安全气囊用涤纶工业丝纺丝工艺与设备研究

探讨了安全气囊用纤维性能的特点,重点研究了安全气囊用细旦涤纶工业丝关键纺程的纺丝设备及工艺参数,分析了纺丝工艺对纤维特性的影响,为指导实际生产提供理论依据。

A genetic algorithm for community detection in complex networks

A new genetic algorithm for community detection in complex networks was proposed. It adopts matrix encoding that enables traditional crossover between individuals. Initial populations are generated using nodes similarity, which enhances the diversity of initial individuals while retaining an acceptable level of accuracy, and improves the efficiency of optimal solution search. Individual crossover is based on the quality of individuals' genes; all nodes unassigned to any community are grouped into a new community, while ambiguously placed nodes are assigned to the community to which most of their neighbors belong. Individual mutation, which splits a gene into two new genes or randomly fuses it into other genes, is non-uniform. The simplicity and effectiveness of the algorithm are revealed in experimental tests using artificial random networks and real networks. The accuracy of the algorithm is superior to that of some classic algorithms, and is comparable to that of some recent high-precision algorithms.

Adaptive neural network based sliding mode altitude control for a quadrotor UAV

Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.

Energy-efficient Traffic Grooming Under Sliding Scheduled Traffic Model for IP over WDM Optical Networks

In this paper,we investigate on the problem of energy-efficient traffic grooming under sliding scheduled traffic model for IP over WDM optical networks,so as to minimize the total energy consumption of the core network.We present a two-layer auxiliary graph model and propose a new energyefficient traffic grooming heuristic named Two-Dimension Green Traffic Grooming(TDGTG) algorithm,which takes both space and time factors into consideration for network energy efficiency.We compare our proposed TDGTG algorithm with the previous traffic grooming algorithms for scheduled traffic model in terms of total energy consumption and blocking probability.The simulation results in three typical carrier topologies show the efficiency of our proposed TDGTD algorithm.

A Grey Wolf Optimization-Based Tilt Tri-rotor UAV Altitude Control in Transition Mode

To solve the problem of altitude control of a tilt tri-rotor unmanned aerial vehicle(UAV)in the transition mode,this study presents a grey wolf optimization(GWO)based neural network adaptive control scheme for a tilt trirotor UAV in the transition mode.Firstly,the nonlinear model of the tilt tri-rotor UAV is established.Secondly,the tilt tri-rotor UAV altitude controller and attitude controller are designed by a neural network adaptive control method,and the GWO algorithm is adopted to optimize the parameters of the neural network and the controllers.Thirdly,two altitude control strategies are designed in the transition mode.Finally,comparative simulations are carried out to demonstrate the effectiveness and robustness of the proposed control scheme.

High-precision grating sensor demodulation device's network interface based on DM9000A

High-precision fiber Bragg grating se nsor demodulation instrument with wide-range dynamic scanning can effectivel y improve the measuring range of the optical fiber grating sensor.Ethernet com munication module is an extremely important part of the high-precision grating sensor demodulation device.Network interface based on Ethernet control chip D M9000A is used to send and receive the Bragg grating sensing pulse.The network transformer YL18-2050S is used to convert and filter the pulse from network.The transmitting and receiving program of grating demodulation,hardware circuit of Ethernet transmission interface are designed.The experimental results show that the network interface can achieve accurate and real-time transmissi on of the grating sensing information at high speed.

Improved Roberts operator for detecting surface defects of heavy rails with superior precision and efficiency

An experimental platform accompanying with the improved Roberts algorithm has been developed to achieve accurate and real-time edge detection of surface defects on heavy rails.Detection results of scratching defects show that the improved Roberts operator can attain accurate positioning to defect contour and get complete edge information.Meanwhile,a decreasing amount of interference noises as well as more precise characteristic parameters of the extracted defects can also be confirmed for the improved algorithm.Furthermore,the BP neural network adopted for defects classification with the improved Roberts operator can obtain the target training precision with 98 iterative steps and time of 2s while that of traditional Roberts operator is 118 steps and 4s.Finally,an enhanced defects identification rate of 13.33%has also been confirmed after the Roberts operator is improved.The proposed detecting platform will be positive in producing high-quality heavy rails and guaranteeing the national transportation safety.

Model-data-driven AVO inversion method based on multiple objective functions

The model-driven inversion method and data-driven prediction method are eff ective to obtain velocity and density from seismic data.The former necessitates initial models and cannot provide high-resolution inverted parameters because it primarily employs medium-frequency information from seismic data.The latter can predict parameters with high resolution,but it require a signifi cant number of accurate training samples,which are typically in limited supply.To solve the problems mentioned for these two methods,we propose a model-data-driven AVO inversion method based on multiple objective functions.The proposed method implements network training,network optimization,and network inversion by using three independent objective functions.Tests on synthetic and fi eld data show that the proposed method can invert high-accuracy and high-resolution velocity and density with a few training samples.

Energy Efficient Access Point Selection and Signal Projection for Accurate Indoor Positioning

We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network(WLAN) environment.First,we intelligently and jointly select the subset of Access Points(APs) used in positioning via Maximum Mutual Information(MMI) criterion.Second,we propose Orthogonal Locality Preserving Projection(OLPP) to reduce the redundancy among selected APs.OLPP effectively extracts the intrinsic location features in situations where previous linear signal projection techniques failed to do,while maintaining computational efficiency.Third,we show that the combination of AP selection and OLPP simultaneously exploits their complementary advantages while avoiding the drawbacks.Experimental results indicate that,compared with the widely used weighted K-nearest neighbor and maximum likelihood estimation method,the proposed method leads to 21.8%(0.49 m) positioning accuracy improvement,while decreasing the computation cost by 65.4%.

A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

Inversion Precipitable Water Vapor by GPS Observation of CMONOC

In this study, we have processed the GPS （Global Position System） and meteorological data from about 220 stations of CMONOC （Crustal Movement Observation Network of China in short） observed in 2014 by GAMIT software. The comparison result of ZTD （zenith total delay） calculated by GPS data and IGS （International GNSS （Global Navigation Satellite System） Service） ZTD product shows that the tropospheric delay based on calculation of CMONOC project data is accurate and reliable. Meanwhile, the PWV （precipitable water vapor） correlation coefficients between GPS observation and upper air sounding is close to 1, which proves that GPS observation data generated in CMONOC project applied to the weather forecast research is feasible. In addition, we make an isoline image for PWV distribution per hour on all stations covered the whole Chinese land area using interpolation algorithms. We observe obvious feature that the precipitable water in north and western area is less than south and east area all over this year. High latitudes area may be dry and low latitudes area is wet.

Hierarchy Bayesian model based services awareness of high-speed optical access networks

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit(ONU) and to perform complex services awareness from the whole view of system in optical line terminal(OLT). Simulation results show that the proposed scheme is able to achieve better quality of services(Qo S), in terms of packet loss rate and time delay.