RVFL-Based Optical Fiber Intrusion Signal Recognition With Multi-Level Wavelet Decomposition as Feature

The optical fiber pre-waming system （OFPS） has been gradually considered as one of the important means for pipeline safety monitoring. Intrusion signal types are correctly identified which could reduce the cost of troubleshooting and maintenance of the pipeline. Most of the previous feature extraction methods in OFPS are usually quested from the view of time domain. However, in some cases, there is no distinguishing feature in the time domain. In the paper, firstly, the intrusion signal features of the running, digging, and pick mattock are extracted in the frequency domain by multi-level wavelet decomposition, that is, the intrusion signals are decomposed into five bands. Secondly, the average energy ratio of different frequency bands is obtained, which is considered as the feature of each intrusion type. Finally, the feature samples are sent into the random vector functional-link （RVFL） network for training to complete the classification and identification of the signals. Experimental results show that the algorithm can correctly distinguish the different intrusion signals and achieve higher recognition rate.

Dynamic temperature prediction of electronic equipment under high altitude long endurance conditions

Unmanned Aerial Vehicle（UAV） is developing towards the direction of High Altitude Long Endurance（HALE）. This will have an important influence on the stability of its airborne electronic equipment using passive thermal management. In this paper, a multi-node transient thermal model for airborne electronic equipment is set up based on the thermal network method to predict their dynamic temperature responses under high altitude and long flight time conditions. Some relevant factors are considered into this temperature prediction model including flight environment,radiation, convection, heat conduction, etc. An experimental chamber simulating a high altitude flight environment was set up to survey the dynamic thermal responses of airborne electronic equipment in a UAV. According to the experimental measurement results, the multi-node transient thermal model is verified without consideration of the effects of flight speed. Then, a modified way about outside flight speed is added into the model to improve the temperature prediction performance. Finally, the corresponding simulation code is developed based on the proposed model. It can realize the dynamic temperature prediction of airborne electronic equipment under HALE conditions.

A power and thermal management system for long endurance hypersonic vehicle

Due to the pneumatic heating and combustion effect,the scramjet engine of hypersonic vehicle faces high temperature challenge.It is necessary to comprehensively consider its thermal management and power generation together.A new Power and Thermal Management System(PTMS)combined with Supercritical Carbon Dioxide(SCO_(2))closed Brayton cycle and fuel vapor turbine is proposed and discussed in this paper.The new PTMS can meet the cooling requirement of hypersonic vehicle at Mach number 6–7,and avoid the coking and scrapping in the scramjet cooling channels.Compared with the PTMS only based on fuel vapor turbine,the new PTMS utilizes the waste heat of scramjet to generate more electricity.In addition,it can reduce the use of fuel sink for cooling,and the additional weight penalty can be compensated for long endurance hypersonic flight.

A New Detection Method Based on CFAR and DE for OFPS

Optical fiber pre-warning system （OFPS） is widely utilized in pipeline transport fields. The intrusions of OFPS need to be located. In this system, the original signals consist of noises, interferences, and intrusion signals. Here, noises are background and harmless interferences possessing with high power, and the intrusion signals are the main target of detection in this system. Hence, the study stresses on extracting the intrusion signals from the total ones. The proposed method can be divided into two parts, constant false alarm rate （CFAR） and dilation and erosion （DE）. The former is applied to eliminate noises, and the latter is to remove interferences. According to some researches, the feature of noise background accords with the CFAR spatial detection. Furthermore, the detection results after CFAR can be presented as a binary image of time and space. Besides, interferences are relatively disconnected. Consequently, they can be eliminated by DE which is introduced from the image processing. To sum up, this novel method is based on CFAR and DE which can eliminate noises and interferences effectively. Moreover, it performs a brilliant detection performance. A series of tests were developed in Men Tou Gou of Beijing, China, and the reliability of proposed method can be verified by these tests.

Well-defined coordination environment breaks the bottleneck of organic synthesis:Single-atom palladium catalyzed hydrosilylation of internal alkynes

Single-atom site(SAS)catalysts have attracted considerable attention due to their excellent performance.However,most of the current research models of SAS catalysts are based on inorganic catalysts,where“metal and coordination atom interaction”cannot simulate the fine-tuning effect of organic ligands on metal catalytic centers in homogeneous catalysts.Therefore,certain chemical transformations in homogeneous catalysis cannot be perfectly replicated.Here,we used porous organic ligand polymers as the carrier,which effectively changes the charge regulation of nanoparticles and monoatomic metal catalysts.Drawing lessons from traditional homogeneous metal/ligand catalysis,we introduced various functional groups into the ligand polymers to adjust the electronic properties,and successfully realized the hydrosilylation of internal alkynes with high catalytic performance.The selectivity and catalytic efficiency under the Pd@POL-1 catalyst system were improved compared with previous studies.The internal alkynes with various structures can complete this reaction,and the ratio of E/Zcan reach up to 100:1.

A Harmful-Intrusion Detection Method Based on Background Reconstruction and Two-Dimensional K-S Test in an Optical Fiber Pre-Warning System

The key technology and main difficulty for optical fiber intrusion pre-warning systems （OFIPS） is the extraction of harmful-intrusion signals. After being processed by a phase-sensitive optical time-domain reflectometer （O-0TDR）, vibration signals can be preliminarily extracted. Generally, these include noises and intrusions. Here, intrusions can be divided into harmful and harmless intrusions. With respect to the close study of signal characteristics, an effective extraction method of harmful intrusion is proposed in the paper. Firstly, in the part of the background reconstruction, all intrusion signals are first detected by a constant false alarm rate （CFAR）. We then reconstruct the backgrounds by extracting two-part information of alarm points, time and amplitude. This ensures that the detection background consists of intrusion signals. Secondly, in the part of the two-dimensional Kolmogorov-Smirnov （K-S） test, in order to extract harmful ones from all extracted intrusions, we design a separation method. It is based on the signal characteristics of harmful intrusion, which are shorter time interval and higher amplitude. In the actual OFIPS, the detection method is used in some typical scenes, which includes a lot of harmless intrusions, for example construction sites and busy roads. Results show that we can effectively extract harmful intrusions.

Thermal models for avionics pod cabin based on Stochastic Configuration Network(SCN)

The thermal failure of airborne avionics equipment is not optimistic.It is very necessary to establish relatively accurate thermal models for predicting thermal response of avionics equipment under different flight conditions.Traditional thermal modeling methods are often difficult to obtain accurate temperature response in complex conditions.This has severely restricted the application of these models.However,the Stochastic Configuration Network(SCN)model based on random algorithm can weaken the heat transfer mechanism and pay attention to the mining of experimental data,so that a more accurate thermal relationship might be obtained.In this paper,the SCN was used to analyze the experimental data of the avionics pod with a Ram Air Turbine(RAT)cooling system.The thermal models based on the SCN were finally built for avionics pod.Compared with the commonly used Random Vector Functional Link Network(RVFLN)thermal models,the SCN thermal models not only inherit the advantages of simple network structure and low computational complexity,but also have some merits,such as the better learning performance and the less human intervention.The presented SCN models provide a way to predict the thermal response of avionics pod cabin under the full flight envelope for a fighter.

Numerical simulation of a cabin ventilation subsystem in a space station oriented real-time system

An environment control and life support system（ECLSS） is an important system in a space station. The ECLSS is a typical complex system, and the real-time simulation technology can help to accelerate its research process by using distributed hardware in a loop simulation system. An implicit fixed time step numerical integration method is recommended for a real-time simulation system with time-varying parameters. However, its computational efficiency is too low to satisfy the real-time data interaction, especially for the complex ECLSS system running on a PC cluster. The instability problem of an explicit method strongly limits its application in the ECLSS real-time simulation although it has a high computational efficiency. This paper proposes an improved numerical simulation method to overcome the instability problem based on the explicit Euler method. A temperature and humidity control subsystem（THCS） is firstly established, and its numerical stability is analyzed by using the eigenvalue estimation theory. Furthermore, an adaptive operator is proposed to avoid the potential instability problem. The stability and accuracy of the proposed method are investigated carefully. Simulation results show that this proposed method can provide a good way for some complex time-variant systems to run their real-time simulation on a PC cluster.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system （OFPS） is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform （MT） is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation （BP） neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Convergence analysis of an infeasible quasi- Newton bundle method for nonsmooth convex programming

By utilizing the improvement function,we change the nonsmooth convex constrained optimization into an unconstrained optimization,and construct an infeasible quasi-Newton bundle method with proximal form.It should be noted that the objective function being minimized in unconstrained optimization subproblem may vary along the iterations(it does not change if the null step is made,otherwise it is updated to a new function).It is necessary to make some adjustment in order to obtain the convergence result.We employ the main idea of infeasible bundle method of Sagastizabal and Solodov,and under the circumstances that each iteration point may be infeasible for primal problem,we prove that each cluster point of the sequence generated by the proposed algorithm is the optimal solution to the original problem.Furthermore,for BFGS quasi-Newton algorithm with strong convex objective function,we obtain the condition which guarantees the boundedness of quasi-Newton matrices and the R-linear convergence of the iteration points.

Study on forecasting the parameters of gas environment of metro station

In the crowded metro station,it is important to forecast the changes of environmental parameters for the normal operation of metro and the safety of passengers in the future.Artificial Neural Network(ANN)has a good perfor-mance on processing time series data.In order to accurately predict environmental parameters,this paper uses ANN method to build the forecasting model of environmental parameters.The forecasting model uses the external environment parameters of the station as input variables.Finally,the accuracy of the model is verified by the field data collected from the metro station.The results show that the mean relative error of the proposed method is within 10%.The forecasting model based on ANN in this paper can accurately forecast the internal environment parameters of the metro station in the future period and is of great significance of emergency prevention and decision-making.

Analysis and comparison of potential power and thermal management systems for high-speed aircraft with an optimization method

Under the dual effects of aerodynamic heating and high-power electronic equipment heating,the heat sink and power demand of advanced high-speed aircraft have been exponentially rising,which seriously restricts the aircraft performance.To improve system cooling and power supply performance and reduce engine performance loss,a power and thermal management system(PTMS)with high performance,low energy consumption,and light weight urgently needs to be developed.In this paper,three modes of a potential PTMS with different heat sinks and bleed air sources are further discussed to analyze and compare the optimal matching with the flight mission at Mach 1-4.4.The equivalent mass method is used to uniformly assess the costs of the fixed weight,bleed,resistance,etc.as a function of the fuel weight penalty,which is chosen as the optimization objective.The optimization variables consist of the compressor outlet temperature,cooling air flow rate,and fan duct heat exchanger structure size.The results show that the intermediate-stage bleed air and fan duct heat sink are more suitable when the Mach number is less than 2,but the ram air bleed is highly suitable for flight missions at a high Mach number.Especially at Mach 3.4-4.4,the ram air bleed mode can respond to the cooling and power demands with a simple architecture.