Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease(CHD)is critical.However,it is challenging to achieve accurate CHD diagnosis via detecting breath.In this work,heterostructured complexes of black phosphorus(BP)and two-dimensional carbide and nitride(MXene)with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy.A light-activated virtual sensor array(LAVSA)based on BP/Ti_(3)C_(2)Tx was prepared under photomodulation and further assembled into an instant gas sensing platform(IGSP).In addition,a machine learning(ML)algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD.Due to the synergistic effect of BP and Ti_(3)C_(2)Tx as well as photo excitation,the synthesized heterostructured complexes exhibited higher performance than pristine Ti_(3)C_(2)Tx,with a response value 26%higher than that of pristine Ti_(3)C_(2)Tx.In addition,with the help of a pattern recognition algorithm,LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols,ketones,aldehydes,esters,and acids.Meanwhile,with the assistance of ML,the IGSP achieved 69.2%accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients.In conclusion,an immediate,low-cost,and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD,which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets

About 60%of emissions into the earth’s atmosphere are produced by the transport sector,caused by exhaust gases from conventional internal combustion engines.An effective solution to this problem is electric mobility,which significantly reduces the rate of urban pollution.The use of electric vehicles(EVs)has to be encouraged and facilitated by new information and communication technology(ICT)tools.To help achieve this goal,this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience.The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle’s current position,battery type,state of charge,nearby charge point availability,and compatibility.In particular,the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points(CPs).To this purpose,two virtual sensors(VSs)are designed,modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking.In particular,the first VS is devoted to locate and find available CPs in a preferred area,whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase.A UML activity diagram describes VSs operations and cooperation,while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors(CP operator,EV manufacturer,etc.).Furthermore,two timed Petri Nets(TPNs)are designed to model the proposed VSs,functioning and interactions as discrete event systems.The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.

Wear dependent virtual flow rate sensor for progressing cavity pumps with deformable stator

This contribution presents a novel wear dependent virtual flow rate sensor for single stage single lobe progressing cavity pumps. We study the wear-induced material loss of the pump components and the impact of this material loss on the volumetric efficiency. The results are combined with an established backflow model to implement a backflow calculation procedure that is adaptive to wear. We use a laboratory test setup with a highly abrasive fluid and operate a pump from new to worn condition to validate our approach. The obtained measurement data show that the presented virtual sensor is capable of calculating the flow rate of a pump being subject to wear during its regular operation.

Energy-efficient virtual sensor-based deep reinforcement learning control of indoor CO_(2)in a kindergarten

High concentrations of indoor CO_(2)pose severe health risks to building occupants.Often,mechanical equipment is used to provide sufficient ventilation as a remedy to high indoor CO_(2)concentrations.However,such equipment consumes large amounts of energy,substantially increasing building energy consumption.In the end,the issue becomes an optimization problem that revolves around maintaining CO_(2)levels below a certain threshold while utilizing the minimum amount of energy possible.To that end,we propose an intelligent approach that consists of a supervised learning-based virtual sensor that interacts with a deep reinforcement learning(DRL)-based control to efficiently control indoor CO_(2)while utilizing the minimum amount of energy possible.The data used to train and test the DRL agent is based on a 3-month field experiment conducted at a kindergarten equipped with a heat recovery ventilator.The results show that,unlike the manual control initially employed at the kindergarten,the DRL agent could always maintain the CO_(2)concentrations below sufficient levels.Furthermore,a 58%reduction in the energy consumption of the ventilator under the DRL control compared to the manual control was estimated.The demonstrated approach illustrates the potential leveraging of Internet of Things and machine learning algorithms to create comfortable and healthy indoor environments with minimal energy requirements.

Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems

For building heating,ventilation and air-conditioning systems(HVACs),sensor faults significantly affect the operation and control.Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand.Owing to its high calibration accuracy and in-situ effectiveness,a virtual sensor(VS)-assisted Bayesian inference(VS-BI)sensor calibration strategy has been applied for HVACs.However,the application feasibility of this strategy for wider ranges of different sensor types(within-control-loop and out-of-control-loop)with various sensor bias fault amplitudes,and influencing factors that affect the practical in-situ calibration performance are still remained to be explored.Hence,to further validate its in-situ calibration performance and analyze the influencing factors,this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit(AHU)terminal.Three target sensors including air supply(SAT),chilled water supply(CHS)and cooling water return(CWR)temperatures are investigated using introduced sensor bias faults with eight different amplitudes of[−2℃,+2℃]with a 0.5℃ interval.Calibration performance is evaluated by considering three influencing factors:(1)performance of different data-driven VSs,(2)the influence of prior standard deviationsσon in-situ sensor calibration and(3)the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes.After comparison,a long short term memory(LSTM)is adopted for VS construction with determination coefficient R-squared of 0.984.Results indicate thatσhas almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR.The potential of using a prior standard deviationσto improve the calibration accuracy is limited,only 8.61%on average.For system within-control-loop sensors like SAT and CHS,VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.

Stereoscopic Camera-Sensor Model for the Development of Highly Automated Driving Functions within a Virtual Test Environment

The need for efficient and reproducible development processes for sensor and perception systems is growing with their increased use in modern vehicles. Such processes can be achieved by using virtual test environments and virtual sensor models. In the context of this, the present paper documents the development of a sensor model for depth estimation of virtual three-dimensional scenarios. For this purpose, the geometric and algorithmic principles of stereoscopic camera systems are recreated in a virtual form. The model is implemented as a subroutine in the Epic Games Unreal Engine, which is one of the most common Game Engines. Its architecture consists of several independent procedures that enable a local depth estimation, but also a reconstruction of a whole three-dimensional scenery. In addition, a separate programme for calibrating the model is presented. In addition to the basic principles, the architecture and the implementation, this work also documents the evaluation of the model created. It is shown that the model meets specifically defined requirements for real-time capability and the accuracy of the evaluation. Thus, it is suitable for the virtual testing of common algorithms and highly automated driving functions.

Energy-efficient low-latency virtual cluster synchronization algorithm based on SMAC protocol in wireless sensor networks

The MAC protocol design for wireless sensor networks has been researched and developed for decades. SMAC protocol is a famous energy-efficient MAC protocol. Based on SMAC protocol, we find that the boundary nodes in the cluster-shaped synchronization structure bring energy consumption seriously, and provide a virtual cluster aggregation (VCA) algorithm. Because the bounder node follows multiple schedules in one cycle, it may deplete earlier and cause segmentation in wireless sensor networks. The algorithm reduces energy consumption of boundary nodes and extends the lifetime of entire sensor network by merging different virtual clusters, but increases the data transmission delay. Because the sensor nodes have the fixed duty cycle, the larger the coverage area of network is, the greater the data transmission delay increases. We propose the dynamic duty cycle (DDC) algorithm to solve this effect. When the network load and data transmission delay increase, the DDC algorithm exponentially changes the duty cycle of the node to reduce latency. The simulation results show that the performance of SMAC with the VCA and DDC algorithm obtains improvement significantly.

数字孪生驱动的巷道自动成形截割虚拟调试方法研究

针对目前巷道自动成形截割控制调试周期长、调试成本大、安全风险大、成形质量难以评价等问题,提出了一种数字孪生驱动的巷道自动成形截割虚拟调试方法。采用基于即时外观建图(RTAP−MAP)技术重建巷道三维环境,构建掘进机控制系统模型,形成虚拟调试环境,并利用虚拟传感器技术实现物理空间到虚拟环境状态的精准映射。针对难以量化评估断面成形质量的问题,确立了巷道自动成形截割性能评价方法,以断面成形截割控制过程在数据传输中心的记录为基础,主要对断面成形精度、截割效率与油缸开关次数、硬岩切割调整、超挖欠挖4个评价指标进行计算,从而为深度学习算法的迭代优化提供精准反馈信号,并提出了一种融合强化学习的自动截割控制策略,以提高自动化作业的适应性和精确度。为验证该虚拟调试方法的有效性和准确性,搭建了掘进机自动控制实验平台,并将虚拟调试系统应用于掘进巷道成形截割自动控制程序中。虚拟仿真结果表明:①被调试软件在控制关键点位处的X,Y,Z轴定位误差的最大值分别为74.8,72.93,123.67 mm,说明虚拟调试方法的定位精度达到性能要求。②虚拟样机与物理样机轨迹基本一致,说明该调试方法实现了对物理空间的映射。应用结果表明:①强化学习控制器在虚拟掘进测试中适应了复杂环境,将虚拟传感器输入有效转换为精准控制指令,验证了模拟−现实迁移训练的可行性。通过处理掘进精度和避免超欠挖的实时反馈,控制器学习并优化了策略。②优化后的断面成形截割控制性能得到了提升,根据数据库中控制量时间戳的记录,用时126 s,较优化前耗时减少了8 s。③优化后截割部末端轨迹跟踪最大误差为6.0 mm,较优化前降低了0.3 mm,避免了截割轨迹抖动导致的欠挖,同时使得轨迹和断面更加平滑。

面向未来的中国智慧林业:观测仪器体系的演进与发展趋势

森林观测信息的生产是支持智慧林业工作开展的基础和先导。本文以森林观测仪器技术体系的构建历史、现状和未来为切入点,以基础性公共技术的发展脉络为支撑,深入分析森林观测仪器与森林观测需求的耦合程度。传统森林观测仪器体系在时效性和准确性上已经无法满足日益发展的林业调查的需要,观测仪器存在孤立性高、难以集成、操作繁琐、效率低等问题。这主要是由于当前针对森林观测研究偏重对成熟仪器使用和数据分析,观测仪器的研发偏重算法补偿和集成第三方元件,轻物理结构设计等因素造成。面对基本测量原理鲜有更新、森林观测标准制定滞后于新型技术发展等问题,本文从对观测仪器现在和未来的联合模式进行阐述,从逻辑架构到物理技术层面上分析森林观测仪器联合模式可能在未来出现的发展状况与应用前景。最后,基于当前基础性技术发展的趋势,对可能出现的几种新形态的森林观测仪器进行推测,并以此展望在近未来可能在中国实现的一种智能森林观测体系的雏形。

Essential Topics on Constructing WCDS-based Virtual Backbone in Wireless Sensor/Mesh Networks

Clustering or connected dominating set (CDS) both approaches can establish a virtual backbone (VB) in wireless sensor networks (WSNs) or wireless mesh networks (WMNs). Each cluster consisting of a cluster head (CH) and its neighboring nodes can form a dominating set. After some bridging nodes were selected, cluster heads (CHs) connected through these bridging nodes naturally formed a CDS. Although CDS provides obvious backbone architecture, however, the number of cluster heads and bridging nodes may be too large, this may cause the loss of advantages of virtual backbone. When we effectively reduce their numbers, more effectively WCDS (Weakly Connected Dominating Set) can be fining out. Some essential topics on constructing WCDS-based VB in WSN/WMN are discussed in this paper. From the point of view of three different protocol layers, including network (NWK) layer, MAC layer, and physical (PHY) layer, we explore their cross-layer research topics and design algorithms. For NWK layer, area-based WCDS algorithms and routing strategies including via VB and not via VB are discussed. For MAC layer, a WCDS-based energy-efficient MAC protocol is presented. For PHY layer, battery-aware alternative VB selections and sensor nodes with different transmission ranges are addressed.

一种基于虚拟星敏感器的卫星姿态确定方法

对于配置多个星敏感器的卫星,采用常规的定常增益卡尔曼滤波方法进行姿态确定时,存在滤波定常增益矩阵众多、系统复杂的问题,为了简化滤波系统设计,统一定常增益矩阵,提出一种基于虚拟星敏感器的姿态确定方法.给出一种计算量小、适合星载计算机在轨实时计算的星敏感器时间滞后补偿及相对基准标定算法,将星敏感器的输出数据统一到当前星时,同时将星敏感器的测量基准统一.基于单星敏/双星敏的输出数据构造虚拟星敏感器(安装矩阵为单位阵)的输出数据,设计统一的定常增益矩阵进行姿态确定.仿真结果表明,本方法与常规的定常增益卡尔曼滤波方法姿态确定精度相当,从而验证该方法有效,且具有重要的工程应用价值.

基于直驱并联机构的六自由度振动校准系统研究

直驱并联机构六自由度振动校准系统可以实现高精度和多自由度的运动模拟和校准,具有良好的应用前景。为了解决平台驱动关节滑块位置跟踪精度低的问题,对该机构进行动力学力矩前馈补偿分析。利用虚功原理建立该机构动力学模型,提出一种基本伺服算法与动力学前馈补偿相结合的控制策略。在实验样机上进行力矩前馈补偿,实验结果表明,在开放伺服程序增加力矩前馈补偿后,可以降低该机构在运动时的跟随误差,进行不同幅值单自由度正弦运动时,跟随误差分别下降40.32%、39.04%、43.24%、48.19%。并对振动台进行了性能检测,在平台基础上,搭建激光测量系统和数据采集模块,进行传感器校准和性能分析,平台横向振动比较小,谐波失真度均小于2%,平台具有较好的稳定性和性能。

基于数字孪生的装配式钢节点混凝土框架结构建造技术

与传统现浇框架结构相比,装配式钢节点混凝土框架结构建造速度更快,但框架钢连接节点处存在应力突变,因此,有必要采用基于数字孪生的手段监测该实际工程结构的施工过程,以获得钢节点对结构性能的影响。基于BIM、有限元和传感器等技术搭建了基于数字孪生的新型装配式钢节点混凝土框架结构的智能建造框架,从物理数据收集、虚拟模型建立、模型信息交互3个方面提出基于数字孪生的建筑结构智能建造实现方法。在新型装配式钢节点混凝土框架实际工程建造阶段,采用传感器技术实现工程中关键点数据的实时监测,并将其与BIM和有限元数据进行比较,进一步调整与修正物理模型中的框架结构受力情况,最终实现装配式钢节点混凝土框架数字孪生模型的建立和应用。研究表明,该数字孪生模型能有效对新型装配式钢节点混凝土框架结构进行实时监测,实现基于传感器网络和时空参数分析的危险点预测,有效减少资源消耗,为该结构的应用提供有效数据信息。

基于LabVIEW-VISA的传感器检测虚拟实验平台设计

为提升应用本科机电专业学生智能传感应用能力,设计传感器检测虚拟实验平台。提出基于LabVIEWVISA的传感器检测虚拟实验平台及实验方法,在LabVIEW-VISA应用设计基础上搭建了传感器检测虚拟实验平台整体架构;设计了传感器实验箱、MyDAQ便携式测量、万用表、应变式传感器等主要硬件;进行了数据采集模块、数据处理、输出模块及信号处理电路等软件设计,利用信号处理电路实现数据在平台中的双通道传输,实现了预期目标。结果表明,设计的传感器检测虚拟实验平台实验验证灵敏度高、线性误差小,有利于提高人才实践动手能力培养。

面向高压设备数字孪生的虚拟传感器设计与应用技术

针对高压设备数字孪生的模型构建、信息交互、动态仿真等需求,对虚拟传感器的设计与应用技术进行了分析,虚拟传感器是具有传感器行为的计算机仿真系统,能够基于功能、结构和服务要求对复杂数字孪生系统进行解耦,降低工程应用的复杂性。文中提出虚拟传感器在高压设备数字孪生工程应用上的设计要求,给出了观测虚拟传感器、参量计算虚拟传感器和预测告警传感器的设计方法及其应用方式。研究结果表明:虚拟传感器具备组件化、服务化、易重构、可进化等优点,可作为数字孪生单元级功能组件的设计手段,解决目前工程应用中数据获取和融合困难、仿真预测算法耦合复杂等问题,支撑设备状态全面感知、趋势精准预测、风险提前预警和设备全寿命精益管理等应用。

“传感器与测试技术”中的虚拟实验研究

面向飞行器设计与工程专业本科生,将“传感器与测试技术”课程与航空航天应用相结合,能够加深学生对传感器原理的理解、提高对测试技术的掌握,并促进理论和实践的联系。以飞行器结构无损检测的具体应用为基础,开展了结构虚拟检测实验平台的建立,探讨了虚拟实验在课程中运行和评测的方式,满足了学生动手操作实验的需求,实现了知识和应用的融合,促进了学生自主和探索式的学习。

基于虚拟传感器的坝区多输出自由场地震时程长序列预测模型研究

坝区自由场地震时程的多维长时序预测对于震害快速分析具有重要意义。虚拟传感器是地震物理传感器的补充感知手段,可实现地震时程的预测,然而现有虚拟传感器难以对多个信号做长时序预测,导致大坝震害分析较为滞后。针对上述问题,提出基于TFA-Seq2Seq虚拟传感器的坝区多输出自由场地震时程长序列预测模型。其中,基于多任务学习将Seq2Seq的虚拟传感器改进为“Encoder-3 Decoder”结构,以建立多个坝体物理传感器信号与自由场三个方向长时序地震时程的映射关系,并添加注意力机制捕获多个输入信号的时序依赖关系,以解决同步多输出预测问题及提升预测精度。进一步,引入可逆的时频变换层和其逆变换层改进编码器和解码器,以缩短地震信号的时域长度,提取频域特征,并提出对应的随机强制学习的模型训练策略,从而克服了现有虚拟传感器难以对长序列进行有效预测的缺陷。案例分析表明,该方法实现了坝区自由场三个方向地震信号的超前10 s虚拟感知,且相较于未添加注意力机制和单输出的模型,预测精度分别提高了6.88%和3.32%,研究为震时地震信息的超前感知提供了新思路和新途径。

无线地下传感网信道传播实验虚拟仿真与可视化教学

为帮助学生更好地理解和掌握无线地下传感网信道传播特性,推导并建立一个融合土壤特性参数的改进地下无线信道传播模型,在该模型基础上开发无线地下传感网实验虚拟仿真系统。该仿真系统不仅解决实验环境受限、难以开展实验教学的问题,且具有易于理解和操作的特点,仿真结果以图形化和可视化的形式呈现。通过此系统,学生可直观地观察和比较不同参数影响下地下无线信道传播变化情况,深入理解地下无线信道的复杂传播特性,提高学生的学习效率,激发学生的学习兴趣,促进学生学习的主动性和积极性。

基于Pixhawk飞控的硬件在环仿真系统设计

Pixhawk飞控在民用无人机(UAV)领域得到广泛应用。为了在Pixhawk飞控上实现硬件在环仿真功能以达到验证飞控飞行性能的目的,设计了基于Pixhawk飞控的四旋翼无人机硬件在环仿真系统。通过构建四旋翼机体模型,实现模型在电机转速信号的驱动下输出位姿数据;位姿数据传输到飞控并重构为模拟传感器数据,飞控内核对传感器数据处理后输出电机转速信号驱动模型飞行,由此构成飞控硬件在环的仿真环路。在测试中使用相同航线进行了仿真飞行与真实飞行,并计算2种飞行模式数据的相关系数,系数普遍高于0.85,表明获得了较为逼真的仿真效果。

基于虚拟传感器的结构位移视觉测量误差抑制方法

温度、热浪和振动等环境因素给结构位移监测造成不可忽视的误差。虽然利用静止标志物降低视觉测量误差的原理可行,但是其适用范围却因背景标志物不易获得和控制而受到很大限制。利用视觉测量系统的现有条件,在近场布置主动光源作为误差检测虚拟传感器,摆脱对天然标志物的依赖。针对焦外成像产生的弥散圆等伴生问题,采用图像滤波、伽马变换、图像质量评估、图像二值化、边缘检测和目标检测等算法,形成一套面向弥散圆光斑的自适应图像增强算法;利用虚拟传感器的虚假位移,实现结构位移测量误差补偿,开展了仿真实验和室外结构位移视觉测量实验。研究结果表明:该方法可以显著降低结构位移的初始误差,残留误差满足测量精度要求。