional lower order Cohen class time-frequency distributions(FLO-CDs), fractional lower order adaptive kernel time-frequency distributions(FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average(FLO-TFARMA) model time-frequency representation method.The methods and the exiting methods based on second order statistics in SaS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized.Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.

Detecting DDoS Attacks against Web Server Using Time Series Analysis

Distributed Denial of Service （DDoS） attack is a major threat to the availability of Web service. The inherent presence of self-similarity in Web traffic motivates the applicability of time series analysis in the study of the burst feature of DDoS attack. This paper presents a method of detecting DDoS attacks against Web server by analyzing the abrupt change of time series data obtained from Web traffic. Time series data are specified in reference sliding window and test sliding window, and the abrupt change is modeled using Auto-Regressive （AR） process. By comparing two adjacent nonoverlapping windows of the time series, the attack traffic could be detected at a time point. Combined with alarm correlation and location correlation, not only the presence of DDoS attack, but also its occurring time and location can be deter mined. The experimental results in a test environment are illustrated to justify our method.

基于Auto-Regressive的河北省旅游接待人数预测研究

旅游人数是发展旅游业的重要指标,对河北省未来接待旅游人数的预测一直受到河北省旅游局的重视。本文通过以1990年~2019年河北省旅游数据为依托,首先,从市场、景区、政策三个方面分析了河北省旅游业现状,然后进行了河北省历年来旅游接待人数数据的平稳性和白噪声检验,分别运用非平稳时间序列的两种残差自回归模型方法(因变量关于时间的回归模型和延迟因变量回归模型)对以往河北省旅游接待人数建立模型。研究结果发现,前者模型拟合效果较好,并对未来旅游人数进行短期预测。最后为促进河北省旅游业的发展提出了一些相关建议。

基于多尺度时间序列块自编码Transformer神经网络模型的风电超短期功率预测

风电超短期功率预测过程中对时间依赖性的有效捕捉与建模,将直接影响风电功率时间序列预测模型的稳定性和泛化性。为此,提出一种新型时序Transformer风电功率预测模型。模型架构在逻辑上分为时间块自编码、隐空间Transformer自注意力时序自回归、随机方差缩减梯度(stochastic variance reduce gradient,SVRG)优化3个部分。首先,依稀疏约束及低秩近似规则,风电功率时空数据被半监督映射至隐空间;其次,隐空间编码经由多头自注意力网络完成时序自回归预测;最后,模型采用方差缩减SVRG优化算法降低噪声,达到更高预测效能。实验结果表明,所提新型Transformer架构能稳定有效进行超短期风电功率预测,预测结果在准确性、泛化性方面相较于传统机器学习模型都有明显提升。