Aerodynamic system instability identification with sample entropy algorithm based on feature extraction

Based on the sample entropy algorithm in nonlinear dynamics,an improved sample entropy method is proposed in the aerodynamic system instability identification for the stall precursor detection based on the nonlinear feature extraction algorithm in an axial compressor.The sample entropy algorithm is an improved algorithm based on the approximate entropy algorithm,which quantifies the regularity and the predictability of data in time series.Combined with the spatial modes representing for the rotating stall in the circumferential direction,the recognition capacity of the sample entropy is displayed well on the detection of stall inception.The indications of rotating waves are extracted by the circumferential analysis from modal wave energy.The significant ascendant in the amplitude of the spatial mode is a pronounced feature well before the imminence of stall.Data processing with the spatial mode effectively avoids the problems of inaccurate identification of a single measuring point only depending on pressure.Due to the different selections of similarity tolerance,two kinds of sample entropy are obtained.The properties of the development process of the identification model show obvious mutation phenomena at the boundary of instability,which reveal the inherent characteristic in aerodynamic system.Then the dynamic difference quotient is computed according to the difference quotient criterion,after the smooth management by discrete wavelet.The rapid increase of difference quotient can be regarded as a significant feature of the system approaching the flow instability.It is proven that based on the principle of sample entropy algorithm,the nonlinear characteristic of rotating stall can be well described.The inception can be suggested by about 12-68 revolutions before the stall arrival.This prediction method presenting is accounted for the nonlinearity of the complex flow in stall,which is in a view of data fusion system of pressure for the spatial mode tracking.

Large Thinned Array Design Based on Multi-objective Cross Entropy Algorithm

To consider multi-objective optimization problem with the number of feed array elements and sidelobe level of large antenna array, multi-objective cross entropy(CE) algorithm is proposed by combining fuzzy c-mean clustering algorithm with traditional cross entropy algorithm, and specific program flow of the algorithm is given.Using the algorithm, large thinned array(200 elements) given sidelobe level(-10,-19 and-30 d B) problem is solved successfully. Compared with the traditional statistical algorithms, the optimization results of the algorithm validate that the number of feed array elements reduces by 51%, 11% and 6% respectively. In addition, compared with the particle swarm optimization(PSO) algorithm, the number of feed array elements from the algorithm is more similar, but the algorithm is more efficient.

Research on Concrete Beam Crack Recognition Algorithm Based on Block Threshold Value Image Processing

To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven ill umination and complex surface color of concrete structure,this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system.The steps in this research are as follows:1.The crack digital images of concrete specimens with typical fea-tures were collected by using the Actis system of KURABO Co,Ltd,of Japan in the concrete beam bending test.2.The images are segmented into blocks to dis-tinguish backgrounds of different grayscale.3.The max imum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented.4.Segmentation is made to the image with 2D max imum entropy threshold segmentation method to obtain the binary image,and the target image can be obtained by screening the connected domain features of the binary image.Results have shown that compared with other algo-rithms,the proposed method can effectively decrease the image over-segmentation and under segmentation rates,highlight the characteristics of the target cracks,solve the problems of excessive difference between the identified length and actual length of cracks caused by background gray level change and uneven ilumnination,and effectively improve the recognition accuracy of bridge concrete cracks.

Dynamic Threshold-Based Approach to Detect Low-Rate DDoS Attacks on Software-Defined Networking Controller

The emergence of a new network architecture,known as Software Defined Networking(SDN),in the last two decades has overcome some drawbacks of traditional networks in terms of performance,scalability,reliability,security,and network management.However,the SDN is vulnerable to security threats that target its controller,such as low-rate Distributed Denial of Service(DDoS)attacks,The low-rate DDoS attack is one of the most prevalent attacks that poses a severe threat to SDN network security because the controller is a vital architecture component.Therefore,there is an urgent need to propose a detection approach for this type of attack with a high detection rate and low false-positive rates.Thus,this paper proposes an approach to detect low-rate DDoS attacks on the SDN controller by adapting a dynamic threshold.The proposed approach has been evaluated using four simulation scenarios covering a combination of low-rate DDoS attacks against the SDN controller involving(i)a single host attack targeting a single victim;(ii)a single host attack targeting multiple victims;(iii)multiple hosts attack targeting a single victim;and(iv)multiple hosts attack targeting multiple victims.The proposed approach’s average detection rates are 96.65%,91.83%,96.17%,and 95.33%for the above scenarios,respectively;and its average false-positive rates are 3.33%,8.17%,3.83%,and 4.67%for similar scenarios,respectively.The comparison between the proposed approach and two existing approaches showed that it outperformed them in both categories.

Validation of Transverse Phase Space Tomography at TRIUMF

Knowledge of the phase space density distribution in details is useful to understand subsequent evolution of the charged particle beam in a transport line.This makes the beam tomography very useful in the application for beam diagnostics.This application is not limited by the beam energy,as opposed to the emittance scanner.This paper presented the simulations and measurements we undertook in TRIUMF beam-lines to validate the maximum entropy(MENT)technique for the tomographic reconstruction of beam density distribution in the 2-dimensional transverse phase space.Beam profiles were taken with a single wire scanner while changing an upstream quadrupole’s strength.Moreover,the phase space plots were directly measured with emittance scanner.A close comparison was made on the resulting phase space density distribution and the emittance value at the same location of the beam-line.They show good agreement.

Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear

In the last years, digital image processing and analysis are used for computer assisted evaluation of semen quality with therapeutic goals or to estimate its fertility by means of spermatozoid motility and morphology. Sperm morphology is assessed routinely as part of standard laboratory analysis in the diagnosis of human male infertility. Nowadays assessments of sperm morphology are mostly done based on subjective criteria. In order to avoid subjectivity, numerous studies that incorporate image analysis techniques in the assessment of sperm morphology have been proposed. The primary step of all these methods is segmentation of sperm’s parts. In this paper, we have proposed a new method for segmentation of sperm’s Acrosome, Nucleus, Mid-piece and identification of sperm’s tail through some points which are placed on the sperm’s tail, accurately. These estimated points could be used to verify the morphological characteristics of sperm’s tail such as length, shape and etc. At first, sperm’s Acrosome, Nucleus and Mid-piece are segmented through a method based on a Bayesian classifier which utilizes the entropy based expectation–maximization (EM) algorithm and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the apriori probability of each class. Then, a pixel at the end of sperm’s Mid-piece, is selected as an initial point. To find other pixels which are placed on the sperm’s tail, structural similarity index (SSIM) is used in an iterative scheme. In order to stop the algorithm automatically at the end of sperm’s tail, local entropy is estimated and used as a feature to determine if a point is located on the sperm’s tail or not. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the Accuracy, Sensitivity and Specificity were calculated.

A stall diagnosis method based on entropy feature identification in axial compressors

A stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors.The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally conducted for two different types of compressor stall diagnoses.A collection of time‐resolved pressure sensors is mounted circumferentially and along the chord direction to measure the dynamic pressure on the casing.Results show that the stall and prestall precursor embedded in the dynamic pressures are identified through nonlinear feature perturbation extraction using the entropy feature extraction algorithm.Further analysis demonstrates that the prestall precursor with the peak entropy value is related to the unsteady tip leakage flow for the spike‐type stall diagnosis.The modal wave inception with increasing amplitude is identified by the considerable increase of the entropy value.The flow field in the tip region indicates that the modal wave corresponds to the flow separation in the suction side of the rotor blade.The warning time is 100–300 rotor revolutions for both types of stall diagnoses,which is beneficial for stall control in different axial compressors.Moreover,a parametric study of the embedding dimension m,similar tolerance n,similar radius r,and data length N in the fuzzy entropy method is conducted to determine the optimal parameter setting for stall diagnosis.The stall warning based on the entropy feature extraction algorithm provides a new stall diagnosis approach in the axial compressor with different stall types.This stall warning can also be adopted as an online stability monitoring index when using the concept of active stall control.

Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation.However,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering process.To address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is proposed.SLSP-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale features.Automatic matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood structure.An optimized density peak algorithm is adopted for MAFC.Based on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional clustering.Finally,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result.Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.