Scale-aware navigation of a low-cost quadrocopter with a monocular camera

We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter’s flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.     

The architecture of an information tool for de-mining: mine identification core module (MICM)

In this work the architecture of a mine identification core module (MICM) system is presented. This system focuses on the objects to be identified rather than the sensor technology, where most R&D efforts are concentrated. Fusing sensorial with accumulated data, it could be the base of a sensor independent decision support system forming the common core of the future multi-sensorial landmine detection/identification systems.The system is based on a landmine CAD database, a database of the different sensor types and a constantly updated false alarm database that exchange information through layers constructed using Object Oriented programming techniques. The overall structure of the tool is based on the agent/object unit concept, which represents the participating entities and their interactions. The action sequences of the de-mining operations are formed with the same technique, giving to the system the characteristics of openness, versatility and reusability.     

A piezoelectric driven ratchet actuator mechanism with application to automotive engine valves

Increasing demands on the performance of internal combustion engines, specifically automobile engines, require the production of more mechanical energy for a given amount of chemical fuel with reduced tailpipe emissions. Of particular interest in the development of high performance/low emission engines is the control and timing of individual engine valves. This paper investigates the design of an innovative piezoelectric ceramic (PZT) based actuator mechanism with a novel stepping motion amplifier to deliver force and displacement at higher magnitudes and operating frequencies. The target application is an engine valve train which traditionally uses cam-based lifter driven rocker arms to regulate the cylinders’ intake and exhaust valve motion. The proposed PZT-based actuator mechanism introduces a high frequency, lightweight, precise position solution for cylinder-by-cylinder variable valve timing. Numerical results are provided to demonstrate the feasibility of a PZT-based/camless valve train. The new valve train demonstrates similar cam-based performance characteristics while enabling computer controlled individual valve timing opportunities.     

Evidence combination rule with contrary support in the evidential reasoning approach

Three aspects of problems such as reasonable weight constraint, cumulative weight effect and relative weight equivalence cannot be well reflected in the evidential reasoning (ER) approach. In order to solve the above problems, a contrary support is defined to restrict the degree influenced by the evidence to be combined in combination process. Then pair-weighted and cumulative pair-weighted discounting methods are presented to generate basic probability assignment (BPA) for evidence. Pair-wised and recursive combination rules are established to make combination with the BPA of evidence by orthogonal sum operation and several theorems such as relative weight equivalence are proved. A combination algorithm is proposed to solve multiple criteria decision making (MCDM) problems by integrating pignistic probability and expected utility with the established combination rules. Finally, an illustrative example is provided to demonstrate the applicability of the proposed combination rules and algorithm.     

Mechanism and control of the long-term performance evolution of structures

It is well known that structural properties degrade under long-term environmental exposure and loading and that the degradation rate is controlled by inherent physical and chemical degradation mechanisms. The elucidation of the degradation mechanisms and the realization of effective long-term performance degradation control have been a research frontier in the field of civil engineering in recent years. Currently, the major topics that concern this research frontier include revealing the physical and chemical mechanisms of structural performance evolution under long-term environmental exposure and loading and developing structural performance degradation control technologies based on fiber-reinforced materials, for example, fiber-reinforced polymers (FRPs) and fabric-reinforced cementitious matrix (FRCM). In addition, there are novel structural performance control technologies, such as using a shape memory alloy (SMA) and self-healing concrete. This paper presents a brief state-of-the-art review of this topic, and it is expected to provide a reference for subsequent research.     

Computer vision-based approach for smart traffic condition assessment at the railroad grade crossing

Slow-moving or stopped trains at highway-railroad grade crossings, especially in the populated metropolitan areas, not only cause significant traffic delays to commuters, but also prevent first responders from timely responding to emergencies. In this study, the researchers introduce an automated video analysis, detection and tracking system to evaluate the traffic conditions, analyze blocked vehicle behaviors at grade crossings, and predict the decongestion time under a simplified scenario. A novel YOLOv3-SPP+ model has been developed to improve the detection performance with dividing the image from finer to coarser levels and enhance local features. The SORT module has been integrated to the model for a simple yet efficient manner to track vehicles at the railroad grade crossing. Two field datasets at the Columbia, SC, with train blockage video records have been tested. The model training performance has been evaluated by mAP @0.5, F1 score, and total loss. Based on the training results, our model outperforms other YOLO series models. The field tracking performance has been assessed by the ratio between prediction and ground truth. The mean value of accuracy of our test cases is 92.37%, indicating a reliable tracking performance. In addition, the present results indicate the traffic during and after the crossing blockage does follow a pattern, and there is a general trend of the behavior of the vehicles waiting or taking an alternative route. A good linear correlation between the decongestion time and the number of blocked vehicles has been observed at the monitored grade crossing at the City of Columbia, SC.     

A round-robin test of thermal desorption spectroscopy with quadrupole mass spectrometers calibrated by standard conductance element

The results of a round-robin test of thermal desorption spectroscopy (TDS) in which the quadrupole mass spectrometer of each participant was calibrated in-situ by a standard conductance element are reported. The desorption rate from three commercially available reference materials was quantified from the calibration results, and compared among the three participants. The total reported desorption amounts of hydrogen and argon molecules from the reference materials showed agreement within about 10%. The degree of equivalence of three TDS apparatuses was discussed. It is also shown that the quantification of the gas desorption amounts yields us the appropriate relative ratio between desorbed gas species.     

基于物联网和模糊聚类的风力发电设备故障诊断系统及方法

为了提高风力发电设备故障诊断的准确性,构建了基于物联网和模糊聚类的风力发电设备的故障诊断系统。首先提出了风力发电设备故障诊断系统的拓扑结构,并介绍了其涉及的关键技术;然后给出了该系统的所采用的数据分析和故障诊断方法,并进行了应用实例分析。结果表明：该风力设备故障诊断系统可监控风力发电设备的运行状态,并能准确地诊断出风力发电设备的故障类型。     

Sensorless induction spindle motor drive using fuzzy neural network speed controller

A sensorless induction spindle motor drive using synchronous PWM and dead-time compensator with fuzzy neural network (FNN) speed controller is proposed in this study for advanced spindle motor applications. First, the operating principles of a new type synchronous PWM technique are described in detail. Then, a speed observer based on the model reference adaptive system (MRAS) theory is adopted to estimate the rotor speed. To increase the accuracy of the estimated speed, the speed estimation algorithm is implemented using a digital signal processor. Moreover, since the control characteristics and motor parameters for high speed operated induction spindle motor drive are time-varying, an FNN speed controller is developed to reduce the influence of parameter uncertainties and external disturbances. In addition, the FNN is trained on-line using a delta adaptation law. Finally, the performance of the proposed sensorless induction spindle motor drive system is demonstrated using some simulation and experimental results.