The modern air traffic management system relies heavily on GNSS systems, which provide multiple information for communication, navigation, and surveillance systems, GNSS interference seriously affects the accuracy, consistency, and reliability of various aviation equipment, endangering aviation safety. When GNSS interferes, Automatic Dependent Surveillance-Broadcast (ADS-B) reported data will also appear abnormal, since ADS-B data mainly depends on GNSS. Therefore, the ADS-B report, which is high-frequency and contains spatiotemporal data characteristics provides a new idea for interference detection. However, most existing methods for detecting GNSS interference sources based on ADS-B data rely on only one or two index from a single ADS-B report. The inherent uncertainty of the ADS-B data leads to a tendency for these detection methods to identify non-interfering as interfering.In this paper, we propose a new A Machine Learning GNSS interference detection method based on ADS-B multi-index features, which reduces the impact of the uncertainty of the ADS-B data itself on GNSS interference detection through two improvements. Firstly, we analyze the multiple relevant features of the interference data based on laboratory simulation experiences and actual accidents, then observes the data distribution of each ADS-B feature under normal and interfering conditions and the interrelationship between ADS-B features. The Navigation Integrity Category (NIC), Navigation Accuracy Category for Position (NACp), Source Integrity Level (SIL), messages update interval, the change rate of ground speed, the change rate of position, track angle(TA), flight level(FL), and ADS-B equipment version were finally extracted as multi-index features. Secondly, this paper uses sliding windows to construct new inputs that contain time dimension change information. This construction of input data can obtain more accurate manual annotation and enable various machine learning classifiers to be more effectively applied to ADS-B report data.To prove the effectiveness of the above two improvements, the logical regression model based on multi-index system with original inputs construction, is used as a baseline for classifier performance, it first compared with the original method with a single index, through experiments we find that the classification method based on multiple index has a better performance. Then, various multi-index machine learning methods with new inputs constructed were used to detect GNSS interference, including Recurrent Neural Network(RNN) and Long Short Term Memory(LSTM). Also, take the multi-index logistic regression model as a baseline, and finally, the experimental results are compared and discussed.
Low-frequency oscillation (LFO) is a security and stability issue that the power system focuses on, measurement data play an important role in online monitoring and analysis of low-frequency oscillation parameters. Aiming at the problem that the measurement data containing noise affects the accuracy of modal parameter identification, a VMD-SSI modal identification algorithm is proposed, which uses the variational modal decomposition algorithm (VMD) for noise reduction combined with the stochastic subspace algorithm for identification. The VMD algorithm decomposes and reconstructs the initial signal with certain noise, and filters out the noise signal. Then, the optimized signal is input into stochastic subspace identification algorithm(SSI), the modal parameters is obtained. Simulation of a three-machine ninenode system verifies that the VMD-SSI mode identification algorithm has good anti-noise performance.
In this paper, the material properties, sectional properties, rotating speed and the boundary condition of beam are treated as random variable. A Ritz method is used to derive the eigenvalues equation of the rotating beam with uncertain parameters. A matrix perturbation technique is employed to obtain probabilistic characters of the natural frequencies and vibration modes for free vibration. The sensitivity analysis is performed to study the influence of parametric changes on the variation of nature frequencies. The effects of variation in material propertied, rotating speed and the joint stiffness on the deterministic part of natural frequencies and vibration modes, the covariance and the coefficient of variance (COV) for the nature frequencies are investigated. A numerical example is studied by the present method and the Monte Carlo simulation to establish the validity of the present method.
Aiming at the non-stationary and non-linearity characteristics of the vibration signals of centrifugal pump,a new method based on complexity feature of empirical mode decomposition(EMD) and least square support vector machine (LS-SVM) was presented.First of all,EMD method was used to decompose the vibration signals into a finite number of stationary intrinsic mode functions(IMF),and then complexity features of each IMF was extracted as the fault characteristics vectors and served as input parameters of LS-SVM classifier to diagnosis fault.Radial basis function(RBF) function was used as kernel function;the differential evolution(DE) method was proposed to select hyper-parameter of LS-SVM.Application results show that the proposed method is very effective,which can better extract the nonlinear features of the fault and more exactly diagnosis fault,optimized LS-SVM model has strong capability of classification.
The paper realized high-speed transmission of product image information in industry field based on the radio technology. It successfully solved the problems, such as jamming or disturbance of 2.4 GHz frequency channel, the exorbitant power dissipation, complicated RF protocol. The paper developed the embedded software, basically realized the predetermined function.
ADRC technology is using nonlinear theory and design of the new controller,based on the study of ADRC technology,on the basis of using Matlab/Simulink for platforms,application S function write tracking differentiator,nonlinear combination and extended state observer and implement subsystems encapsulation.Through the main steam temperature control system power simulation example,showing auto-disturbances-rejection technology has stronger robustness,quickness and has high practical value.
A pulse-mode transmitter with low carrier leakage for 24GHz short-range car radar applications is described. A 12.5dBm output power amplifier (continuous into 50Ω), a pulse width and rate control circuit and a voltage reference circuit are included on the IC. The pulse-mode 24GHz output signal is modulated via the final stage bias current to achieve a RF carrier leakage of -50dBm in the off-state. The power dissipation is 360mW when RF is on, 117mW when RF is off, resulting in a typical 122mW dissipation in normal operation. The 1.2 × 0.87mm 2 IC operates from a 4.5V supply and is fabricated in 0.25μm SiGe:C BiCMOS technology [1].
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