Fast computation of the spherical harmonic perturbation on an artificial satellite

Up till now, among the numerical computation methods of the spherical harmonic perturbation on an artificial satellite, the computation speed of Cunningham's method is the fastest However, running programs based on this method easily causes overflow. A method is presented, which has a computation speed higher than that of any other methods, and can avoid overflow in operation, even when the order and degree of the Earth's spherical harmonic perturbation are extended to 100 × 100 or more. It satisfies the requirements of the high-accuracy SLR, GPS, LLR, etc. This method has been programmed and used in practice Now a large-scale calculation related to the satellite precision ephemeris could be done just with a microcomputer.     

Dissipation of Turbulent Kinetic Energy near a Bubble Plume

Profiles of the rate of dissipation of turbulent kinetic energy were inferred from temperature microstructure measurements near a bubble plume at the center of a tank with diameter of 13.7 m and maximum depth of 8.3 m. Six sets of between 18 and 51 profiles were collected at airflow rates of 0.1–0.6 L/s, measured at atmospheric pressure, and ensemble-averaged dissipation profiles were calculated. The dissipation in all cases was between 10?8 and 10?6?m2/s3 in most of the profile, but it increased sharply near the water surface. Energy considerations are used to discuss the experimental results in terms of previous numerical models of bubble plume turbulence. Two previous numerical studies show that the turbulence dissipates between 15 and 30% of the available power. In the experiments, the fraction is less than 1% because some of the energy of the plume is used to generate waves on the water surface and the profiles used to compute the volume-averaged dissipation were relatively far from the bubble plume.     

Design and Hydraulics of a Combined Storm Overflow Structure

Storm overflow structures in combined sewers are used to separate sewage from storm water. For subcritical approach flow, sideweirs are currently used; however, they often behave hydraulically poor. This study was conducted to explore a combined storm overflow structure able to improve the hydraulic performance of sewer sideweirs. Such a structure consists of a low-crested sideweir and a bottom opening just downstream from the sideweir end. First, the hydraulic design procedure is described, and then, based on laboratory experiments and by using the governing flow equations, the main hydraulic features are highlighted. The results indicate that the proposed device has advantageous characteristics in terms of hydraulic efficiency, reliability, and maintenance.     

远程缓冲区溢出攻击的原理分析与检测

基于缓冲区溢出的攻击是目前使用相当普遍的一种黑客技术,该文分析了这种攻击的基本原理,在此基础上提出了利用NDIS开发包进行检测的一般方法,并且用实验证实了这种方法的有效性。     

Effects of tilt on high-resolution ADF-STEM imaging

A study of the effects of small-angle specimen tilt on high-resolution annular dark field images was carried out for scanning transmission electron microscopes with uncorrected and aberration-corrected probes using multislice simulations. The results indicate that even in the cases of specimen tilts of the order of 1 degree a factor of 2 reduction in the contrast of the high-resolution image should be expected. The effect holds for different orientations of the crystal. Calculations also indicate that as the tilted specimen gets thicker the contrast reduction increases. Images simulated with a low-angle annular dark field detector show that tilt effects are more pronounced in this case and suggest that these low-angle detectors can be used to correct specimen tilt during scanning transmission electron microscopes operation.     

Optical and thermal processes involved in ultrafast laser pulse interaction with a field emitter

In the interaction between ultrafast laser pulses and a field emitter both optical and thermal processes are involved. In this paper, these physical process, and their timescales, are experimentally explored. Simple models are proposed to explain the observed experimental behaviour, and the influence of various parameters are investigated. In the case of optical processes, it is shown that the optical field is greatly enhanced at the tip apex, and that field evaporation could be induced by an optical non-linear effect called optical rectification. In the case of thermal processes, it is shown that the temperature rise because of light absorption can be determined and that the cooling process of the tip surface can be studied by pump probe measurements.     

Improved single neuron controller for multivariable stochastic systems with non-Gaussianities and unmodeled dynamics

In this paper, a new adaptive control approach is presented for multivariate nonlinear non-Gaussian systems with unknown models. A more general and systematic statistical measure, called (h,?)-entropy, is adopted here to characterize the uncertainty of the considered systems. By using the “sliding window” technique, the non-parameter estimate of the (h,?)-entropy is formulated. Then, the improved neuron based controllers are developed for multivariate nonlinear non-Gaussian systems by minimizing the entropies of the tracking errors in closed loops. The condition to guarantee the strictly decreasing entropy of tracking error is presented. Moreover, the convergence in the mean-square sense has been analyzed for all the weights in the neural controllers. Finally, the comparative simulation results are presented to show that the performance of the proposed algorithm is superior to that of PID control strategy.     

H∞ filtering for a class of discrete-time singular Markovian jump systems with time-varying delays

The problem of _H∞ filtering for a class of discrete-time singular Markovian jump systems with time-varying delays is investigated in this paper. The transition probabilities under consideration are time-varying, i.e., Markovian chain is nonhomogeneous. By using the Lyapunov functional approach and reciprocally convex technique, a less conservative delay-dependent bounded real lemma is developed in terms of linear matrix inequalities. Moreover, a sufficient condition for the existence of the desired filter which guarantees the stochastic admissibility and the _H∞ performance index of the resulting filtering error system is presented. Numerical examples are employed to show the usefulness of the proposed results.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

小浴比溢喷染色机的结构特征

小浴比溢喷染色机的结构特征,主要包括主体染液独立循环系统,为加料提供一个稀释过程,可有效控制低浴比下敏感色的均匀上染.通过自动控制染色过程,可避免小浴比条件下出现的织物折痕、染色不匀等现象.