Sensors2015, 15(9), 21931-21956; doi:10.3390/s150921931 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing.
Sensors2015, 15(9), 21898-21930; doi:10.3390/s150921898 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: Human age can be employed in many useful real-life applications, such as customer service systems, automatic vending machines, entertainment, etc. In order to obtain age information, image-based age estimation systems have been developed using information from the human face. However, limitations exist for current age estimation systems because of the various factors of camera motion and optical blurring, facial expressions, gender, etc. Motion blurring can usually be presented on face images by the movement of the camera sensor and/or the movement of the face during image acquisition. Therefore, the facial feature in captured images can be transformed according to the amount of motion, which causes performance degradation of age estimation systems. In this paper, the problem caused by motion blurring is addressed and its solution is proposed in order to make age estimation systems robust to the effects of motion blurring. Experiment results show that our method is more efficient for enhancing age estimation performance compared with systems that do not employ our method.
Sensors2015, 15(9), 21876-21897; doi:10.3390/s150921876 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: Harmonic vibrations of high-speed rotors in momentum exchange devices are primary disturbances for attitude control of spacecraft. Active magnetic bearings (AMBs), offering the ability to control the AMB-rotor dynamic behaviors, are preferred in high-precision and micro-vibration applications, such as high-solution Earth observation satellites. However, undesirable harmonic displacements, currents, and vibrations also occur in the AMB-rotor system owing to the mixed rotor imbalances and sensor runout. To compensate the rotor imbalances and to suppress the harmonic vibrations, two control methods are presented. Firstly, a four degrees-of-freedom AMB-rotor model with the static imbalance, dynamic imbalance, and the sensor runout are described. Next, a synchronous current reduction approach with a variable-phase notch feedback is proposed, so that the rotor imbalances can be identified on-line through the analysis of the synchronous displacement relationships of the geometric, inertial, and rotational axes of the rotor. Then, the identified rotor imbalances, which can be represented at two prescribed balancing planes of the rotor, are compensated by discrete add-on weights whose masses are calculated in the vector form. Finally, a repetitive control algorithm is utilized to suppress the residual harmonic vibrations. The proposed field balancing and harmonic vibration suppression strategies are verified by simulations and experiments performed on a control moment gyro test rig with a rigid AMB-rotor system. Compared with existing methods, the proposed strategies do not require trial weights or an accurate model of the AMB-rotor system. Moreover, the harmonic displacements, currents, and vibrations can be well-attenuated simultaneously.
Sensors2015, 15(9), 21857-21875; doi:10.3390/s150921857 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV).
Sensors2015, 15(9), 21844-21856; doi:10.3390/s150921844 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: Pressure measurements in high-temperature applications, including compressors, turbines, and others, have become increasingly critical. This paper proposes an implantable passive LC pressure sensor based on an alumina ceramic material for in situ pressure sensing in high-temperature environments. The inductance and capacitance elements of the sensor were designed independently and separated by a thermally insulating material, which is conducive to reducing the influence of the temperature on the inductance element and improving the quality factor of the sensor. In addition, the sensor was fabricated using thick film integrated technology from high-temperature materials that ensure stable operation of the sensor in high-temperature environments. Experimental results showed that the sensor accurately monitored pressures from 0 bar to 2 bar at temperatures up to 800 °C. The sensitivity, linearity, repeatability error, and hysteretic error of the sensor were 0.225 MHz/bar, 95.3%, 5.5%, and 6.2%, respectively.
Sensors2015, 15(9), 21824-21843; doi:10.3390/s150921824 (registering DOI) - published 31 August 2015 Show/Hide Abstract
Abstract: The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy.