Driving Speed Estimation and Trapped Drivers’ Detection inside Tunnels Using Distributed MIMO Bluetooth Devices

Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.

Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network Structure

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer.

Internal solitary waves propagation speed estimation in the northern-part of Lombok Strait observed by Sentinel-1 SAR and Himawari-8 images

Remotely sensed data, both Synthetic Aperture Radar (SAR) and optical sensors, significantly contribute to the study and understanding internal solitary wave (ISW) dynamics in the ocean. Pairs of SAR and optical sensors were analyzed to estimate the ISW propagation speed in the northern-part of Lombok Strait. ISW propagation speed estimation used an image from Sentinel-1 SAR and three image pairs of Himawari-8 on 29 October 2018 with a time difference of 409 minutes. Sentinel-1 wide-swath imagery (250 km x 400 km) from two adjacent scenes can provide information on multiple ISW packets evolution in the northern-part of Lombok Strait. ISW propagation speed estimation on Sentinel-1 SAR image using the simple estimation by measuring the interpacket distance and dividing by the semidiurnal tidal period. The high temporal resolution of the optical sensor from Himawari-8 can estimate the ISW propagation speed using two different approaches. ISW propagation speed estimation using the semidiurnal tidal period from Sentinel-1 and Himawari-8 showed almost similar values. Sentinel-1 estimation results are 2.69 m.s−1 (Lombok Strait) and 1.30 m.s−1 (northern-part area), Himawari-8 results are 2.52 m.s−1 (Lombok Strait) and 1.27 m.s−1 (northern-part area). ISW propagation speed variability in the northern-part of the Lombok Strait shown in this study.

Research on a PMSM control strategy for electric vehicles

This paper proposes a novel state estimation based permanent magnet synchronous motor (PMSM) control method for electric vehicle (EV) driving. Firstly, a state feedback decoupling control with disturbance feed-forward (SFDCDF) is described. As motor angular speed and rotary angle are key information for the proposed control algorithm and park’s transformation, a novel observer based angular speed estimator (OBASE) is proposed for angular speed estimation. Moreover, an extended Kalman filter (EKF) based rotary angle estimator (EBRAE) is used for rotary angle estimation with information of the estimated angular speed. The convergence of angular speed estimation is proven through Lyapunov stability theory. Simulation results also indicate that the proposed algorithms can control PMSM torque, current, and angular speed to accurately follow reference values without severe fluctuation. In addition, in order to provide SFDCDF with load torque information, the OBASE is slightly modified to work as a vehicle load estimator (VLE) so PMSM responds more rapidly and speed fluctuates more slightly when the load suddenly changes. Then a series of hardware in the loop (HIL) simulations are carried out. Results indicate that the proposed control strategy can precisely estimate PMSM’s angular speed and rotor angle. Also, it can improve the driving performance of PMSM used on EVs.

Comparative Analysis of Two Speed-Estimation Methods for Dual Three-Phase Induction Motor with Stator Resistance Online Identification

The dual three-phase induction motor (DTPIM) has gained wide attention in special applications, such as vessel propulsion, because of its advantages of less torque ripple and higher reliability. However, speed sensors are greatly affected and easily become faulty when used in harsh environments for a long time. In this paper, two model reference adaptive system (MRAS) speed-estimation methods are proposed, based on the double (α, β) coordinate system (DCS) and vector space decomposition method (VSD) of the two groups of the three-phase armature vectors, respectively. Both methods can be used for the speed sensorless control system of the DTPIM to improve reliability. The changing of the stator resistance value, caused by temperature variation, affects the accuracy of the speed-estimation. Two online resistance-identification algorithms, combining the DCS method and the VSD method, were proposed to reduce the effect of changes in stator resistance. Simulation results show that the dynamic speed-estimation error of the VSD method decreased greatly compared with the DCS method, which verifies the effectiveness of the theoretical analysis.