摆式加速度计零位偏离故障分析

针对悬丝支承摆式加速度计的输出零位偏离问题,通过故障树图进行了机理分析和试验验证,得出了输出零位偏离是由信号传感器对称线圈发生形变导致的结论,并提出了纠正预防措施。     

Wireless sensors as a tool to explore avalanche internal dynamics: Experiments at the Weissflühjoch Snow Chute

Specially designed wireless accelerometers units were used in a series of experiments at the snow chute operated by the SLF at Weissflühjoch (Switzerland) during 2008-2009 winter. The purpose of the experiment was to evaluate the best design and the performance of these innovative instruments to provide information on the internal dynamics of flowing snow. The wireless accelerometers were placed in the snow chute starting zone prior to the experiments and traveled within the flow when the avalanche was released. The characteristics of the units (size and density) allow them to evolve like active particle tracers. Acceleration measurements obtained at 85 Hz in the different experiments were analyzed. The analysis methods used include Empirical Mode Decomposition and Kalman filtering techniques. The developed methodologies were used to obtain reliable speed and position values from the single 2D acceleration measurements. The obtained results were compared to independent speed and position measurements. The results show to be in agreement with that obtained from independent speed measurements from optoelectronic sensor arrays and video images and open a new perspective for future avalanche research. The extracted information could provide valuable data related to internal dynamics of the avalanche. Small-scale chutes are the ideal scenario to test these new technologies. Moreover, we consider these sites essential to develop and test new instrumentation (to be deployed), in the future, in full-scale experiments. In addition, the experiments performed show for the first time the potential of the wireless technologies and wireless sensors to study snow avalanches.     

Computational study of the pressure-driven flow in a four-electrode rectangular micro-electrochemical accelerometer with an infinite aspect ratio

Numerical simulation is done on the pressure-driven flow in a four-electrode rectangular micro-electrochemical accelerometer with an infinite aspect ratio. The pressure gradient is proportional to the applied acceleration. Two kinds of electrode arrangements along the channel, the anode-cathode-cathode-anode (ACCA) and the cathode-anode-anode-cathode (CAAC) layouts, are studied. With the imposed electric potential difference between the electrodes, the electrode reaction, , occurs at the cathodes, with the reverse reaction occurring simultaneously at the anodes. For the given electrolyte, it is found that the cathodic current difference is two-order-of-magnitude larger than the anodic current difference and thus employed as the output electric current. The magnitude of the output electric current is found to be non-monotonous as the pressure gradient: when the pressure gradient is lower than the critical pressure gradient corresponding to the peak output electric current, the output electric current monotonously increases as the pressure gradient; when the pressure gradient is larger than the critical pressure gradient, the output electric current decreases as the pressure gradient and asymptotically approaches zero. The critical pressure gradient is found to be slightly larger for the ACCA layout. Due to the higher tri-iodide concentration compared to that found in the ACCA layout, when the pressure gradient is below the critical pressure gradients of both kinds of layouts, the CAAC layout generates larger output electric current.     

Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers

Although activity recognition is an emerging general area of research in computer science, its potential in construction engineering and management (CEM) domain has not yet been fully investigated. Due to the complex and dynamic nature of many construction and infrastructure projects, the ability to detect and classify key activities performed in the field by various equipment and human crew can improve the quality and reliability of project decision-making and control. In particular to simulation modeling, process-level knowledge obtained as a result of activity recognition can help verify and update the input parameters of simulation models. Such input parameters include but are not limited to activity durations and precedence, resource flows, and site layout. The goal of this research is to investigate the prospect of using built-in smartphone sensors as ubiquitous multi-modal data collection and transmission nodes in order to detect detailed construction equipment activities which can ultimately contribute to the process of simulation input modeling. A case study of front-end loader activity recognition is presented to describe the methodology for action recognition and evaluate the performance of the developed system. In the designed methodology, certain key features are extracted from the collected data using accelerometer and gyroscope sensors, and a subset of the extracted features is used to train supervised machine learning classifiers. In doing so, several important technical details such as selection of discriminating features to extract, sensitivity analysis of data segmentation window size, and choice of the classifier to be trained are investigated. It is shown that the choice of the level of detail (LoD) in describing equipment actions (classes) is an important factor with major impact on the classification performance. Results also indicate that although decreasing the number of classes generally improves the classification output, considering other factors such as actions to be combined as a single activity, methodologies to extract knowledge from classified activities, computational efficiency, and end use of the classification process may as well influence one’s decision in selecting an optimal LoD in describing equipment activities (classes).     

基于无线体域网的囚犯异常行为实时分析

随着无线传感技术的快速发展,无线体域网在远程医疗、智能家居等方面的应用日渐成为研究热点.监狱作为一个特殊场所,对囚犯的日常行为监控是必不可少的.准确而有效的监控系统能够在囚犯有异常行为发生时及时告警,这有助于监狱的管理,并阻止危险事故的发生.在监狱环境下,提出一种基于无线体域网的囚犯异常行为识别方法,即通过一个腕带式加速度传感器获取囚犯活动时的三轴加速度数据,采用分类算法判断是否有打架斗殴等异常行为发生.实验结果表明,该方法对异常行为的识别准确率能够达到95％.     

基于信号重构技术的FBG加速度测量系统研究

采用连续波调频,结合波分复用技术设计了基于小波滤波的光纤光栅加速度测量系统,提出压应力和拉应力同时实施的双光栅加速度测量探头的结构,选用其中心反射波长之差为传感信号,既消除了温度噪声对传感信号的干扰,又提高了测量的灵敏度和分辨率.应用小波信号处理方法,不仅实现了波长绝对编码,而且实现了微加速度信号的重构.所设计的测量系统具有结构简单、扫描频率高、分辨率好、线性度好等优点.     

加速度计参数辨识建模

根据加速度计参数模型辨识的需要,通过采集加速度计零偏,分析了数据的特点,结合最小二乘、递推最小二乘、最大似然估计3种系统辨识方法,建立起零偏数据相关的数学模型,再通过对模型进行仿真,观察参数变化曲线趋势,把模型参数和辨识之后的模型参数作对比,证明了模型的可靠性。     

数字线加速度传感器的研究

本文采用一种自行设计的压电谐振器进行线加速度传感器的研究,论述了该传感器的机理,结构及性能特点,对这种传感器进行了制作和实验。结果表明该数字线加速度传感器具有良好的线性和稳定性,测量范围±５０ｇ,非线性１％,灵敏度０．０１１６ｇ。且结构简单,成本低。     

基于加速度传感器的无线人体动作测量设备

使用ADXL345三轴加速度传感器、nRF24LO1无线收发芯片及USB接口芯片CY7C68013与上位机构建了一个人体动作信息测量设备。该设备由5组相同的动作测量模块、1组数据接收模块构成。动作测量模块置于人体四肢及躯干测量人体动作信息,数据接收模块通过无线方式收集加速度数据,并通过USB芯片上传给PC机,PC机计算...     

Head movements during two computer work tasks assessed by accelerometry

We investigated whether potential differences in head inclinations and accelerations for two highly similar computer work tasks could be detected using (1) a triaxial accelerometer and (2) a simulated uniaxial accelerometer.Ten subjects’ head movements were registered with a triaxial accelerometer system for two similar document-management tasks at their work place: a fully electronic document-management task and one also involving paper documents.In situations where head movements were small, a triaxial accelerometer was able to discriminate between the different degrees of static work of the neck in terms of range of head inclinations and accelerations. A difference in head acceleration was also found by using a simulated uniaxial accelerometer. Thus, in terms of head movement and for work similar to this office work, potential dynamic differences in observationally similar work tasks can be investigated by using a triaxial accelerometer. For acceleration alone, a uniaxial accelerometer can also be used.