Physiological signal processing in heart rate variability measurement:A focus on spectral analysis

Human physiological(biological)systems function in such a way that their complexity requires mathematical analysis.The functioning of the brain,heart and other parts are so complex to be easily comprehended.Under conditions of rest or work,the temporal distances of successive heartbeats are subject to fluctuations,thereby forming the basis of Heart Rate Variability(HRV).In normal conditions,the human is persistently exposed to highly changing and dynamic situational demands.With these demands in mind,HRV can,therefore,be considered as the human organism’s ability to cope with and adapt to continuous situational requirements,both physiologically and emotionally.Fast Fourier Transform(FFT)is used in various physiological signal processing,such as heart rate variability.FFT allows a spectral analysis of HRV and is great help in HRV analysis and interpretation.

Model-Driven Derivation of Domain Functional Requirements from Use Cases

Domain analysis is essential to core assets development in software product line engineering. Most existing approaches, however, depend on domain experts’ experience to analyze the commonality and variability of systems in a domain, which remains a manual and intensive process. This paper addresses the issue by proposing a model-driven approach to automating the domain requirements derivation process. The paper focuses on the match between the use cases of existing individual products and the domain functional requirements of a product line. By introducing a set of linguistic description dimensions to differentiate the sub-variations in a use case, the use case template is extended to model variability. To this end, a transformation process is formulated to sustain and deduce the information in use cases, and to match it to domain functional requirements. This paper also presents a prototype which implements the derivation as a model transformation described in a graphical model transformation language MOLA. This approach complements existing domain analysis techniques with less manual operation cost and more efficiency by automating the domain functional requirements development.

Soil liquefaction assessment by using hierarchical Gaussian Process model with integrated feature and instance based domain adaption for multiple data sources

For soil liquefaction prediction from multiple data sources,this study designs a hierarchical machine learning model based on deep feature extraction and Gaussian Process with integrated domain adaption techniques.The proposed model first combines deep fisher discriminant analysis(DDA)and Gaussian Process(GP)in a unified framework,so as to extract deep discriminant features and enhance the model performance for classification.To deliver fair evalu-ation,the classifier is validated in the approach of repeated stratified K-fold cross validation.Then,five different data resources are presented to further verify the model’s robustness and generality.To reuse the gained knowledge from the existing data sources and enhance the generality of the predictive model,a domain adaption approach is formu-lated by combing a deep Autoencoder with TrAdaboost,to achieve good performance over different data records from both the in-situ and laboratory observations.After comparing the proposed model with classical machine learn-ing models,such as supported vector machine,as well as with the state-of-art ensemble learning models,it is found that,regarding seismic-induced liquefaction prediction,the predicted results of this model show high accuracy on all datasets both in the repeated cross validation and Wilcoxon signed rank test.Finally,a sensitivity analysis is made on the DDA-GP model to reveal the features that may significantly affect the liquefaction.

Data-aided channel estimation and frequency domain equalization of minimum-shift keying in optical transmission systems

We demonstrate a digital optical communication system based on minimum shift keying （MSK） signal transmission with coherent detection. 5-Gb/s MSK signal can transmit over a 160-km standard single mode fiber （SSMF） without phase compensation. At the receiver, we use data-aided channel estimation and frequency domain equalization （FDE） techniques in the digital signal processing （DSP） algorithm, then analyze its performance characteristics compared with quadrature phase shift keying （QPSK） format. The simulation results show that the MSK format will be a potential candidate for next-generation access network.

A Method for Service-Oriented Personalized Requirements Analysis

The development of Web service has changed the process of software production, and requirements engineering becomes the key issue of service-oriented software engineering. Meantime, it reduces the degree of difficulty of software production, which facilitates end-users to customize software according to their personalized requirements. The paper proposes a method for service-oriented personalized requirements analysis, which is based on domain goal model and process model. The method can inform users of potential errors in requirements by detecting the correctness of requirements, which is driven by users’ personalized operations on goal models, and customize personalized processes to satisfy users’ requirements by reusing domain processes. The personalized processes are the basis for Web service discovery and composition.

基于图像处理的路面裂缝特征提取方法

裂缝是一种常见的路面损坏形式,及时、准确地检测和评估路面裂缝的状态,对于路况监测和养护决策具有重要的意义。文章提出了一种基于图像处理的路面裂缝特征提取方法,首先,对分割得到的路面二值图像进行连通域分析,利用裂缝区域和干扰区域之间的特征差异,筛选出真正代表裂缝的连通域;然后,采用改进的快速并行细化算法提取出裂缝骨架,以端点所在分支的长度为标准来去除骨架中的毛刺,针对骨架断裂和向内缩短的问题,通过端点方向以及端点间距离来进行骨架的连接和生长。实验结果表明,该方法能够有效去除分割结果中的干扰区域,提取出清晰完整且无毛刺的裂缝单一像素骨架,较好地反映了裂缝的主要结构和形态特征。

基于COMSOL软件的直角弯管声波传播特性规律研究

基于声波传播理论,建立并推导了声波在弯管处的Helmholtz方程。采用COMSOL Multophysics软件的压力声学模块,对不同弯曲半径的直角弯管进行频域分析,研究了不同弯曲半径弯管内声波传播特性规律。研究结果显示,弯曲半径对弯管内声波散射系数和传输损耗具有显著影响,弯曲半径较大的弯管曲率较小,弯管内声波散射效应较弱,传输损耗较小,声压级较高;弯曲半径较小的弯管曲率较大,弯管内声波散射效应较强,传输损耗较大,声压级较低。低频段内,声波波长大于弯管弯曲半径,散射系数与传输损耗较小;高频段内,声波波长接近或小于弯管弯曲半径,散射系数与传输损耗较大。

高低频通道特征交叉融合的低光人脸检测算法

低光条件下捕获的人脸图像存在着噪声严重、对比度低等不足,极大影响了现有人脸检测器的准确性,另外,现有的低光图像检测算法欠缺小区域人脸信息的提取能力。为此,提出一种基于深度学习的两阶段人脸检测算法,即利用现有的低光图像增强算法对人脸图像进行增强后再进行检测。为提升检测算法对人脸信息的提取能力,设计一种新型的高低频通道特征交叉融合方法,该方法首先使用高低频通道特征可分离模块分离出不同尺度特征的高低频信息,然后对上述信息进行交叉融合,提升网络提取高频细节信息和低频色域信息的能力,进而提高检测网络的性能。对比试验和消融试验验证了该研究方法的有效性,试验结果表明该方法优于基准方法4.0%mAP。

基于领域分析的结构线性静力软件串并行一致化方法

并行CAE软件的计算结果串并行一致性是其计算结果可信的必要条件。然而,软件研发时常引入串并行不一致缺陷,其形式众多,现象相互耦合,散布于海量代码中,成为实现CAE软件串并行一致性的挑战。文中以结构线性静力软件的串并行一致性需求为切入点,针对现有的“专家知识法”与“缺陷定位法”应用于CAE软件串并行一致化时存在的粒度粗、准度差、成本高和缺乏系统性问题,引入领域分析方法,并与专家知识和数据流状态比对结合,提出了一种适用于结构线性静力的串并行一致化方法,实现了结构线性静力软件串并行不一致缺陷的细粒度、高准度与低成本系统性识别与修复。基于前述方法形成相关工具,并将方法与工具应用于SSTA的串并行一致化,识别并修复其中8处串并行不一致缺陷,使其通过90余真实模型的串并行一致考核,并实现串并行结果严格一致;同时,该方法与工具还将串并行不一致缺陷定位耗时由平均大于两人天降低至数人时。

天津市居住型地铁站域街道景观评价与优化策略——以西康路站为例

“十四五”规划指出,我国将全面提高城市基础设施运行效率及推进城市基础设施协同建设。文章采用量化研究的方式,定量评估地铁站域街道景观的影响因素,从而提出相应的优化策略,旨在为今后相关问题研究,以及站域周边街道景观优化改良工作贡献力量。文章采用核密度分析法,结合实地考察确定研究样本,并通过层次分析法(AHP)与语义分析法(SD)的综合评价模型,对西康路站这一居住型地铁站域的街道景观进行定量评价。结果显示,西康路站地铁站域街道景观在改造的过程中,首先要满足街道设施和街道绿化的配置需求;在此基础上,应根据当地居民及当地环境,优化街道交通环境,协调好街道建筑之间的关系;同时,体验感受也是街道景观改造的一个侧重点。

Implementation of Time-Scale Transformation Based on Continuous Wavelet Theory

The basic objective of time-scale transformation is to compress or expand the signal in time field while keeping the same spectral properties. This paper presents two methods to derive time-scale transformation formula based on continuous wavelet transform. For an arbitrary given square-integrable function f(t),g(t) = f(t/λ) is derived by continuous wavelet transform and its inverse transform. The result shows that time-scale transformation may be obtained through the modification of the time-scale of wavelet function filter using equivalent substitution. The paper demonstrates the result by theoretic derivations and experimental simulation.

DSML ProcGraph: Overview and a Mid-Size Industrial Application Example

This paper presents model-based approach to process-control software development. The presented approach enables modelling of control software in a straightforward manner and, at the same time, on a high level of abstraction. The essence of the presented approach is a high-level, domain-specific modelling language ProcGraph, which is based on three types of diagrams that describe the modelled system using a domain-oriented hierarchical structure of interdependent procedural control entities and state-transition diagrams describing the behaviour of the procedural control entities. The presented concept is demonstrated by means of higher-level model segments of a real process-control application that deals with the micronisation process in the production of titanium dioxide. The presented industrial case shows that the application of ProcGraph provides adequate expressive power for an elegant preparation of graphic specifications in a transparent and easy way.

基于MyDAQ的信号分析与处理创新实验设计

根据信号分析与处理课程实验教学要求,以信号的采样与恢复实验为例,综合利用MyDAQ的任意波形发生器、示波器、波特分析仪和动态信号分析仪等实验仪器,设计口袋实验板以构建创新实验平台,实现从时域和频域两个维度探索MyDAQ在信号分析与处理课程实验中的创新应用。实验设计有助于巩固信号基础理论,提升学生复杂系统分析设计能力,激发实验兴趣,培养发现和解决实际工程问题的能力。实践表明,基于MyDAQ的信号分析与处理课程实验设计,具有实验维度广、操作方便灵活等特点并具备设计性、趣味性和创新性,有利于学生参与实验的自主性和提升创新实践水平。

基于灰色关联分析和聚类云模型的变压器油纸绝缘状态评估

电力变压器复合油纸绝缘状态的准确诊断对电力系统安全稳定运行以及设备自身运维具有重要指导意义。针对油纸绝缘介电响应少数特征量评估和未考虑系统随机性导致评估不准确的问题,提出基于灰色关联分析(grey relational analysis,GRA)与聚类云模型的评估方法。首先,基于回复电压法和扩展德拜模型提取5个相关特征量,建立油纸绝缘状态评估体系。然后,针对多特征量在反映绝缘状态的敏感性差异,采用组合赋权法综合GRA和改进层次分析法,避免了数据信息丢失,使权重分配更加合理。最后,利用云模型雾化特性反映数据随机性,全面考虑评估指标等级分类边界的随机性和模糊性后构建了聚类云模型隶属度选择器。通过多台不同糠醛含量变压器实测数据进行验证表明,该评估方法不仅能够准确反映变压器实际绝缘状态,而且能体现其劣化趋势,为检修策略的制定提供参考依据。

基于知识图谱与依存句法分析的井控领域文本相似度计算

针对油田井控领域文本相似度计算时,由于语义信息不足导致相似度计算结果准确度不高的问题。论文提出一种基于知识图谱和依存句法分析的文本相似度计算方法。在该方法中,首先利用依存句法技术获取文本核心关系词对,然后基于井控知识图谱和同义词词林对计算不同的关系词对间的相似度,再结合基于词向量的句子相似度计算,分配权重,最后获取井控文本的综合相似度。实验结果表明:与现有方法相比,该方法融合了文本在词和句子级别上的信息,更好地对文本语义进行识别,较大地提高了油田井控领域文本之间相似度计算结果准确率。

随钻井下振动数据分析软件设计与实现

随钻井下振动数据的分析和处理对安全钻井、仪器维保策略选择、钻具疲劳损伤评估、钻进效率评估等具有重要意义。文中在Visual Studio环境下,利用C#语言开发了随钻井下振动数据分析软件,对振动数据进行时域和频域分析,实现了振动信号时域波形显示、振动量级统计分析、振动信号频谱分析、横向和纵向特征强度计算、粘滑比计算、井下异常振动状态识别等功能,解决了大数据文件的快速读取问题,提高了软件的运行效率和鲁棒性。利用该软件对现场实测振动数据的分析结果表明,振动数据分析结果正确,软件运行稳定,数据处理速度快,满足用户使用需求。

基于改进连通域的铝合金薄板对接焊缝特征点识别

薄板自动化焊时产生的光反射、飞溅、粉尘等噪声使焊缝位置信息被遮挡,从而影响特征点的识别与提取。因此,提出了用连通区域的算法对焊缝的特征进行标记,并改进了连通区域算法用于提取焊缝特征点和获取其位置信息。在图像预处理之前,用感兴趣区域(Region of interest,ROI)方法对激光条纹进行图像分割,可滤除大量弧光、飞溅等噪声;在图像预处理的过程中,采用中值滤波和最大类间方差的二值化算法降低激光条纹附近的干扰噪声,将激光条纹与背景分离,使焊缝特征更清晰、明显;在图像预处理后,用连通区域的方法对激光条纹进行标记,通过改进的算法判断出连通区域的位置,从而识别焊缝特征点,获得焊缝特征点的位置信息。该算法不仅保留了焊缝激光条纹的边缘信息,还能在复杂的工作环境中完成焊缝特征的识别。通过对比薄板的实际间隙宽度和试验计算出的间隙宽度,该算法平均误差在0.067 mm以内,满足工业中的精度要求,适合激光视觉的焊缝跟踪过程。

基于Ansys软件的轮胎硫化工艺数值分析

利用仿真分析软件Ansys Fluent和Workbench等对硫化胶囊内部流体域、胶囊内壁和轮胎进行仿真建模和热流耦合处理,分析硫化胶囊内部流体域平均压力、入口处流体域平均速度、内部液态水体积分数和流体域温度等的变化规律及自硫化机合模至硫化324 s内轮胎3个点位温度的变化规律。对比轮胎相应位置的仿真温度与实测温度曲线,验证了仿真模型的准确性,利用该模型可优化轮胎硫化工艺。

汽车振动信号分析方法研究

针对汽车NVH性能提升过程中所面对的振动信号处理问题,在探讨振动信号测试的基础上,借助Matlab软件对振动信号处理方法进行分析研究。在时域范围内,计算信号的均值、方差和均方根值,并绘制概率密度曲线;在频域范围内,基于DFT理论计算信号的幅值谱、功率谱和相干函数。最后,对多测点多方向的测量信号同时进行分析,通过统计和对比得出发动机振动信号能量在峰值频率的分布状况和点火频率的对应关系,为改善汽车NVH性能提供必要的数据支持。

抹香鲸点击信号的小波能量积累处理

传统的海洋生物声学信号分析处理偏重时域和频域,未能全面利用信号中的有效信息。为了提高抹香鲸叫声点击信号处理和识别能力,基于点击信号的时域、频域以及时频联合特征,开展抹香鲸点击信号的小波多尺度能量积累处理和分析研究,提取小波积累能量特征。抹香鲸叫声实测数据处理结果表明:小波能量积累处理能够区分背景噪声、混响信号和抹香鲸点击信号,并且能够有效去除背景噪声和混响信号,保持点击信号趋势基本不变,大幅度提高点击信号的幅度信噪比,信噪比最大为4.3,适用于复杂海洋环境噪声中的生物声学信号分析处理。