2,638 research outputs found
A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
In this paper, we employ Probabilistic Neural Network (PNN) with image and
data processing techniques to implement a general purpose automated leaf
recognition algorithm. 12 leaf features are extracted and orthogonalized into 5
principal variables which consist the input vector of the PNN. The PNN is
trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater
than 90%. Compared with other approaches, our algorithm is an accurate
artificial intelligence approach which is fast in execution and easy in
implementation.Comment: 6 pages, 3 figures, 2 table
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind
power generator.ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΌΡΠ»ΡΡΠΈΠΊΠ»Π°ΡΡΠΎΠ²ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°ΡΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ Π½Π°Π±ΠΎΡΠ° Π΄Π²ΠΎΠΈΡΠ½ΡΡ
ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΈ ΠΈΡ
ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΈ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΌΠ°ΡΠΈΠ½Π° ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ² Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠΌ Π²ΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΊΠΎΠ΄ΠΎΠ² ΠΈΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΡΠΈΠ±ΠΎΠΊ(ECOC-SVM) Ρ ΡΠ΅Π»ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°ΡΡ ΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°ΡΡ ΡΠ°ΠΊΠΈΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ, ΠΊΠ°ΠΊ Π³Π°ΡΠΌΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΠΊΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ ΠΈ ΡΠΊΠ°ΡΠΎΠΊ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ Π²Π΅ΡΡΠΎΠ²ΡΡ
ΡΠ»Π΅ΠΊΡΡΠΎΡΡΠ°Π½ΡΠΈΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ. Π‘Π½Π°ΡΠ°Π»Π° Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΡΠΎΠΊΠ° Π½Π΅ΡΠΈΠΌΠΌΠ΅ΡΡΠΈΡΠ½ΠΎΠΉ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΡΡΠ΅Ρ
ΡΠ°Π· Π΄Π»Ρ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ°Π·Π½ΠΎΡΡΠ½ΡΡ
Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΡΡΠΎΠ²Π½Π΅ΠΉ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ, Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠΎΡΠ»Π΅ ΡΡΠΎΠ³ΠΎ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ΅ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅ΡΡΡ Ρ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΡΡ ECOC-SVM Π΄Π»Ρ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ°. ΠΠ°ΠΊΠΎΠ½Π΅Ρ, ECOC-SVM ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΠΈ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΡΠ΅Ρ ΡΠΈΠΏ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ½Π΅ΡΠ³ΠΈΠΈ Π΄ΠΈΡΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π²Π΅ΠΉΠ²Π»Π΅Ρ-ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π°Π΅Ρ ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ 99,2% ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌΠΈ ΠΈ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΊΠ°ΠΆΠ΄ΠΎΠ΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ»Π΅ΠΊΡΡΠΎΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΈΠΌΠ΅Π΅Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠ΅ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ ΠΎΡ ΡΠΈΡΡΠΎ ΡΠΈΠ½ΡΡΠΎΠΈΠ΄Π°Π»ΡΠ½ΠΎΠΉ ΡΠΎΡΠΌΡ Π²ΠΎΠ»Π½Ρ, ΡΡΠΎ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠΈΠΏΠ° Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡ, Π³Π΅Π½Π΅ΡΠΈΡΡΠ΅ΠΌΠΎΠ³ΠΎ Π²Π΅ΡΡΠΎΠ²ΡΠΌ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΎΠΌ
Target-adaptive CNN-based pansharpening
We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight network which trains very fast. Leveraging on this latter property,
we propose a target-adaptive usage modality which ensures a very good
performance also in the presence of a mismatch w.r.t. the training set, and
even across different sensors. The proposed method, published online as an
off-the-shelf software tool, allows users to perform fast and high-quality
CNN-based pansharpening of their own target images on general-purpose hardware
High Resolution Linear Polarimetric Imaging for the Event Horizon Telescope
Images of the linear polarization of synchrotron radiation around Active
Galactic Nuclei (AGN) identify their projected magnetic field lines and provide
key data for understanding the physics of accretion and outflow from
supermassive black holes. The highest resolution polarimetric images of AGN are
produced with Very Long Baseline Interferometry (VLBI). Because VLBI
incompletely samples the Fourier transform of the source image, any image
reconstruction that fills in unmeasured spatial frequencies will not be unique
and reconstruction algorithms are required. In this paper, we explore
extensions of the Maximum Entropy Method (MEM) to linear polarimetric VLBI
imaging. In contrast to previous work, our polarimetric MEM algorithm combines
a Stokes I imager that uses only bispectrum measurements that are immune to
atmospheric phase corruption with a joint Stokes Q and U imager that operates
on robust polarimetric ratios. We demonstrate the effectiveness of our
technique on 7- and 3-mm wavelength quasar observations from the VLBA and
simulated 1.3-mm Event Horizon Telescope observations of Sgr A* and M87.
Consistent with past studies, we find that polarimetric MEM can produce
superior resolution compared to the standard CLEAN algorithm when imaging
smooth and compact source distributions. As an imaging framework, MEM is highly
adaptable, allowing a range of constraints on polarization structure.
Polarimetric MEM is thus an attractive choice for image reconstruction with the
EHT.Comment: 19 pages, 9 figures. Accepted for publication in ApJ. Imaging code
available at https://github.com/achael/eht-imaging
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