Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR

Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required.

Extraction Characteristics of Radio Frequency ICP Ion Source

An inductively coupled radio frequency ion source has been developed and its extraction characteristics measured. Beam current density up to 0.11 mA/ cm2 was obtained with argon at a rf discharge power of about 140 W. The dependences of ion beam on the discharge parameters such as rf source power, gas pressure and gas flow rate was studied.

Analytical and Numerical Model Confrontation for Transfer Impedance Extraction in Three-Dimensional Radio Frequency Circuits

3D chip stacking is considered known to overcome conventional 2D-IC issues, using through silicon vias to ensure vertical signal transmission. From any point source, embedded or not, we calculate the impedance spread out;our ultimate goal will to study substrate noise via impedance field method. For this, our approach is twofold: a compact Green function or a Transmission Line Model over a multi-layered substrate is derived by solving Poisson’s equation analytically. The Discrete Cosine Transform (DCT) and its variations are used for rapid evaluation. Using this technique, the substrate coupling and loss in IC’s can be analyzed. We implement our algorithm in MATLAB;it permits to extract impedances between any pair of embedded contacts. Comparisons are performed using finite element methods.

Analysis of active patents to investigate the frequency and patterns of Chinese herbal extract combinations claiming to treat heart disease

Objective:Using Chinese patents in force to investigate the frequency and patterns of Chinese herbal extract combinations claiming to treat heart disease.Methods:Patent documents were retrieved from the official website of the State Intellectual Property Office of the People’s Republic China.Cluster,frequency,and fuzzy cluster analyses were applied.Results:A high number of patents in force included high-frequency herbs such as Salvia miltiorrhiza,Panax ginseng,and Panax notoginseng,as well as high-frequency herbal families such as Araliaceae,Leguminosae,Labiatae,and Umbelliferae.Herb pairs such as P.ginsengþOphiopogon japonicus,S.miltiorrhizaþDalbergia odorifera,and P.ginsengþSchisandra chinensis are also commonly used,as well as herbal family pairs such as AraliaceaeþLiliaceae,LauraceaeþLeguminosae,and AraliaceaeþSchisandraceae.Traditional treatment principles for preventing and treating heart diseases was most-commonly based on simultaneously treating the liver and heart and treating the lung and spleen secondarily for choosing herbal combinations.Conclusion:Most of the high-frequency Chinese herbs in the patents investigated belong to the high-frequency herbal families,and herb pairs were commonly selected to coincide with the commonly-used herbal family pairs.Low-frequency Chinese herbs were also used,but generally belonged to the high-frequency herbal families,and were therefore similar to the highfrequency herbs in terms of traditional categories of taste and channel entered.The results reflect the use of traditional principles of formula composition,and suggest that these principles may indeed be an effective guide for further research and development of Chinese herbal extract combinations to prevent and treat heart diseases.

ESPRIT-Based Feature Extraction of Helicopter Acoustic Signal

Aim To extract harmonic frequencies of helicopter acoustic signal as features for hel icopter identification. Methods Estimation of signal parameters via rotational invariance techniques(ESPRIT) was selected to extract harmonic frequencies from really measured helicopter acoustic signal and an algorithm based on the SVD TLS was used. Results ESPRIT correctly extracted harmonic frequencies of helicopter using the data of limited length under the variousflight conditions. Conclusion ESPRIT is an effective method of extracting harmonic frequencies and using harmonic frequencies of helicopter acoustic signal to recognize helicopter is feasible.

Fractional Envelope Analysis for Rolling Element Bearing Weak Fault Feature Extraction

The bearing weak fault feature extraction is crucial to mechanical fault diagnosis and machine condition monitoring. Envelope analysis based on Hilbert transform has been widely used in bearing fault feature extraction. A generalization of the Hilbert transform, the fractional Hilbert transform is defined in the frequency domain, it is based upon the modification of spatial filter with a fractional parameter, and it can be used to construct a new kind of fractional analytic signal. By performing spectrum analysis on the fractional envelope signal, the fractional envelope spectrum can be obtained. When weak faults occur in a bearing, some of the characteristic frequencies will clearly appear in the fractional envelope spectrum. These characteristic frequencies can be used for bearing weak fault feature extraction. The effectiveness of the proposed method is verified through simulation signal and experiment data. © 2017 Chinese Association of Automation.

Chromatic processing for feature extraction of PD-induced UHF signals in GIS

Partial discharge(PD) detection is an effective means of discovering insulation faults in gas-insulated switchgear(GIS). One of the most extensively used methods in PD detection has historically been the ultrahigh frequency(UHF) method. This study evaluates the chromatic processing methodology and its key factors for feature extraction of UHF signals in GIS. Three types of artificial defects are installed in the GIS tank at 0°, 90°, and 180°, respectively. The features of the UHF signals are extracted in the chromatic space, and PD discrimination of the defects is achieved. The influences of processors are studied before the feature selections are suggested. The time-stepping method is proposed to determine the rules of UHF signal frequency characteristics that vary with time. Finally, the process and options of the chromatics-inspired methodology are summarized.

Generator Unit Fault Diagnosis Using the Frequency Slice Wavelet Transform Time-frequency Analysis Method

为了提取有效的故障特征，提出了基于频率切片小波变换时频分解的故障特征分离提取方法。先对信号进行频率切片小波变换获取其时频分布，然后根据信号的能量分布特点选择时频区域，再以较高的时频分辨率对选择的时频区域进一步细化分析，以突出隐含在信号中的时频特征，在此基础上分割出含有故障特征时频区域，再通过滤波和逆变换重构分离出有效的故障特征。仿真实验和工程应用表明，这种方法可从噪声信号中分离出有效的特征分量，在发电机组故障特征提取时取得了较好的效果。

Feature Extraction and Recognition for Rolling Element Bearing Fault Utilizing Short-Time Fourier Transform and Non-negative Matrix Factorization

Due to the non-stationary characteristics of vibration signals acquired from rolling element bearing fault, thc time-frequency analysis is often applied to describe the local information of these unstable signals smartly. However, it is difficult to classitythe high dimensional feature matrix directly because of too large dimensions for many classifiers. This paper combines the concepts of time-frequency distribution（TFD） with non-negative matrix factorization（NMF）, and proposes a novel TFD matrix factorization method to enhance representation and identification of bearing fault. Throughout this method, the TFD of a vibration signal is firstly accomplished to describe the localized faults with short-time Fourier transform（STFT）. Then, the supervised NMF mapping is adopted to extract the fault features from TFD. Meanwhile, the fault samples can be clustered and recognized automatically by using the clustering property of NMF. The proposed method takes advantages of the NMF in the parts-based representation and the adaptive clustering. The localized fault features of interest can be extracted as well. To evaluate the performance of the proposed method, the 9 kinds of the bearing fault on a test bench is performed. The proposed method can effectively identify the fault severity and different fault types. Moreover, in comparison with the artificial neural network（ANN）, NMF yields 99.3% mean accuracy which is much superior to ANN. This research presents a simple and practical resolution for the fault diagnosis problem of rolling element bearing in high dimensional feature space.

OPERATING POINT AND STRIPPING EXTRACTION CALCULATIONS FOR CYCLONE 10/5

By using the magnetic field map, equilibrium orbit, operating point of v r and v z and beam trajectories after stripper for Cyclone 10/5 have been calculated. The positions of two strippers and two targets have been determined. The numerical simulation is consistent with experimental tests.

Micro-Doppler Parameter Estimation Method Based on Compressed Sensing

A micro-Doppler parameter estimation method based on compressed sensing theory is proposed in this paper.The micro-Doppler parameter estimation algorithm was improved for micro-motion targets with translation in this paper.Relatively ideal micro-Doppler parameter estimation results were obtained.The proposed micro-Doppler parameter estimation was compared with the traditional micro-Doppler parameter estimation algorithm.Requirements for return signal length were analyzed with this new algorithm and its performance was also analyzed in various environments with different SNR.

Numerical research of a 2D axial symmetry hybrid model for the radio-frequency ion thruster

Since the high efficiency discharge is critical to the radio-frequency ion thruster(RIT), a 2D axial symmetry hybrid model has been developed to study the plasma evolution of RIT. The fluid method and the drift energy correction of the electron energy distribution function(EEDF) are applied to the analysis of the RIT discharge. In the meantime, the PIC-MCC method is used to investigate the ion beam current extraction character for the plasma plume region. The beam current simulation results, with the hybrid model, agree well with the experimental results, and the error is lower than 11%, which shows the validity of the model. The further study shows there is an optimal ratio for the radio-frequency(RF) power and the beam current extraction power under the fixed RIT configuration. And the beam extraction efficiency will decrease when the discharge efficiency beyond a certain threshold(about 87 W). As the input parameters of the hybrid model are all the design values, it can be directly used to the optimum design for other kinds of RITs and radio-frequency ion sources.

Extraction of novel features for emotion recognition

Hilbert-Huang transform method has been widely utilized from its inception because of the superiority in varieties of areas. The Hilbert spectrum thus obtained is able to reflect the distribution of the signal energy in a number of scales accurately. In this paper, a novel feature called ECC is proposed via feature extraction of the Hilbert energy spectrum which describes the distribution of the instantaneous energy. The experimental results conspicuously demonstrate that ECC outperforms the traditional short-term average energy. Combination of the ECC with mel frequency cepstral coefficients （MFCC） delineates the distribution of energy in the time domain and frequency domain, and the features of this group achieve a better recognition effect compared with the feature combination of the short-term average energy, pitch and MFCC. Afterwards, further improvements of ECC are developed. TECC is gained by combining ECC with the teager energy operator, and EFCC is obtained by introducing the instantaneous frequency to the energy. In the experiments, seven status of emotion are selected to be recognized and the highest recognition rate 83.57% is achieved within the classification accuracy of boredom reaching 100%. The numerical results indicate that the proposed features ECC, TECC and EFCC can improve the performance of speech emotion recognition substantially.

Feature Extraction for Audio Classification of Gunshots Using the Hartley Transform

In audio classification applications, features extracted from the frequency domain representation of signals are typically focused on the magnitude spectral content, while the phase spectral content is ignored. The conventional Fourier Phase Spectrum is a highly discontinuous function;thus, it is not appropriate for feature extraction for classification applications, where function continuity is required. In this work, the sources of phase spectral discontinuities are detected, categorized and compensated, resulting in a phase spectrum with significantly reduced discontinuities. The Hartley Phase Spectrum, introduced as an alternative to the conventional Fourier Phase Spectrum, encapsulates the phase content of the signal more efficiently compared with its Fourier counterpart because, among its other properties, it does not suffer from the phase ‘wrapping ambiguities’ introduced due to the inverse tangent function employed in the Fourier Phase Spectrum computation. In the proposed feature extraction method, statistical features extracted from the Hartley Phase Spectrum are combined with statistical features extracted from the magnitude related spectrum of the signals. The experimental results show that the classification score is higher in case the magnitude and the phase related features are combined, as compared with the case where only magnitude features are used.

STATISTICAL FEATURE OF PITCH FREQUENCY DISTRIBUTIONS FOR OBUST SPEAKER IDENTIFICATION

This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Frequency Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead of simply taking one kind of features into account.Experimental results indicate that the hybrid approach can give outstanding improvement for text-independent speaker identification under noisy environments corrupted by AWGN.

The Influence of Low Frequency Electromagnetic Field （LF EMF） on the Agricultural Crops Seeds Germination

This research is about changing rate of seeds germination by low frequencies electromagnetic field. The method of resonant frequencies to increase seeds germination were developed and patented by authors of this research. The idea of the means is to research a technique to changing a capacitor component of full resistance for extracts of wheat and sunflower seeds under the influence of low frequency electromagnetic field （LF EMF）. After the green sprouting experimentin cups of Petri, it was found that most frequencies found by this method are good, and the best results increasing germination of wheat of a grade Krasnodar 99 were after treatment with 31 Hz, for sunflower of a grade Buzuluk 16 Hz.

Radar Signal Intra-Pulse Feature Extraction Based on Improved Wavelet Transform Algorithm

With the new system radar put into practical use, the characteristics of complex radar signals are changing and developing. The traditional analysis method of one-dimensional transformation domain is no longer applicable to the modern radar signal processing, and it is necessary to seek new methods in the two-dimensional transformation domain. The time-frequency analysis method is the most widely used method in the two-dimensional transformation domain. In this paper, two typical time-frequency analysis methods of short-time Fourier transform and Wigner-Ville distribution are studied by analyzing the time-frequency transform of typical radar reconnaissance linear frequency modulation signal, aiming at the problem of low accuracy and sen-sitivity to the signal noise of common methods, the improved wavelet transform algorithm was proposed.

Efficacy of a Kiwifruit Extract (PhenActivTM) on Gastrointestinal Tract Function: A Randomised Double-Blind Placebo-Controlled Study

Objective: Gastrointestinal (GI) discomfort is experienced by millions of people every day. This study aimed to evaluate the effect of PhenActivTM, a novel green kiwifruit extract, on gastrointestinal tract (GIT) function in otherwise healthy adults. Methods: 41 healthy adults with mild GI discomfort were enrolled in this double-blind, randomized, placebo-controlled study. Participants were randomized to either take 3.0 g/day of PhenActivTM or a placebo for 6 weeks. Interviews were conducted at baseline, week 3 and week 6, with participants completing questionnaires regarding GI symptoms. Frequency of bowel movements was self-recorded daily. Results: There were no differences in daily and weekly defecation frequency and stool characteristics in either group. The active and placebo groups significantly improve GSRS scores (p , only the active group had a significant improvement in the IBSSS and PAC-QOL scores (p < 0.05) from baseline. Neither group had changes in sleep quality, quality of life and fatigue, plasma zonulin concentrations or macular pigment optical density scores. The product was well tolerated with no GI disturbances or adverse events being reported. Conclusion: Supplementation of 3.0 g/day of PhenActivTM for 6 weeks did not improve defecation frequency or stool composition in healthy adults, but did improve perceived symptoms of GIT function, including symptoms of functional GIT disorders, IBS and constipation. The product was well tolerated and future trials investigating higher doses with more participants and/or a different population would be beneficial.

基于多频特征和纹理增强的轻量化图像超分辨率重建

现有基于卷积神经网络主要关注图像重构的精度,忽略了过度参数化、特征提取不充分以及计算资源浪费等问题。针对上述问题,提出了一种轻量级多频率特征提取网络(MFEN),设计了轻量化晶格信息交互结构,利用通道分割和多模式卷积组合减少参数量;通过分离图像的低频、中频以及高频率信息后进行特征异构提取,提高网络的表达能力和特征区分性,使其更注重纹理细节特征的复原,并合理分配计算资源。此外,在网络内部融合局部二值模式(LBP)算法用于增强网络对纹理感知的敏感度,旨在进一步提高网络对细节的提取能力。经验证,该方法在复杂度和性能之间取得了良好的权衡,即实现轻量有效提取图像特征的同时重建出高分辨率图像。在Set5数据集上的2倍放大实验结果最终表明,相比较于基于卷积神经网络的图像超分辨率经典算法(SRCNN)和较新算法(MADNet),所提方法的峰值信噪比(PSNR)分别提升了1.31 dB和0.12 dB,参数量相比MADNet减少了55%。

基于自适应窗口与压缩幅值的瞬时转频估计

针对变转速工况下轴承信号的时频分布能量发散、噪声和谐波干扰强导致其转频难以获取的问题,提出一种基于自适应窗口和压缩幅值的瞬时转频估计方法。首先,通过自适应窗口在频率轴方向搜索出脊线窗口避免噪声和谐波的干扰;其次,在脊线窗口内用压缩幅值方法集中发散的脊线能量;然后,用惩罚函数法提取脊线,实现转频的精确估计;最后,根据采用轴承实验台收集的数据验证了所提出方法的有效性和鲁棒性。结果表明,相比于传统方法,采用所提方法估计瞬时转动频率使误差降低约8%。