ALBERT

Description: Oil-water two-phase flow is widespread in petroleum industry processes. The study of oil-water two-phase flow in horizontal pipes and the liquid holdup measurement of oil-water two-phase flow are of great importance for the optimization of the oil production process. This paper presents a novel sensor, i.e., a mini-conductance probe (MCP) for measuring pure-water phase conductivity of oil-water segregated flow in horizontal pipes. The MCP solves the difficult problem of obtaining the pure-water correction for water holdup measurements by using a ring-shaped conductivity water-cut meter (RSCWCM). Firstly, using the finite element method (FEM), the spatial sensitivity field of the MCP is investigated and the optimized MCP geometry structure is determined in terms of the characteristic parameters. Then, the responses of the MCP for the oil-water segregated flow are calculated, and it is found that the MCP has better stability and sensitivity to the variation of water-layer thickness in the condition of high water holdup and low flow velocity. Finally, the static experiments for the oil-water segregated flow were carried out and a novel calibration method for pure-water phase conductivity measurements was presented. The validity of the pure-water phase conductivity measurement with segregated flow in horizontal pipes was verified by experimental results.

Description: Pyroelectric harvesters convert time-dependent temperature variations into electric current. The appropriate geometry of the pyroelectric cells, coupled with the optimal period of temperature fluctuations, is key to driving the optimal load resistance, which enhances the performance of pyroelectric harvesters. The induced charge increases when the thickness of the pyroelectric cells decreases. Moreover, the induced charge is extremely reduced for the thinner pyroelectric cell when not used for the optimal period. The maximum harvested power is achieved when a 100 μm-thick PZT (Lead zirconate titanate) cell is used to drive the optimal load resistance of about 40 MΩ. Moreover, the harvested power is greatly reduced when the working resistance diverges even slightly from the optimal load resistance. The stored voltage generated from the 75 μm-thick PZT cell is less than that from the 400 μm-thick PZT cell for a period longer than 64 s. Although the thinner PZT cell is advantageous in that it enhances the efficiency of the pyroelectric harvester, the much thinner 75 μm-thick PZT cell and the divergence from the optimal period further diminish the performance of the pyroelectric cell. Therefore, the designers of pyroelectric harvesters need to consider the coupling effect between the geometry of the pyroelectric cells and the optimal period of temperature fluctuations to drive the optimal load resistance.

Description: Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

Description: The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

Description: Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.

Description: The online and continuous measurement of velocity, concentration and mass flow rate of pneumatically conveyed solid particles for the high-efficiency utilization of energy and raw materials has become increasingly significant. In this paper, an integrated instrumentation system for the velocity, concentration and mass flow rate measurement of dense phase pneumatically conveyed solid particles based on electrostatic and capacitance sensorsis developed. The electrostatic sensors are used for particle mean velocity measurement in combination with the cross-correlation technique, while the capacitance sensor with helical surface-plate electrodes, which has relatively homogeneous sensitivity distribution, is employed for the measurement of particle concentration and its capacitance is measured by an electrostatic-immune AC-based circuit. The solid mass flow rate can be further calculated from the measured velocity and concentration. The developed instrumentation system for velocity and concentration measurement is verified and calibrated on a pulley rig and through static experiments, respectively. Finally the system is evaluated with glass beads on a gravity-fed rig. The experimental results demonstrate that the system is capable of the accurate solid mass flow rate measurement, and the relative error is within −3%–8% for glass bead mass flow rates ranging from 0.13 kg/s to 0.9 kg/s.

Description: An on-chip optical humidity sensor using Nano-anatase TiO2 coating is presented here. The coating material was prepared so that the result is in solution form, making the fabrication process quick and simple. Then, the solution was effortlessly spin-coated on an SU8 straight channel waveguide. Investigating the sensitivity and performance (response time) of the device revealed a great linearity in the wide range (35% to 98%) of relative humidity (RH). In addition, a variation of more than 14 dB in transmitted optical power was observed, with a response time of only ~0.7 s. The effect of coating concentration and UV treatment was examined on the performance and repeatability of the sensor. Interesting observations were found, and the attributed mechanisms were described. In addition, the proposed sensor was extensively compared with other state-of-the-art proposed counterparts from the literature and remarkable advantages were found. Since a high sensitivity of ~0.21 dB/%RH and high dynamic performances were demonstrated, this sensor is proposed for use in biomedical applications.

Description: To solve the problem of tracking maneuvering airborne targets in the presence of clutter, an improved interacting multiple model probability data association algorithm (IMMPDA-MDCM) using radar/IR sensors fusion is proposed. Under the architecture of the proposed algorithm, the radar/IR centralized fusion tracking scheme of IMMPDA-MDCM is designed to guarantee the observability of the target state. The interacting multiple model (IMM) deals with the model switching. The modified debiased converted measurement (MDCM) filter accounts for non-linearity in the dynamic system models, and reduces the effect of measurement noise on the covariance effectively. The probability data association (PDA) handles data association and measurement uncertainties in clutter. The simulation results show that the proposed algorithm can improve the tracking precision for maneuvering target in clutters, and has higher tracking precision than the traditional IMMPDA based on EKF and IMMPDA based on DCM algorithm.

Description: We present in this paper a novel fully decoupled silicon micromachined tri-axis linear vibratory gyroscope. The proposed gyroscope structure is highly symmetrical and can be limited to an area of about 8.5 mm × 8.5 mm. It can differentially detect three axes’ angular velocities at the same time. By elaborately arranging different beams, anchors and sensing frames, the drive and sense modes are fully decoupled from each other. Moreover, the quadrature error correction and frequency tuning functions are taken into consideration in the structure design for all the sense modes. Since there exists an unwanted in-plane rotational mode, theoretical analysis is implemented to eliminate it. To accelerate the mode matching process, the particle swam optimization (PSO) algorithm is adopted and a frequency split of 149 Hz is first achieved by this method. Then, after two steps of manual adjustment of the springs’ dimensions, the frequency gap is further decreased to 3 Hz. With the help of the finite element method (FEM) software ANSYS, the natural frequencies of drive, yaw, and pitch/roll modes are found to be 14,017 Hz, 14,018 Hz and 14,020 Hz, respectively. The cross-axis effect and scale factor of each mode are also simulated. All the simulation results are in good accordance with the theoretical analysis, which means the design is effective and worthy of further investigation on the integration of tri-axis accelerometers on the same single chip to form an inertial measurement unit.

Description: This paper presents a novel approach for estimating the ego-motion of a vehicle in dynamic and unknown environments using tightly-coupled inertial and visual sensors. To improve the accuracy and robustness, we exploit the combination of point and line features to aid navigation. The mathematical framework is based on trifocal geometry among image triplets, which is simple and unified for point and line features. For the fusion algorithm design, we employ the Extended Kalman Filter (EKF) for error state prediction and covariance propagation, and the Sigma Point Kalman Filter (SPKF) for robust measurement updating in the presence of high nonlinearities. The outdoor and indoor experiments show that the combination of point and line features improves the estimation accuracy and robustness compared to the algorithm using point features alone.