Short-time maritime target detection based on polarization scattering characteristics

In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.

The analysis of frequency-dependent characteristics for fluid detection: a physical model experiment

According to the Chapman multi-scale rock physical model, the seismic response characteristics vary for different fluid-saturated reservoirs. For class I AVO reservoirs and gas-saturation, the seismic response is a high-frequency bright spot as the amplitude energy shifts. However, it is a low-frequency shadow for the Class III AVO reservoirs saturated with hydrocarbons. In this paper, we verified the high-frequency bright spot results of Chapman for the Class I AVO response using the frequency-dependent analysis of a physical model dataset. The physical model is designed as inter-bedded thin sand and shale based on real field geology parameters. We observed two datasets using fixed offset and 2D geometry with different fluid- saturated conditions. Spectral and time-frequency analyses methods are applied to the seismic datasets to describe the response characteristics for gas-, water-, and oil-saturation. The results of physical model dataset processing and analysis indicate that reflection wave tuning and fluid-related dispersion are the main seismic response characteristic mechanisms. Additionally, the gas saturation model can be distinguished from water and oil saturation for Class I AVO utilizing the frequency-dependent abnormal characteristic. The frequency-dependent characteristic analysis of the physical model dataset verified the different spectral response characteristics corresponding to the different fluid-saturated models. Therefore, by careful analysis of real field seismic data, we can obtain the abnormal spectral characteristics induced by the fluid variation and implement fluid detection using seismic data directly.

Leak Detection of Gas Pipelines Based on Characteristics of Acoustic Leakage and Interfering Signals

When acoustic method is used in leak detection for natural gas pipelines,the external interferences including operation of compressor and valve,pipeline knocking,etc.,should be distinguished with acoustic leakage signals to improve the accuracy and reduce false alarms.In this paper,the technologies of extracting characteristics of acoustic signals were summarized.The acoustic leakage signals and interfering signals were measured by experiments and the characteristics of time-domain,frequency-domain and time-frequency domain were extracted.The main characteristics of time-domain are mean value,root mean square value,kurtosis,skewness and correlation function,etc.The features in frequency domain were obtained by frequency spectrum analysis and power spectrum density,while time-frequency analysis was accomplished by short time Fourier transform.The results show that the external interferences can be removed effectively by the characteristics of time domain,frequency domain and time-frequency domain.It can be drawn that the acoustic leak detection method can be applied to natural gas pipelines and the characteristics can help reduce false alarms and missing alarms.

Detection and characteristics analysis of the western subarctic front using the high-resolution SST product

Oceanic front plays a significant role in the ocean vertical mixing and the regulation of air-sea interaction,among others.The western branch of the subarctic front(WSAF)located in the Northwest Pacific has attained lots of attention given its strong intensity and widespread influence on this region.In this study,we take advantage of the merged sea surface temperature(SST)at a high spatial resolution of 0.05°to investigate the characteristics of WSAF.The front detection algorithm that combines the Sobel operator and histogram analysis is adopted.It is advantageous in both preserving the front intensity represented by the SST gradient as well as reducing the detection noise level.We systematically applied this algorithm to the daily SST products for front detection,based on which the WSAF characteristics including its intensity,occurrence of frequency,latitudinal position and coverage area are then extracted.WSAF is mostly located within a small latitude range between 40°N and 41°N with a clear seasonal trend in its intensity that peaks in the winter and troughs in the summer.The seasonal variation of WSAF intensity is almost consistent throughout the temporal period of interest from 2010 to 2018.Similar seasonality is observed for its occurrence of frequency with the winter-summer contrast reaching up to5%.The findings presented here shall help better interpret the WSAF characteristics in the long-term run as well as their impact on the regional weather and climate patterns at high spatial resolution.

Microscopic Characteristics and Routine Detection of Hedyotis hedyotidea(DC.)Merr.

[Objectives]To study the microscopic characteristics and routine detection of Hedyotis hedyotidea(DC.)Merr.[Methods]The microscopic characteristics of the root,stem cross section and the whole plant powder of H.hedyotidea were studied by the methods of slide-shaft section,biological staining and microscopic imaging;the routine detection items such as moisture,ash,acid-insoluble ash and extract were detected and analyzed in accordance with the requirements of Chinese Pharmacopoeia.[Results]The microscopic characteristics of the root,the stem and the whole plant powder of H.hedyotidea were obtained.The moisture content of 10 batches of H.hedyotidea samples was in the range of 4.25%-7.90%,the water-soluble extract is in the range of 15.08%-22.52%,the total ash was in the scope of 8.27%-10.45%,and the acid-insoluble ash was in 0.13%-0.95%.The proposed water content of H.hedyotidea should not exceed 10.00%,the water-soluble extract should not be less than 12.00%,the total ash content should not exceed 13.00%,and the acid-insoluble ash content should not exceed 1.20%.[Conclusions]The results of this experiment can provide a reference for the quality control and quality standard of H.hedyotidea.

Study on detection method of voltage sag characteristics for power system

This paper introduced briefly the Voltage sag characteristics with emphasizs on the algorithm methods for Voltage sags characteristics detection and their differences,and analyzed the problems faced by the sags detection.

Characteristic-knowledge-aided spectral detection of high frequency first-order sea echo

To detect high frequency （HF） first-order sea echo spectra contaminated with ships, ionosphere interference, and other, a new characteristic-knowledge-aided detection method is proposed. With 2-D image features in range-Doppler spectrum, the trend of first-order sea echoes is extracted as indicative information by a multi-scale filter. Detection rules for both single and splitting first-order sea echoes are given based on the characteristic knowledge combining the indicative information with the global characteristics such as amplitude, symmetry, continuity, etc. Compared with the classical algorithms, the proposed method can detect and locate the first-order sea echo in the HF band more accurately especially in the environment with targets/clutters smearing. Experiments with real data verify the validity of the algorithm.

Cluster DetectionMethod of Endogenous Security Abnormal Attack Behavior in Air Traffic Control Network

In order to enhance the accuracy of Air Traffic Control(ATC)cybersecurity attack detection,in this paper,a new clustering detection method is designed for air traffic control network security attacks.The feature set for ATC cybersecurity attacks is constructed by setting the feature states,adding recursive features,and determining the feature criticality.The expected information gain and entropy of the feature data are computed to determine the information gain of the feature data and reduce the interference of similar feature data.An autoencoder is introduced into the AI(artificial intelligence)algorithm to encode and decode the characteristics of ATC network security attack behavior to reduce the dimensionality of the ATC network security attack behavior data.Based on the above processing,an unsupervised learning algorithm for clustering detection of ATC network security attacks is designed.First,determine the distance between the clustering clusters of ATC network security attack behavior characteristics,calculate the clustering threshold,and construct the initial clustering center.Then,the new average value of all feature objects in each cluster is recalculated as the new cluster center.Second,it traverses all objects in a cluster of ATC network security attack behavior feature data.Finally,the cluster detection of ATC network security attack behavior is completed by the computation of objective functions.The experiment took three groups of experimental attack behavior data sets as the test object,and took the detection rate,false detection rate and recall rate as the test indicators,and selected three similar methods for comparative test.The experimental results show that the detection rate of this method is about 98%,the false positive rate is below 1%,and the recall rate is above 97%.Research shows that this method can improve the detection performance of security attacks in air traffic control network.

Detecting the Position of the Moving-iron Solenoid by Non-displacement Sensor Based on Parameter Identification of Flux Linkage Characteristics

Currently, most researches use signals, such as the coil current or voltage of solenoid, to identify parameters; typically, parameter identification method based on variation rate of coil current is applied for position estimation. The problem exists in these researches that the detected signals are prone to interference and difficult to obtain. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which adds a new group of secondary winding to the coil of the ordinary switching electromagnet. On the basis of electromagnetic coupling theory analysis and simulation research of the magnetic field regarding the primary and secondary winding coils, and in accordance with the fact that under PWM control mode varying core position and operating current of windings produce different characteristic of flux increment of the secondary winding. The flux increment of the electromagnet winding can be obtained by conducting time domain integration for the induced voltage signal of the extracted secondary winding, and the core position from the two-dimensional fitting curve of the operating winding current and flux-linkage characteristic quantity of solenoid are calculated. The detecting and testing system of solenoid core position is developed based on the theoretical research. The testing results show that the flux characteristic quantity of switching electromagnet magnetic circuit is able to effectively show the core position and thus to accomplish the non-displacement transducer detection of the said core position of the switching electromagnet. This paper proposes a new method for detecting the core position by using flux characteristic quantity, which provides a new theory and method for switch solenoid to control the proportional valve.

Boundary Layer Characteristics and Numerical Simulation Analysis of Winter Dense Fog in Nanjing

Based on the boundary layer data of winter dense fog in 2007 from Nanjing University of Information Science & Technology,the profile characteristics of temperature,wind direction,wind speed and humidity in a dense fog weather on December 13-14 in 2007 were analyzed,as well as their evolution laws in the formation and dispersion of fog,and the boundary layer characteristics of winter dense fog in Nanjing were revealed,while the development of fog was simulated by means of mesoscale numerical model.The results showed that the formation and dispersion of fog was greatly affected by inversion and humidity in the surface layer,and the wind direction in the surface layer also had effect on the formation and dispersion of advection fog.Mesoscale numerical model could preferably simulate the evolution of temperature,humidity,vertical speed in the development of fog,and the simulation of water vapor content in the fog could forecast the formation and dispersion of fog.

Distributing Characteristics of the Charge on the Bomb Body with Capacitance Fuze

For establishing the equation of the capacitive target detection accurately, the distributing characteristics of the charges on the bomb body with capacitance fuze were explored. Continuous charges were analyzed dispersively. Based on the Coulomb's law, the dynamic equilibrium equations of the inducing charges on the bomb body were set up. For the four cases of d 0/L (the ratio between the electrode distance and the bomb length), the curves of the charge's distribution were given. It was concluded that: ① the charge density decreases steadily from the end near the frontal electrode to the bomb tail; ② the declining rate of the density is governed by d 0/L , the larger the value of d 0/L ,the higher the declining rate, and vice versa.

Detection of phase randomly distributed weak transient signal using chaos

In practical communication and radar systems, the phase of the received signal is random, the arrival time is unknown, the lasting time is limited and the SNR is often very low. In order to realize the detection of the signal, the method of using a group of nonlinear differential equations is presented. The theory of this chaos-based detection is analyzed. Computer simulation indicates that the shortest lasting time of the transient signal that can be detected out is 12 periods, the detection error of arrival time is less than 7/8 signal＇ s period, the detection characteristics are got using Monte-Carlo simulation.

Gas Leakage Detection and Pressure Difference Identification by Asymmetric Differential Pressure Method

Currently,the measurement methods for pneumatic system leakage include bubbling,ultrasonic,and pressure detection methods.These methods are sensitive to high-precision sensors,long detection times,and stable external environments.The traditional differential pressure method involves severe differential pressure fluctuations caused by environmental pressure fluctuations or electromagnetic noise interference of sensors,leading to inaccurate detection.In this paper,a differential pressure fitting method for an asymmetric differential pressure cylinder is proposed.It overcomes the limitation of the detection efficiency caused by the asynchronous temperature recovery of the two chambers in the asymmetric differential pressure method and uses the differential pressure substitution equation to replace the differential calculation of the differential pressure.The improved differential pressure method proposes an innovation based on the detection principle and calculation method.Additionally,the influence of the parameters in the differential pressure substitution equation on the leakage calculation results was simulated,and the specific physical significance of the parameters of the differential pressure substitution equation was explained.The experiments verified the fitting effect and proved the accuracy of this method.Compared with the traditional differential pressure method,the maximum leakage deviation of inhibition was 0.5 L/min.Therefore,this method can be used to detect leaks in air tanks.

Robust Lane Detection in Shadows and Low Illumination Conditions using Local Gradient Features

This paper presents a method for lane boundaries detection which is not affected by the shadows, illumination and un-even road conditions. This method is based upon processing grayscale images using local gradient features, characteris-tic spectrum of lanes, and linear prediction. Firstly, points on the adjacent right and left lane are recognized using the local gradient descriptors. A simple linear prediction model is deployed to predict the direction of lane markers. The contribution of this paper is the use of vertical gradient image without converting into binary image(using suitable thre-shold), and introduction of characteristic lane gradient spectrum within the local window to locate the preciselane marking points along the horizontal scan line over the image. Experimental results show that this method has greater tolerance to shadows and low illumination conditions. A comparison is drawn between this method and recent methods reported in the literature.

ULTRAWIDE BAND RADAR SIGNAL DETECTION, ESTIMATION AND EXPERIMENT

With reference to the air target detection of ultra-wide band (UWB)/impulse radar(IR), the transient signal processing techniques was discussed. In weak UWB signal detection, the wavelet transforms and high order spectrum estimation techniques were preferred. In target characteristic analysis, a time domain bispectrum estimation algorithm was used to analyze the target impulse response, which could estimate accurately local scattering distribution of complex target. A free field IR experimental system installed in an anechoic chamber was used. With this system, experiments to several target models were made. The results of these experiments verified the signal processing method efficiency.

Signal Detection of Large Volume Airgun Source Excitation in the Fixed Field of the Yangtze River

As this is the first time a large volume airgun has been excited in the "Yangtse River Geoscience Project",it is necessary to study the time-frequency characteristic based on the linear stacked seismic data from records from portable stations near the fixed fields and seismic stations. Airgun signal propagation distances were detected using stacked seismic data to analyze the environmental impact on signal propagation distance. The results showed that:( 1) the airgun signal produced by bubble pulses,pressure pulses and the surface wave can be received by a portable station near the fixed field;( 2) the dominant frequency of a bubble at 5Hz or so can be received by both near-field stations and far-field stations,pressure pulses rapidly weaken and the dominant frequency bands get narrower as epicentral distance increases;( 3) the longest spread distance of signal is 260 km,the nearest is 180 km,and the signal can travel further in the evening.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

Comprehensive Detection and Analysis of Defects in Foundation Pile of Bridge

In the process of piling ,there are many various defects in foundation pile of bridge such as mud-bearing,sediment-bearing, isolation, honeycomb, broken piles, and so on, showing physical and mechanical features of low-density and low-intensity. In fact, by using the comprehensive detection of acoustic transmission method, the reflected wave method as well as drill coring sample method, and the rational utilization of engineering geological condition in field, the characteristics, size and location of common defects of foundation pile of bridge can be accurately detected and judged and the integrity of piles and the quality of concrete can be impersonally estimated.comprehensive detecting and analyzing methods on this kind of piles are introduced briefly. The physical characters of defects and basic features of detecting curves and their corresponding relation are emphasized, and causes are analyzed in in detail in this paper.

An Improved Randomized Circle Detection Algorithm Using in Printed Circuit Board Locating Mark

This paper presents an improved Randomized Circle Detection (RCD) algorithm with the characteristic of circularity to detect randomized circle in images with complex background, which is not based on the Hough Transform. The experimental results denote that this algorithm can locate the circular mark of Printed Circuit Board (PCB).