Classification of rotor blade number of rotor targets micro-motion signal based on CNN

In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the problem of under-decomposition or over-decomposition caused by unreasonable choice of decomposition layer, it is more accurate than detrended fluctuation analysis (DFA) and empirical mode decomposition (EMD). Finally, the strongly correlated modes and weakly correlated modes are judged by calculating the Mahalanobis distance between the band-limited intrinsic mode functions (BLIMFs) and the original signal, which is more robust than the correlation judgment methods such as computing cross-correlation, Euclidean distance, Bhattachryya distance and Hausdorff distance. After the weak correlation modes are eliminated, the signal is reconstructed locally, and the separation of the micro-motion signal is realized. The experimental results show that the proposed method can filter out the strong clutter component and the fuselage component from radar echo more effectively than the local mean decomposition (LMD), empirical mode decomposition and moving target indicator (MTI) filter.

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Separation and feature extraction of micro‐motion signal of ballistic target

The Journal of Engineering ◽

10.1049/tje2.12083 ◽

2021 ◽

Author(s):

Yuxi Li ◽

Cunqian Feng ◽

Xuguang Xu ◽

Lixun Han ◽

Dayan Wang

Keyword(s):

Feature Extraction ◽

Motion Signal ◽

Micro Motion

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Rotor blade optimization and flight testing of a small UAV rotorcraft

Journal of Unmanned Vehicle Systems ◽

10.1139/juvs-2017-0005 ◽

2019 ◽

Vol 7 (4) ◽

pp. 325-344 ◽

Cited By ~ 1

Author(s):

Mark Kotwicz Herniczek ◽

Dustin Jee ◽

Brian Sanders ◽

Daniel Feszty

Keyword(s):

Power Consumption ◽

Rotor Blade ◽

Reynolds Numbers ◽

Rotational Frequency ◽

Minor Effect ◽

Blade Number ◽

Blade Optimization ◽

Dynamics Simulations ◽

A Minor ◽

Reduced Power Consumption

Rotor blade optimization with blade airfoil Reynolds numbers between 100 000 and 500 000 — characteristic of small single-rotor unmanned aerial vehicles (UAV) — was performed for hover using blade element momentum theory (BEMT) and demonstrated via flight tests. BEMT was used to test various airfoil profiles and rotor blade shapes using airfoil data from 2D computational fluid dynamics simulations with Reynolds numbers representative of the blade elements. Selected blade designs were manufactured and flight tested on a Blade 600X single main-rotor UAV (671 mm blade radius) to validate the theoretical results. The parameters considered during the optimization process were the rotor frequency, radius, taper ratio, twist, chord length, airfoil profile, and blade number. The best of the improved blade designs increased the figure of merit, a measure of rotor efficiency, from 0.31 to 0.68 and reduced power consumption by 54%. Reducing the rotational frequency accounted for 45% of the improvement in power consumption, while the taper ratio and blade number accounted for 25% and 17%, respectively. The blade twist and airfoil profile only had a minor effect on the power consumption, contributing 7% and 6% to the improvement. The rotor diameter and root chord were kept identical to the original rotor and hence had no contribution. The presented results could serve as useful guidelines to single-rotor UAV manufacturers and operators for increasing endurance and payload capabilities.

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Classification of Space Targets with Micro-motion Based on Deep CNN

2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT) ◽

10.1109/iceict.2019.8846441 ◽

2019 ◽

Cited By ~ 1

Author(s):

Yizhe Wang ◽

Cunqian Feng ◽

Yongshun Zhang ◽

Qichao Ge

Keyword(s):

Deep Cnn ◽

Micro Motion

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Note on the spectral classification of carbon stars in the photographic infrared region

Symposium - International Astronomical Union ◽

10.1017/s0074180900053080 ◽

1966 ◽

Vol 24 ◽

pp. 21-23

Author(s):

Y. Fujita

Keyword(s):

Infrared Region ◽

Spectral Classification ◽

Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

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Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100068096 ◽

1970 ◽

Vol 28 ◽

pp. 220-221

Author(s):

Gerald Fine ◽

Azorides R. Morales

Keyword(s):

Cardiac Myxoma ◽

Osteogenic Sarcoma ◽

Diagnostic Value ◽

Soft Tissue Tumors ◽

Amelanotic Melanoma ◽

Growth Patterns ◽

Light Microscopic Level ◽

Mesenchymal Tumors ◽

Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

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Ultrastructure of mesotheliomas

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100110775 ◽

1984 ◽

Vol 42 ◽

pp. 98-101

Author(s):

Irving Dardick

Keyword(s):

Electron Microscopy ◽

Latent Period ◽

Spindle Cell ◽

Spindle Cell Sarcoma ◽

Metastatic Adenocarcinoma ◽

Cell Sarcoma ◽

Cytological Features ◽

Industrial Use ◽

Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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