Hybrid Fault Diagnosis and Isolation for Component and Sensor of APU in a Distributed Control System

This paper addresses the gas path component and sensor fault diagnosis and isolation(FDI) for the auxiliary power unit(APU). A nonlinear dynamic model and a distributed state estimator are combined for the distributed control system. The distributed extended Kalman filter(DEKF)is served as a state estimator,which is utilized to estimate the gas path components’ flow capacity. The DEKF includes one main filter and five sub-filter groups related to five sensors of APU and each sub-filter yields local state flow capacity. The main filter collects and fuses the local state information,and then the state estimations are feedback to the sub-filters. The packet loss model is introduced in the DEKF algorithm in the APU distributed control architecture. FDI strategy with a performance index named weight sum of squared residuals(WSSR) is designed and used to identify the APU sensor fault by removing one sub-filter each time. The very sensor fault occurs as its performance index WSSR is different from the remaining sub-filter combinations. And the estimated value of the soft redundancy replaces the fault sensor measurement to isolate the fault measurement. It is worth noting that the proposed approach serves for not only the sensor failure but also the hybrid fault issue of APU gas path components and sensors. The simulation and comparison are systematically carried out by using the APU test data,and the superiority of the proposed methodology is verified.

An optimized time-adaptive aerothermal coupling calculation method for aerothermal analysis of disc cavity system

In order to further achieve the balance between the calculation accuracy and efficiency of the transient analysis of the aero-engine disc cavity system,an Optimized Time-adaptive Aerother-mal Coupling calculation(OTAC)method has been proposed.It combines one-dimensional tran-sient calculation of air system,Conventional Sequence Staggered(CSS)method,Time-adaptive Aerothermal Coupling calculation(TAC)method and differential evolution optimization algorithm to obtain an efficient and high-precision aerothermal coupling calculation method of air system.Considering both the heat conduction in the solid domain and the flow in the fluid domain as unsteady states in the OTAC,the interaction of fluid-solid information within a single coupling time step size was implemented based on the CSS method.Furthermore,the coupling time step size was automatically adjusted with the number of iterations by using the Proportional-Integral-Deri vative(PID)controller.Results show that when compared with the traditional loosely coupling method with a fixed time step size,the computational accuracy and efficiency of the OTAC method are improved by 8.9%and 30%,respectively.Compared with the tight coupling calculation,the OTAC method can achieve a speedup of 1 to 2 orders of magnitude,while the calculation error is maintained within 6.1%.

An Approximate Calculation Method for Lateral Trajectory Correction

The two-dimensional trajectory correction needs to adjust not only the force in velocity direction,but also the lateral force or lateral trajectory (normal to the perpendicular plane of fire direction) . Therefore,its structure of control cabin is more complicated than that of one-dimensional trajectory correction projectiles (ODTCP). In order to simplify the structure and reduce the cost,a scheme of adding a damping disc to the control cabin of ODTCP has been developed recently. The damping disc will unfold at the right moment during its flight to change the ballistic drift of rotary projectiles. Aimed at this technical scheme,a mathematical model of two-dimensional trajectory corrections was discussed according to the theory of exterior ballistics. An approximate formula for predicting the ballistic drift and trajectory correction was deduced. The capability of lateral trajectory correction and the flight stability of TDTCP were also analyzed. All the work is valuable for further research.

NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

In this paper,the new method for OCT images denoizing based on empirical mode decomposition(EMD)is proposed.The noise reduction is a very important process for following operations to analyze and recognition of tissue structure.Our method does not require any additional operations and hardware modifications.The basics of proposed method is described.Quality improvement of noise suppression om example of edge detection procedure using the classical Canny's algorithm without any additional pre-and post-proc essing operations is demonstrated.Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

A Survey of SQL Injection Attack Detection and Prevention

Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.

Role playing learning for socially concomitant mobile robot navigation

In this study, the authors present the role playing learning scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks （NNs） are constructed to parameterise a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, this process is called role playing learning, which is formulated under a reinforcement learning framework. The NN policy is optimised end-to-end using trust region policy optimisation, with consideration of the imperfectness of robot＇s sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of the proposed method.

Multi-scale simulation model of air system based on cross-dimensional data transmission method

The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation.

Effects of Heterogeneous Vegetation on the Surface Hydrological Cycle

Using the three-layer variable infiltration capacity （VIC-3L） hydrological model and the successive interpolation approach （SIA） of climate factors, the authors studied the effect of different land cover types on the surface hydrological cycle. Daily climate data from 1992 to 2001 and remotely-sensed leaf area index （LAI） are used in the model. The model is applied to the Baohe River basin, a subbasin of the Yangtze River basin, China, with an area of 2500 km^2. The vegetation cover types in the Baohe River basin consist mostly of the mixed forest type （-85%）. Comparison of the modeled results with the observed discharge data suggests that： （1） Daily discharges over the period of 1992-2001 simulated with inputs of remotely-sensed land cover data and LAI data can generally produce observed discharge variations, and the modeled annual total discharge agrees with observations with a mean difference of 1.4%. The use of remote sensing images also makes the modeled spatial distributions of evapotranspiration physically meaningful. （2） The relative computing error （RCE） of the annual average discharge is -24.8% when the homogeneous broadleaf deciduous forestry cover is assumed for the watershed. The error is 21.8% when a homogeneous cropland cover is assumed and -14.32% when an REDC （Resource and Environment Database of China） land cover map is used. The error is reduced to 1.4% when a remotely-sensed land cover at 1000-m resolution is used.

Study on lift-up speed of aerodynamic compliant foil thrust bearings

Objective The experimental study on the lift-up speed of a new kind of compliant aerodynamic foil thrust bearings was performed on the multifunctional test rig established for testing the performances of foil gas bearings.Methods The lift-up speed of foil gas thrust bearing under given axial load was analyzed through the spectrum of axial displacement response in frequency domain.Results The test results indicated that the difference in the spectrum of axial displacement responses before and after lifting up of the rotor was obvious.After lifting up of the rotor,there were only larger components of rotation frequency and lower harmanic frequencies.If the rotor wasn't lift-up,there were also larger components of other frequencies in the spectrum.Conclusion So by analyzing the spectrum of axial displacement response,the results showed that the lift-up speed was about 1860rpm when the axial load was 31N.

Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents （e.g., uniform scaling） cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content- aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.

Comparison of Attribute Reduction Methods for Coronary Heart Disease Data by Decision Tree Classification

Attribute reduction is necessary in decision making system. Selecting right attribute reduction method is more important. This paper studies the reduction effects of principal components analysis (PCA) and system reconstruction analysis (SRA) on coronary heart disease data. The data set contains 1723 records, and 71 attributes in each record. PCA and SRA are used to reduce attributes number (less than 71 ) in the data set. And then decision tree algorithms, C4.5, classification and regression tree ( CART), and chi-square automatic interaction detector ( CHAID), are adopted to analyze the raw data and attribute reduced data. The parameters of decision tree algorithms, including internal node number, maximum tree depth, leaves number, and correction rate are analyzed. The result indicates that, PCA and SRA data can complete attribute reduction work,and the decision-making rate on the reduced data is quicker than that on the raw data; the reduction effect of PCA is better than that of SRA, while the attribute assertion of SRA is better than that of PCA. PCA and SRA methods exhibit goodperformance in selecting and reducing attributes.

Developing a geographic Case-Based Reasoning approach

Case-Based Reasoning （CBR） is an AI approach and been applied to many areas. However, one area - geography - has not been investigated systematically and thus has been identified as the focus for this study. This paper intends to further extend current CBR to a geographic CBR （Geo-CBR）. First, the concept of Geo-CBR is proposed. Second, a representation model for geographic cases has been established based on the Tesseral model and on a further extension in spatio-temporal dimensions for geographic cases. Third, a reasoning model for Geo-CBR is developed by considering the spatio-temporat characteristics and the uncertain and limited information of geographic cases. Finally, the Geo-CBR model is applied to forecasting the production of ocean fisheries to demonstrate the applicability of the developed Geo-CBR in solving problems in the real world. According to the experimental results, Geo-CBR is an effective and easy-to-implement approach for predicting geographic cases quantitatively.

Unmanned aerial vehicle swarm mission reliability modeling and evaluation method oriented to systematic and networked mission

With the development of Unmanned Aerial Vehicle(UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations,and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decisionmaking work and supports the development of UAV-swarm-related work.

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle(UAV)swarms have been foreseen to play an important role in military applications in the future,wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults.Therefore,a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms.However,their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system.In this paper,a complex network theory-based robustness evaluation method for a UAV swarming system is proposed.This method takes into account the dynamic evolution of UAV swarms,including dynamic reconfiguration and information correlation.The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies.The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed.Finally,the validity of the proposed method is verified by conducting a case study analysis.The results can further provide some guidance and reference for the robust design,mission planning and decision-making of UAV swarms.

Microstructure, porosity and mechanical properties of selective laser melted AlSi10Mg

Finite element modeling(FEM),microscopy,X-ray computed tomography(CT)and mechanical property tests were used to study the microstructure,porosity and mechanical properties of an AlSi10Mg alloy produced by selective laser melting(SLM).The simulation of the melt pool and thermal history under different energy densities produced an optimized result with an energy density of 44.5 J·mm-3.The high cooling rate during the SLM process significantly refined the previous a-Al dendrites.The growth direction of the network-like Al-Si eutectic structure at different orientations confirmed the anisotropic nature of the microstructure.Furthermore,the microhardness,tensile testing and fracture analysis results proved that there were no obvious distinctions in the strength between the transverse and longitudinal directions,and that the ductility was anisotropic,possibly due to the shape and distribution of the pores.The pores measured by X-ray CT at different energy densities confirmed that the sphericity of the pores was inversely related to pores volumes.With optimized processing conditions,the porosity of the selective laser melted sample decreased leading to the improved fabricated fuel system component via SLM.

Deterministic quantum communication using the symmetric W state

This study proposes a new coding function for the symmetric W state. Based on the new coding function, a theoretical protocol of deterministic quanama communication （DQC） is proposed. The sender can use the proposed coding function to encode his/her message, and the receiver can perform the imperfect Bell measurement to obtain the sender＇s message. In comparison to the existing DQC protocols that also use the W class state, the proposed protocol is more efficient and also more practical within today＇s technology. Moreover, the security of this protocol is analyzed to show that any eavesdropper will be detected with a very high probability under both the ideal and the noisy quantum channel.

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter(KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multisensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Squareroot Cubature Information Filter(MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate informationfilter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment.Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.

A novel control method for turboshaft engine with variable rotor speed based on the Ngdot estimator through LQG/LTR and rotor predicted torque feedforward

In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed,a cascade PID control method based on the acceleration estimator of gas turbine speed(Ngdot)and rotor predicted torque feedforward is proposed.Firstly,a two-speed Dual Clutch Transmission(DCT)model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed.Then,an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery(LQG/LTR)is proposed for power turbine speed cascade control.Finally,according to the cascade PID controller based on Ngdot estimator,a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network(MGD-NN)is put forward to compromise the influence of rotor torque interference.The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control,the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%,which possesses faster response,superior dynamic effect and satisfactory robustness performance.The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.