Timing sequence changes of landscape plants in Shanghai Area,China

Based on published reports and direct observation, the common resources of landscape plant species in the Shanghai area were identified and studied in terms of the landscape characteristics of timing sequence changes over a one year time period. The results showed that there were 199 common landscape plant species in Shanghai, distributed in 67 families and 129 genera. These plants were categorized by three ornamental characteristics: flowers, foliage and fruits. The species were thus further analyzed according to the time periods which these characteristics were present. The results showed that flower ornamental distributed throughout the year, particularly from April to June, and plant species with colorful foliage could be seen every month, and particularly in November and December. On the other hand, few plant species with fruit ornamentals were observed in Shanghai area. The diversity of landscape timing sequence changes of the 199 plant species under examination was then analyzed by using the Shannon-Weiner and Simpson index formulas. The results showed that the higher diversity was observed in January, February and from August to October, while the lowest diversity was recorded from March to July and during November and December. The diversity of landscape characteristics of these plants was ranked according to foliage, flowers and fruit. Using this method, one park and one plant community were selected to evaluate the advantages and disadvantages of their time-changing plant landscape, with the goal of assessing the validity of the chosen ranking method. These results in the present study would be helpful to evaluate the plant landscape in a specified space and further enrich the diversity of plant and landscape by a target-oriented way.     

Study on launch dynamics of the tank marching fire

As an army main battle equipment, it is required that the tank should have high firing accuracy and high first round hit probability during marching. The initial disturbance of the projectile is the premier factor that takes effect on the marching fire accuracy of the tank. And the marching fire accuracy of the tank depends on the launch dynamics behaviors of the tank. In this paper, the launch dynamics theory of a tank marching fire is studied, and its launch dynamics model is established. Based on the transfer matrix method for multibody system(MSTMM) and the automatic deduction theorem of overall transfer equations, the overall transfer equation and the overall transfer matrix of a tank multibody system are deduced; the launch dynamics equations of the tank marching fire are deduced, and the dynamic response of the tank system, the motion of projectile in barrel, the initial disturbance of the projectile and the vertical target dispersion are exactly simulated; meanwhile, the results of simulation are verified by tests. This work provides both theoretical foundation and simulation approaches for improving the marching fire accuracy of the tank.     

隧道下穿超浅埋国道的爆破减振技术

隧道下穿交通量大、超浅埋的国道公路时,为了能安全穿越,确保行车畅通,围岩开挖采用了拱上部周边打空炮眼减振、开挖面增打减振孔、炮孔复合装药、预留光爆层等综合减振措施的爆破技术。经现场爆破监测,爆破振动波速基本控制在20mm/s之内,降低了爆破的振波,有效地控制了公路地表下沉,确保了公路运营的安全。     

Geometric calibration algorithm of polarization camera using planar patterns

Polarization pattern provides additional information besides spectral signatures. It can be used in many applications, such as navigation, defect detection and object identification. A novel polarization camera composed of four synchronized cameras is proposed, and it can realize real-time polarization measurement. This study particularly concentrates on the geometric calibration of the system. The projection model is analyzed and the multi-camera calibration algorithm is proposed. Firstly, each camera is calibrated separately using planar patterns, and then the geometric calibration algorithms are performed. Due to the geometrical constraint, a global optimization method results in smaller estimation uncertainties. A mean rotation error of 0.025° and a mean translation error of 0.26mm are achieved after geometric calibration. The images are rectified to establish a correspondence among cameras and are combined to acquire the polarization measurement. The polarization pattern of the skylight is measured by the system and the results are consistent with the previous studies.     

Research of legal affair simulation based on Petri net

Legal affair simulation is on the basis of information technology, and can simulate legal scene. College students do legal experiments with virtual reality scene. This helps to train college students. Petri net has not only strict math definition to analyze the dynamic behavior of models, but also intuitive graphic expression. Petri net is used to model for legal process. Reduction rules of Petri net are used to analyze the correctness of the system. At last Java Business Process Management (JBPM) technology is used to realize legal affair simulation models, models are transformed into flow charts. Simulation software deduces processes of legal affair. Many college students have improved their ability with the software, the fact proves that the method is effective.     

Magnetic field tuning characteristics of bimodal ultrasonic motor stator

The magnetic field tuning characteristics of an ultrasonic motor (USM) stator are discussed. The stator consists of two piezoelectric ceramic transducer (PZT) plates and one sandwiched-in Terfenol-D plate. The dimensions of the stator are carefully adjusted to specifically discuss the influence of the magnetic field on the frequency difference between the longitudinal and bending modes of the stator. The frequency difference discussed in this paper is usually small and mainly caused by uneven materials, machining errors and changes in external conditions (temperature, pre-stress or load). The longitudinal and bending modes of the stator are simultaneously excited by an external electric field to generate the elliptic motion trajectories of the driving points. A direct current (DC) magnetic field is applied to decrease the difference between the two mode frequencies of the fabricated stator. In experiments, the dependences of the two mode frequencies and their difference on DC magnetic fields are all investigated. The experimental results indicate that the difference between the longitudinal and bending mode frequencies of the PZT/Terfenol-D/PZT composite stator can be tuned by changing the intensity of the external DC magnetic field.     

Sensorimotor self-learning model based on operant conditioning for two-wheeled robot

Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment.     

Compensation of pressure enthalpy effects on temperature fields for throttling of high-pressure real gas

For the pressure enthalpy of high pressure pneumatics, the computational fluid dynamics (CFD) simulation based on ideal gas assumption fails to obtain the real temperature information. Therefore, we propose a method to compensate the pressure enthalpy of throttling for CFD simulation based on ideal gas assumption. Firstly, the pressure enthalpy is calculated for the pressure range of 0.101 to 30 MPa and the temperature range of 190 to 298 K based on Soave-Redlich-Kwong (S-R-K) equation. Then, a polynomial fitting equation is applied to practical application in the above mentioned range. The basic idea of the compensation method is to convert the pressure enthalpy difference between inlet air and nodes into the compensation temperature. In the above temperature and pressure range, the compensated temperature is close to the real one, and the relative temperature drop error is below 10%. This error is mainly caused by the velocity difference of the orifice between the real and ideal gas models. Finally, this compensation method performs an icing analysis for practical high pressure slide pilot valve.     

Approximate method for reliability assessment of complex phased mission systems

Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.