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This paper proposes two ways to improve pressure measurement in air-blast experimentations, mostly for close-in detonations defined by a small-scaled distance below 0.4 m.kg−1/3. Firstly, a new kind of custom-made pressure probe sensor is presented. The transducer is a piezoelectric commercial, but the tip material has been modified. The dynamic response of this prototype is established in terms of time and frequency responses, both in a laboratory environment, on a shock tube, and in free-field experiments. The experimental results show that the modified probe can meet the measurement requirements of high-frequency pressure signals. Secondly, this paper presents the initial results of a deconvolution method, using the pencil probe transfer function determination with a shock tube. We demonstrate the method on experimental results and draw conclusions and prospects. [ABSTRACT FROM AUTHOR]

This study investigated the dynamic characteristics of thermocouples by using double-pulse laser excitation for dynamic temperature calibration under extreme conditions. An experimental device was constructed for double-pulse laser calibration; the device uses a digital pulse delay trigger to precisely control the double-pulse laser to achieve sub-microsecond dual temperature excitation with adjustable time intervals. The time constants of thermocouples under single-pulse laser excitation and double-pulse laser excitation were evaluated. In addition, the variation trends of thermocouple time constants under different double-pulse laser time intervals were analyzed. The experimental results indicated that the time constant increases and then decreases with the decrease in the time interval of the double-pulse laser. A method for dynamic temperature calibration was established for the evaluation of the dynamic characteristics of temperature sensors. [ABSTRACT FROM AUTHOR]

At the current stage, there is an urgent need for new techniques to dynamically calibrate advanced wide-range (up to 104 N~105 N) triaxial force transducers. Based on this background, this paper proposes a novel impact calibration method, specifically for the triaxial force transducer, with a wide range and high-frequency response. In this method, the Hopkinson bar, which is typically used to test the dynamic mechanical properties of materials, was used as a generator to generate reference input force for the transducer. In addition, unlike conventional methods, the transverse sensitivities of the transducer were given necessary importance in the proposed method. The calibration result of the triaxial force transducer was expressed in a sensitivity matrix, containing three main sensitivity coefficients and six transverse sensitivity coefficients. The least squares method (LSM) was used to fit the sensitivity matrix linearly. Calibration experiments were performed on a typical triaxial force transducer. Several key issues, involving the validity and the test range, of the method were further investigated numerically. The feasibility and validity of the method were eventually confirmed. The test range of the method can be up to 106 N. [ABSTRACT FROM AUTHOR]

Generally, in order to ensure the reliability of Navigation system, vehicles are usually equipped with two or more sets of inertial navigation systems (INSs). Fusion of navigation measurement information from different sets of INSs can improve the accuracy of autonomous navigation effectively. However, due to the existence of misalignment angles, the coordinate axes of different systems are usually not in coincidence with each other absolutely, which would lead to serious problems when integrating the attitudes information. Therefore, it is necessary to precisely calibrate and compensate the misalignment angles between different systems. In this paper, a dynamic calibration method of misalignment angles between two systems was proposed. This method uses the speed and attitude information of two sets of INSs during the movement of the vehicle as measurements to dynamically calibrate the misalignment angles of two systems without additional information sources or other external measuring equipment, such as turntable. A mathematical model of misalignment angles between two INSs was established. The simulation experiment and the INSs vehicle experiments were conducted to verify the effectiveness of the method. The results show that the calibration accuracy of misalignment angles between the two sets of systems can reach to 1” while using the proposed method. [ABSTRACT FROM AUTHOR]

Dynamic calibration is one of the important ways to acquire the dynamic performance parameters of a pressure sensor. This research focuses on the processing method for the output of calibrated pressure sensor, and mainly attempts to solve the problem of extracting the true information of step response under strong interference noise. A dynamic calibration system based on a shock tube is established to excite the time-domain response signal of a calibrated pressure sensor. A key processing on difference modeling is applied for the obtained signal, and several generating sequences are established. A fusion process for the generating sequences is then undertaken, and the true information of the step response of the calibrated pressure sensor can be obtained. Finally, by implementing the common QR decomposition method to deal with the true information, a dynamic model characterizing the dynamic performance of the calibrated pressure sensor is established. A typical pressure sensor was used to perform calibration tests and a frequency-domain experiment for the sensor was also conducted. Results show that the proposed method could effectively filter strong interference noise in the output of the sensor and the corresponding dynamic model could effectively characterize the dynamic performance of the pressure sensor. [ABSTRACT FROM AUTHOR]

The aim in the current work is the development of a method to characterize force sensors under sinusoidal excitations using a primary standard as the source of traceability. During this work the influence factors have been studied and a method to minimise their contributions, as well as the corrections to be performed under dynamic conditions have been established. These results will allow the realization of an adequate characterization of force sensors under sinusoidal excitations, which will be essential for its further proper use under dynamic conditions. The traceability of the sensor characterization is based in the direct definition of force as mass multiplied by acceleration. To do so, the sensor is loaded with different calibrated loads and is maintained under different sinusoidal accelerations by means of a vibration shaker system that is able to generate accelerations up to 100 m/s2 with frequencies from 5 Hz up to 2400 Hz. The acceleration is measured by means of a laser vibrometer with traceability to the units of time and length. A multiple channel data acquisition system is also required to simultaneously acquire the electrical output signals of the involved instrument in real time. [ABSTRACT FROM AUTHOR]

Software which corrects the dynamic error of force transducers in impact force measurements using their own output signal has been developed. The software corrects the output waveform of the transducers using the output waveform itself, estimates its uncertainty and displays the results. In the experiment, the dynamic error of three transducers of the same model are evaluated using the Levitation Mass Method (LMM), in which the impact forces applied to the transducers are accurately determined as the inertial force of the moving part of the aerostatic linear bearing. The parameters for correcting the dynamic error are determined from the results of one set of impact measurements of one transducer. Then, the validity of the obtained parameters is evaluated using the results of the other sets of measurements of all the three transducers. The uncertainties in the uncorrected force and those in the corrected force are also estimated. If manufacturers determine the correction parameters for each model using the proposed method, and provide the software with the parameters corresponding to each model, then users can obtain the waveform corrected against dynamic error and its uncertainty. The present status and the future prospects of the developed software are discussed in this paper. [ABSTRACT FROM AUTHOR]

Dynamic calibration was performed in the laboratory on two catching-type drop counter rain gauges manufactured as high-sensitivity and fast response instruments by Ogawa Seiki Co. Ltd. (Japan) and the Chilbolton Rutherford Appleton Laboratory (UK). Adjustment procedures were developed to meet the recommendations of the World Meteorological Organization (WMO) for rainfall intensity measurements at the one-minute time resolution. A dynamic calibration curve was derived for each instrument to provide the drop volume variation as a function of the measured drop releasing frequency. The trueness of measurements was improved using a post-processing adjustment algorithm and made compatible with the WMO recommended maximum admissible error. The impact of dynamic calibration on the rainfall amount measured in the field at the annual and the event scale was calculated for instruments operating at two experimental sites. The rainfall climatology at the site is found to be crucial in determining the magnitude of the measurement bias, with a predominant overestimation at the low to intermediate rainfall intensity range. [ABSTRACT FROM AUTHOR]

The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified. [ABSTRACT FROM AUTHOR]

The fiber Bragg grating (FBG) sensor calibration process is critical for optimizing performance. Real-time dynamic calibration is essential to improve the measured accuracy of the sensor. In this paper, we present a dynamic calibration method for FBG sensor temperature measurement, utilizing the online sequential extreme learning machine (OS-ELM). During the measurement process, the calibration model is continuously updated instead of retrained, which can reduce tedious calculations and improve the predictive speed. Polynomial fitting, a back propagation (BP) network, and a radial basis function (RBF) network were compared, and the results showed the dynamic method not only had a better generalization performance but also had a faster learning process. The dynamic calibration enabled the real-time measured data of the FBG sensor to input calibration models as online learning samples continuously, and could solve the insufficient coverage problem of static calibration training samples, so as to improve the long-term stability, accuracy of prediction, and generalization ability of the FBG sensor. [ABSTRACT FROM AUTHOR]

Tip clearance flow in axial flow compressor is unavoidable and responsible for pressure losses and noise generation and influences the stability of the compressor. However, necessary flow measurement in the blade tip region is a great challenge due to the small gap width as well as the structure limitation. In this paper, a polyvinylidene fluoride (PVDF) piezoelectric-film sensor array is developed to capture the dynamic pressure field over the blade tip in an axial flow compressor. The PVDF sensor array with 40 evenly distributed sensing points is fabricated directly on a 30 μm thick aluminum-metalized polarized PVDF film through photolithography. Dynamic calibration of the sensor is accomplished using acoustic source as excitation and a microphone as a reference. The test pressure range is up to 3.5 kPa and the sampling frequency is 20 kHz. The sensor presents a high signal-to-noise ratio and good consistency with the reference microphone. Sensitivity, frequency response, linearity, hysteresis, repeatability as well as the influence of temperature are also investigated through the calibration apparatus. The calibration gives credence to the relevance and reliability of this sensor for the application in dynamic pressure field measurement. The sensor is then applied to an actual measurement in a compressor. The output of the PVDF sensor array is also compared with the results of common pressure transducers, and the features of the dynamic pressure filed are discussed. The results indicate that the PVDF sensor array is capable of the dynamic pressure field measurement over the blade tip, and superior to the conventional approaches in installation, spatial resolution, frequency response, and cost. These advantages indicate its potential broad application in pressure measurement, especially for the complex spatial surface or thin-walled structure, such as the blade surface and the thin casing wall of the compressor. [ABSTRACT FROM AUTHOR]

Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions. In this paper, we explore a novel robust subject-independent human emotion recognition system, which consists of two major models. The first one is an automatic feature calibration model and the second one is a classification model based on Cellular Neural Networks (CNN). The proposed system produces state-of-the-art results with an accuracy rate between 80 % and 89 % when using the same elicitation materials and physiological sensors brands for both training and testing and an accuracy rate of 71.05 % when the elicitation materials and physiological sensors brands used in training are different from those used in training. Here, the following physiological signals are involved: ECG (Electrocardiogram), EDA (Electrodermal activity) and ST (Skin-Temperature). [ABSTRACT FROM AUTHOR]