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Multi-scene design analysis of integrated energy system based on feature extraction algorithm

ENERGY REPORTS 2022年 8卷 466-476页

作者： Huang, Sihua Ali, Noor Azizi Mohd Shaari, Nazlina Noor, Mohd Sallehuddin Mat Univ Putra Malaysia Fac Design & Architecture Seri Kembangan 43400 Selangor Malaysia Museum Chinese Culture Tiger Designed Ceram Wuhan 430073 Hubei Peoples R China

The specific analysis of a region's energy needs to model and simulate various types of energy, quantify energy information, and clearly and intuitively reflect the energy situation and energy potential of a region. In this paper, according to the input attributes of various energy load forecasting models, the correlation degree of main control factors is analyzed, and the influence degrees of environmental factors on electric power, gas, heating and cooling loads are obtained respectively. Then, convolution neural network is used to extract the feature vectors of comprehensive environmental factors. Finally, according to the given feature vectors, the feature clustering models of various energy loads are established by using K-means clustering algorithm, and the load forecasting results of multi-energy systems are obtained. The errors between the predicted results of various energy loads and the actual load records in the study area are 1.105%, 1.876%, 3.102% and 2.834%, respectively. The load forecasting method based on feature clustering proposed in this paper can effectively extract the influence of different environmental factors on the load forecasting results, and get more accurate load forecasting results. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： feature extraction algorithm Integrated energy system Scene design

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An Auditory-Based feature extraction algorithm for Robust Speaker Identification Under Mismatched Conditions

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2011年第6期19卷 1791-1801页

作者： Li, Qi Huang, Yan Li Creat Technol Inc Florham Pk NJ 07932 USA

An auditory-based feature extraction algorithm is presented. We name the new features as cochlear filter cepstral coefficients (CFCCs) which are defined based on a recently developed auditory transform (AT) plus a set of modules to emulate the signal processing functions in the cochlea. The CFCC features are applied to a speaker identification task to address the acoustic mismatch problem between training and testing environments. Usually, the performance of acoustic models trained in clean speech drops significantly when tested in noisy speech. The CFCC features have shown strong robustness in this kind of situation. In our experiments, the CFCC features consistently perform better than the baseline MFCC features under all three mismatched testing conditions-white noise, car noise, and babble noise. For example, in clean conditions, both MFCC and CFCC features perform similarly, over 96%, but when the signal-to-noise ratio (SNR) of the input signal is 6 dB, the accuracy of the MFCC features drops to 41.2%, while the CFCC features still achieve an accuracy of 88.3%. The proposed CFCC features also compare favorably to perceptual linear predictive (PLP) and RASTA-PLP features. The CFCC features consistently perform much better than PLP. Under white noise, the CFCC features are significantly better than RASTA-PLP, while under car and babble noise, the CFCC features provide similar performances to RASTA-PLP.

关键词： Auditory-based features automatic speaker recognition (ASR) cochlea feature extraction algorithm robust speaker recognition speaker identification

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Zone Based Hybrid feature extraction algorithm for Handwritten Numeral Recognition of South Indian Scripts

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2nd International Conference on Contemporary Computing

作者： Rajashekararadhya, S. V. Ranjan, P. Vanaja Anna Univ Coll Engn Dept Elect & Elect Engn Madras 600025 Tamil Nadu India

ISBN: (纸本)9783642035463

India is a multi-lingual multi script Country, where eighteen official scripts are accepted and have over hundred regional languages. In this paper we propose a zone based hybrid feature extraction algorithm scheme towards the recognition of off-line handwritten numerals of south Indian scripts. The character centroid is computed and the image (character/numeral) is further divided in to n equal zones. Average distance and Average angle from the character centroid to the pixels present in the zone are computed (two features). Similarly zone centroid is computed (two features). This procedure is repeated sequentially for all the zones/grids/boxes present in the numeral image. There could be some zones that are empty, and then the Value of that particular zone image Value in the feature vector is zero. Finally 4*n Such features are extracted. Nearest neighbor classifier is used for subsequent classification and recognition Purpose. We obtained 97.55%, 94%, 92.5% and 95.2% recognition rate for Kannada, Telugu, Tamil and Malayalam numerals respectively.

关键词： Handwritten Character Recognition feature extraction algorithm Nearest Neighbor Classifier Indian scripts

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Detection and classification of disturbances in the islanded micro-grid by using wavelet transformation and feature extraction algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第18期2019卷 5284-5286页

作者： Wang, Yunqi Raza, Ahmed Mohammed, Faisal Parvez Ravishankar, Jayashri Phung, Toan Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Digital signal processing (DSP) methods have been used by many researchers for detection and classification of transient disturbances because of their fast and powerful abilities to recognise waveform distortions. However, some DSP methods such as the wavelet transformation (WT) show less accuracy when applied to noisy real data. In this study, disturbance features are extracted in the wavelet domain based on the WT levels. Moreover, a new feature extraction algorithm namely normalised Renyi entropy with the signal energy is applied. This algorithm has been proven to be effective and robust for noisy signals. However, their application in power systems has not yet been tested. Using a laboratory setup of an islanded micro-grid, experimental results validate the efficacy of the wavelet-based normalised Renyi entropy in the detection and classification of four disturbance types (voltage sag, interruption, harmonics, mixture of harmonics and sag).

关键词： Computer vision and image processing techniques disturbance features Biology and medical computing disturbance types wavelet transforms noisy signals Signal processing and detection wavelet transformation Distribution networks Digital signal processing noisy real data feature extraction DSP methods detection classification feature extraction algorithm transient disturbances digital signal processing methods fast abilities medical signal processing signal energy wavelet domain distributed power generation entropy islanded microgrid wavelet-based normalised Renyi entropy Integral transforms power supply quality waveform distortions digital signal processing chips signal processing powerful abilities power distribution faults Distributed power generation power systems signal classification

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Safety Protection Technology of Power Monitoring System Based on feature extraction algorithm

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Distributed Generation and Alternative Energy Journal 2022年第2期37卷 311-326页

作者： Xiangbei, Che Yuhong, Ouyang Wenqian, Kang Jing, Su Shenzhen Power Supply Bureau Co. Ltd. Guangdong Shenzhen518000 China

The network security protection technology of power monitoring systems is of great significance. Aiming at the power network monitoring and protection technology problem, the paper proposes an active monitoring and protection strategy based on a feature extraction algorithm. The algorithm can calculate the transfer degree of security incidents based on evidence theory. First, the paper obtains a specific state transition diagram based on the security topology of a generalized random power communication network. Then, we analyze the relationship between power system information security and engineering security based on the system’s operating results and feature extraction algorithms. The experimental results demonstrate the rapid effectiveness of this method. © 2021 River Publishers.

关键词： evidence theory feature extraction algorithm Power monitoring network security protection technology

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CONVERGENCE ANALYSIS OF LOCAL feature-extraction algorithmS

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NEURAL NETWORKS 1992年第2期5卷 229-240页

作者： HORNIK, K KUAN, CM UNIV ILLINOIS DEPT ECONURBANAIL 61801 VIENNA TECH UNIV INST STAT & WAHRSCHEINLICHKEITSTHEORIEA-1040 VIENNAAUSTRIA

We investigate the asymptotic behavior of a general class of on-line Principal Component Analysis (PCA) learning algorithms, focusing our attention on the analysis of two algorithms which have recently been proposed and are based on strictly local learning rules. We rigorously establish that the behavior of the algorithms is intimately related to an ordinary differential equation (ODE) which is obtained by suitably averaging over the training patterns, and study the equilibria of these ODEs and their local stability properties. Our results imply, in particular, that local PCA algorithms should always incorporate hierarchical rather than more competitive, symmetric decorrelation, for reasons of superior performance of the algorithms.

关键词： PRINCIPAL COMPONENT ANALYSIS feature extraction algorithm HEBBIAN LEARNING

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Neural network-based strategies for automatically diagnosing of COVID-19 from X-ray images utilizing different feature extraction algorithms

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023年第1期12卷 1-30页

作者： Prity, Farida Siddiqi Nath, Nishu Nath, Antara Uddin, K. M. Aslam Noakhali Sci & Technol Univ Dept Informat & Commun Engn Noakhali Bangladesh Montana State Univ Gianforte Sch Comp Bozeman MT 59717 USA Mainamoti Med Coll Cumilla Bangladesh

The COVID-19 pandemic has had an obliterating impact on the health and well-being of the worldwide populace. It has recently become one of the most severe and acute diseases and has spread globally. Therefore, an automated detection system should be implemented as the fastest diagnostic option to control the spread of COVID-19. This study aims to introduce neural network-based strategies for classifying and detecting COVID-19 early through image processing utilizing X-ray images. Despite the extensive use of directly fed X-ray images into the classifier, there is a lack of comparative analysis between the feature-based system and the direct imaging system in the classification of COVID-19 to evaluate the efficiency of feature extraction methods. Therefore, the proposed system represents introductory experiments of feature-based system using image feature extraction algorithms [Texture, Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Dependence Matrix (GLDM), Fast Fourier Transform (FFT), and Discrete Wavelet Transform (DWT)], Dimensionality Reduction algorithms (Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Sparse Autoencoder, and Stacked Autoencoder), feature Selection algorithms (Anova F-measure, Chi-square Test, and Random Forest), and Neural Networks [Feed-Forward Neural Network (FFNN) and Convolutional Neural Network (CNN)] on generated datasets consisting of Normal, COVID-19, and Pneumonia-infected chest X-ray images. Tenfold Cross-Validations are implemented during the classification process. A baseline model is created where no feature extraction algorithm is implemented. Accuracy, sensitivity, specificity, precision, and F-measure metrics are utilized to assess classification performance. Finally, a comprehensive comparison of the success of the feature-based system (feature extraction algorithms, Dimensionality Reduction algorithms, and feature Selection algorithms) in COVID-19 classification from X-ray images is made w

关键词： COVID-19 X-ray image Neural network feature extraction algorithm Dimensionality reduction algorithm feature selection algorithm

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Bioinformatics Study with an Implementation of Two feature extraction algorithms for Protein Sequences

Bioinformatics Study with an Implementation of Two Feature E...

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3rd International Conference on Material Science, Environment Science and Computer Science (MSESCS 2014)

作者： Qiao, Shanping Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783038350118

Predicting subcellular localizations of proteins is related to multi-label learning. A serial of computational approaches have been developed. This study focuses on the extracting protein features. The feature vector influences the performance of a predicting algorithm significantly. In this paper, two feature extraction algorithms named composition-transition-distribution and class pattern frequency were introduced and implemented in Java, respectively: This program provided a friendly graphical user interface where users can get these two kinds of features easily and quickly. Moreover, the results can be saved into a specified file for later use. Finally, this program can be compressed into a single jar file and runs on a computer which installed the proper JRE. We hope that this program would give researchers some help in the future.

关键词： protein subcellular localization prediction feature extraction algorithm Java

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feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2010年第3期4卷 177-192页

作者： Evagorou, D. Kyprianou, A. Lewin, P. L. Stavrou, A. Efthymiou, V. Metaxas, A. C. Georghiou, G. E. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Cyprus Dept Mech & Mfg Engn CY-1678 Nicosia Cyprus Univ Southampton Sch Elect & Comp Sci Tony Davies High Voltage Lab Southampton SO17 1BJ Hants England Elect Author Cyprus Nicosia Cyprus Univ Cambridge Dept Engn Elect Utilisat Grp Cambridge CB2 1TN England

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

关键词： Computer vision and image processing techniques Dielectric breakdown and discharges probabilistic neural network partial discharge signals wavelet packet transform wavelet transforms Transformers and reactors power engineering computing partial discharges PD classification current transformers high-frequency current transformer Neural computing techniques power cables two-fold process HFCT sensor Optical, image and video signal processing feature extraction Power engineering computing internal discharge feature extraction algorithm floating discharge PDF neural nets probability density function Integral transforms feature classification algorithm PNN classifier Power cables high voltage equipment power cable accessories surface discharge

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AN ONLINE UNSUPERVISED LEARNING-MACHINE FOR ADAPTIVE feature-extraction

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 1994年第2期41卷 87-98页

作者： CHEN, H LIU, RW UNIV NOTRE DAME NOTRE DAME IN 46556 USA

Adaptive feature extraction is useful in many information processing systems. In this paper we propose a learning machine implemented via a neural network to perform such a task using the tool principal component analysis. This machine (1) is adaptive to nonstationary input, (2) is based on an un-supervised learning concept and requires no knowledge of if, or when, the input changes statistically, and (3) performs on-line computation that requires little memory or data storage. Associated with this machine, we propose a learning algorithm (LEAP), whose convergence properties are theoretically analyzed and whose performance is evaluated via computer simulations. Two major contributions of this paper are: (1) Under appropriate conditions, we prove that the algorithm will extract multiple principal components, when the learning rate is constant;and (2) we identify a near optimal domain of attraction.

关键词： PRINCIPAL COMPONENT ANALYSIS feature extraction algorithm HEBBIAN LEARNING UNSUPERVISED LEARNING NEURAL NETWORK DATA COMPRESSION CONVERGENCE OF SUCCESSIVE APPROXIMATION

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