Energy-dispersive X-ray fluorescence (EDXRF) spectrometry is a nondestructive, rapid, multielement, highly accurate, and environment friendly analysis compared with other elemental detection methods. Thus, EDXRF spectrometry is applicable for production quality control, ecological environment monitoring, geological surveying, food inspection, and heritage analysis, among others. A hardware platform for the EDXRF spectrometer is designed in this study based on the theoretical analysis of energy-dispersive X-ray. The platform includes a power supply subsystem, an optical subsystem, a control subsystem, and a personal computer. A fluorescence spectrum analytical method is then developed to obtain the category and content of elements in a sample. This method includes qualitative and quantitative analyses. Finally, a series of experiments is performed. Results show that the precision of the proposed measurement method is below 8%, whereas its repeatability is below 2%.

SUMMARY: An unmanned aerial vehicle (UAV) dynamic path planning method is proposed to avoid not only static threats but also mobile threats. The path of a UAV is planned or modified by the potential trajectory of the mobile threat, which is predicted by its current position, velocity, and direction angle, because the positions of the UAV and mobile threat are dynamically changing. In each UAV planning path, the UAV incurs some costs, including control costs to change the direction angle, route costs to bypass the threats, and threat costs to acquire some probability to be destroyed by threats. The model predictive control (MPC) algorithm is used to determine the optimal or sub-optimal path with minimum overall costs. The MPC algorithm is a rolling-optimization feedback algorithm. It is used to plan the UAV path in several steps online instead of one-time offline to avoid sudden and mobile threats dynamically. Lastly, solution implementation is described along with several simulation results that demonstrate the effectiveness of the proposed method.

The powered parafoil system is obtained by adding the propeller thrust to the unpowered parafoil system, and has coupling and nonlinear characteristics, which make its precise control more difficult than that of the unpowered parafoil system. To achieve the trajectory tracking control of the powered parafoil system in the field of precision airdrop, a mathematical model of the 6-DOF of the powered parafoil system is established first. Then, a new trajectory tracking strategy is proposed, which can overcome the limitations of the traditional guidance-based trajectory tracking strategy. We design lateral, longitudinal, and velocity controllers on the basis of the motion characteristics of the powered parafoil system. Then, we adopt the widely used PID control strategy in engineering. In response to the difficult of the PID controller parameter tuning of the powered parafoil system, we achieve the PID controller parameter tuning with the ecosystem particle swarm optimization (ESPSO) of the swarm intelligence optimization algorithm. The effectiveness of the algorithm is verified by simulation experiments. Results show that the proposed algorithm can obtain high trajectory tracking accuracy even when a deviation in the initial state and a random gust wind disturbance in the outside world occur regardless of the method of the multiphase homing or the optimal control homing adopted. The proposed trajectory tracking strategy also has strong robustness and adaptability.

Measurement of flame or combustion is an important method to estimate combustion quality, study the performance of engine and develop renewable energy sources. This paper presents a method to detect the combustion flow field in a confined space using Positron Emission Tomography (PET) technology. Radionuclides are labelled in aviation kerosene, and the nuclides are distributed in the confined space along with the kerosene in the process of combustion. γ photons generated by the decay of the nuclides in the combustion region are detected by the detectors around the object. Each pair of γ photons with 511keV travelling in opposite directions within a short time window are detected as a line of response (LOR). The distributions of nuclide concentrations in the combustion region, which are related to the combustion flow field, are estimated through the counts of LORs at different angles and positions. In order to display the combustion flow field based on human visual features, ray casting algorithm is used to reconstruct the 3D image with the slice images got from PET. Due to the complexity of ray casting algorithm, the interpolation algorithm in ray casting algorithm is optimized. The experiment results show that the image construction speed is improved with the optimized ray casting algorithm. The constructed 3D image can display the combustion flow field from different viewpoints.

This paper addresses the problem of risk in adversarial environments and presents a new attack model in which the cooperative action is considered. The actions of each UAV not only get benefit from attacking targets but also reduce the risk in the environment for other UAVs. The implications of the survival probabilities and the time discount factor in this model are analyzed and the results will help to build the optimal attack strategy. An improved genetic algorithm is applied to quickly build the optimal strategy. Simulation results show that this algorithm provides better solutions than genetic algorithm. © 2013 Copyright TSI® Press.