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USING APPLIANCE SIGNATURES FOR MONITORING RESIDENTIAL LOADS AT METER PANEL LEVEL

IEEE TRANSACTIONS ON POWER DELIVERY 1991年第4期6卷 1380-1385页

作者： SULTANEM, F Direction des Etudes et Recherches Electricitd de France Clamart France

As an initial approach, in order to identify electrical household appliances strictly by analyzing variations in current-voltage signals at meter panel level may seem to be utopian or, to say the least, an ambitious undertaking. However, on observation of the currents consumed by these appliances it is surprising to see that each one of them is recognizable and perfectly identifiable by a certain number of parameters. Nevertheless, difficulties flow from the fact that an appliance is generally switched on while other appliances are already in use. The main issue is then to isolate the characteristic parameters of the appliance from the aggregate signal. One is also aware that the success of the identification does not only depend on the efficiency of the monitoring device, but also on a set of circumstances relating to the composition of the range of appliances installed on the measurement site, and the random sequencing of their operation. This paper presents a study whose aim is to show the feasibility of this device. In particular, it presents the test findings secured on site using a test prototype.

关键词： site measurements sorting algorithm Sufficient Writers measuring instruments Reactive power Device Panel device test LOAD

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Integrating LiDAR and high-resolution imagery for object-based mapping of forest habitats in a heterogeneous temperate forest landscape

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2018年第23期39卷 8859-8884页

作者： Gonzalez, Ramiro Silveyra Latifi, Hooman Weinacker, Holger Dees, Matthias Koch, Barbara Heurich, Marco Univ Freiburg Dept Biometry & Environm Syst Anal Tennenbacherstr 4 D-79106 Freiburg Germany KN Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran Univ Wurzburg Dept Remote Sensing Wurzburg Germany Univ Freiburg Chair Remote Sensing & Landscape Informat Syst Freiburg Germany Univ Freiburg Chair Wildlife Ecol & Management Freiburg Germany

Land-cover mapping (LCM) at a fine scale would be useful for forest management across heterogeneous natural landscapes. However, the heterogeneity of land covers at such scales results in complex spectral and textural properties that hinder the applicability of LCM. Besides, the method suffers from, e.g. inconsistent representation of different land-cover types, lack of sufficient and balanced training samples, and instability of classifiers trained by a high number of predictor variables. Even well-known object-based classification approaches are challenged with an objective evaluation of segmentation outputs. Here we classified partially ambiguous land-cover types across heterogeneous forest landscapes in the Bavarian Forest National Park (Germany) by combining metrics from airborne light detection and ranging (LiDAR) and colour infrared (CIR) imagery data and a random forest classifier implemented in an object-based paradigm. We evaluated the segmentation results by creating a global quality score based on inter- and intra-measurements of variance and the number of segments. Selected segmentation outputs were combined with balanced training samples to run the classification algorithm based on representative blocks within the national park. The entire processing chain was implemented in an open-source domain. The final segmentation consisted of LiDAR-based height, image-based Normalized Difference Vegetation Index (NDVI) and red band, with 20 cluster seeds and a minimum segment size of 40 pixels. In the classification, the most important variables included the height of the top layer, NDVI, Enhanced Vegetation Index (EVI) and Green-Red Vegetation Index (GRVI). The average values of 500 random forest runs indicated an overall accuracy of 86.6% and an estimated Cohen's kappa coefficient of 85.2%, with different probabilities of correct classification for land-cover classes. Mature deciduous, standing deadwood, fallen deadwood, meadow, and bare soil classes were clas

关键词： Classifiers object orientation temperate forest sorting algorithm normalized difference vegetation index Taxonomy Bavarian Forest forest habitats Lidar Heterogeneous Disease Predictive Factor Lowest common multiple High resolution imaging

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DETECTION OF TECHNOGENIC RADIONUCLIDES AT LOW CONCENTRATIONS WHEN MONITORING NUCLEAR POWER PLANT EMISSIONS USING AN ARMS SPECTROMETRIC CHANNEL

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ATOMIC ENERGY 2010年第1期108卷 70-78页

作者： Korolev, S. A. Lavrukhin, Yu. E. Rumyantsev, O. V. Russian Acad Sci GEOKhI RAN Vernadskii Inst Geochem & Analyt Chem Moscow Russia

The application of statistical algorithms in the problem of early detection of technogenic, nuclear power plant emissions in the atmosphere near the ground by means of the spectrometric channel of ARMS ( Automatic Radiation Monitoring System) is examined. It is noted that the changes in the radon progeny content which are associated with the diurnal behavior of the intensity of turbulent mixing influence the variance of the readings in the (131)I and (137)Cs detection ranges. As a result, when a stationary Poisson model is used in the algorithm the number of false alarms increases and the algorithm becomes ineffective. Regional corrections lowering the probability of false alarms to <= 10(-3) with correct detection probability >= 0.7 were introduced into the detection and classification algorithm on the basis of the results of monitoring performed over the course of a year for intervals with stationary gamma background.

关键词： atmosphere reading Early Diagnosis diurnal animal sorting algorithm Variance Nuclear powerplants False alarms Arms algorithms ground turbulent mixing RADON PROGENY

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EVALUATION OF TEXTURAL AND MULTIPOLARIZATION RADAR FEATURES FOR CROP CLASSIFICATION

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1995年第5期33卷 1170-1181页

作者： ANYS, H HE, DC Universite de Sherbrooke Sherbrooke Que Can

The aim of this research is to evaluate crop discrimination using airborne radar data based on multipolarization and textural information. Multipolarization data (C-HH, C-VV, and C-HV) were used for discriminating 5 crop types i.e., corn, wheat, soya, pasture, and alfalfa. For the multipolarization evaluation, an unsupervised classification algorithm and a supervised method based on maximum likelihood were used on the data. For the textural evaluation, textural measures of different degrees were calculated on three different order histograms and were evaluated from the crop discrimination point of view. Results show that multipolarization correct classification rates of 86.31% and 74.47% were obtained for supervised and unsupervised methods respectively. Hence, multipolarization radar data offer an adequate tool for crop identification especially with supervised classification. The evaluation of textural measures shows that for a first order histogram the mean measure gives the best rate of discrimination. In the case of second and third order histograms, the best measures are contrast and large number emphasis respectively. These textural measures were integrated with the three multipolarization channels in order to determine their specific contributions. Results show that crop class separability is thereby improved and that the rate of correct classification increased hy 9.79% for the crops.

关键词： histograms sorting algorithm Discrimination Medicago sativa order maximum likelihood CROP IDENTIFICATION radar data airborne radar

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Classification of multisource remote sensing imagery using a Genetic algorithm and Markov random fields

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1999年第3期37卷 1255-1260页

作者： Tso, BCK Mather, PM Univ Nottingham Sch Geog Nottingham NG7 2RD England

The use of contextual information for modeling the prior probability mass function has found applications in the classification of remotely sensed data, With the increasing availability of multisource remotely sensed data sets, random field models, especially Markov random fields (MRF), have been found to pro,ide a theoretically robust yet mathematical tractable way of coding multisource information and of modeling contextual behavior. It is well known that the performance of a model is dependent both on its functional form (in this case, the classification algorithm) and on the accuracy of the estimates of model parameters, In dealing with multisource data, the determination of source weighting and MRF model parameters is a difficult issue. We extend the methodology proposed in [1] by demonstrating that the use of an effective search procedure, the Genetic algorithm, leads to improved parameter estimation and hence higher classification accuracies.

关键词： model parameters remote-sensing data sorting algorithm remote sensing mass function Parameter estimation Markov random fields Genetic algorithm (GA)

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Blind modulation classification algorithm based on machine learning for spatially correlated MIMO system

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IET COMMUNICATIONS 2017年第7期11卷 1000-1007页

作者： Liu, Xiaokai Zhao, Chenglin Wang, Pengbiao Zhang, Yang Yang, Tianpu Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Dept China Mobile Grp Design Inst Co Ltd Beijing Peoples R China

Spatial correlation is a decisive factor for pragmatic multiple-input multiple-output (MIMO) system, simultaneously bringing about some problems in the received signal modulation identification respect. In this study, the authors focus on blind digital modulation identification in the spatially correlated MIMO system and deliver a robust signal recognition algorithm based on extreme learning machine (ELM) and higher order statistical features for MIMO signal identification without a priori knowledge of the channel and signal parameters. The superiority of ELM lies in random selections of hidden nodes and ascertains output weights analytically, which result in lower computational complexity. Theoretically, this algorithm has a tendency to supply excellent generalisation performance at staggering learning rate. Further, the simulation results indicate that the ELM could reap a perfectly acceptable recognition performance and thus provides a solid ground structure for tackling MIMO modulation challenges in low signal-to-noise ratio.

关键词： sorting algorithm multiple input multiple output systems signal parameter signal recognition

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SAR image classification based on alpha-stable distribution

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REMOTE SENSING LETTERS 2011年第1期2卷 51-59页

作者： Peng, Yijin Xu, Xin Zhou, Wanbin Zhao, Yin Wuhan Univ Signal Proc Lab Sch Elect Informat Wuhan 430079 Hubei Peoples R China

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multi-level SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.

关键词： sorting algorithm Synthetic aperture radar Support Vector Network Eigenvector Image classification images algorithms Imagery (Psychotherapy) Rescue SAFETY ANALYSIS REPORTS distributed parameter Specific absorption rate

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Flaw identification from time and frequency features of ultrasonic waveforms

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IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 1996年第4期43卷 592-600页

作者： Case, TJ Waag, RC UNIV ROCHESTER DEPT ELECT ENGNROCHESTERNY 14627 UNIV ROCHESTER DEPT RADIOLROCHESTERNY 14627

Time and frequency features have been used with classification algorithms to distinguish between ultrasonic echoes from flaws in pipes and ultrasonic echoes from various geometric configurations of weld root and counterbore. Waveforms containing reflections from known geometries and from flaws were obtained and sets of features were defined using a k-nearest neighbor approach to separate waveforms into classes, Two independent databases containing various flaws and pipe geometries were used to determine these feature sets, From these databases, optimal feature sets were found to separate counterbore waveforms from crack waveforms, Optimal feature sets were also found to distinguish between waveforms from counterbore, waveforms containing both counterbore and root echoes, and waveforms from flaws, The best feature sets used with the classifier algorithms could separate waveforms from the same database with accuracy in the 92-97% range and with high confidence, Another database was obtained from pipe structures in a nuclear power plant to provide a field test of the method, When applied to this database, the same classifier algorithms and feature sets used with the other databases either resulted in a comparable percentage of correct decisions, but with low confidence, or could not classify anywhere from 79 to 88% of the waveforms, Spatial parameters based on averaging feature vectors in axial and circumferential directions were also defined and used for classification, These classifications had higher accuracy but lower confidence levels than the classifications based on individual waveforms.

关键词： echo feature extraction flaw detection pattern classification ultrasonic materials testing welding classification algorithms counterbore crack waveforms feature vectors flaw identification frequency features geometric configurations k-nearest neighbor ap sorting algorithm Echoes Eigenvector Test Method ultrasonic materials testing Feature extraction flaws SETTING waveforms Taxonomy Welding Geometric forms Flaw detection Pattern recognition

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Development of a sky imager for cloud cover assessment

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION 2008年第1期25卷 29-39页

作者： Cazorla, A. Olmo, F. J. Alados-Arboledasl, L. Univ Granada Ctr Andaluz Medio Ambiente Grp Fis & Atmosfera E-18071 Granada Spain Univ Granada Fac Ciencias Dept Fis Aplicada E-18071 Granada Spain

Based on a CCD camera, we have developed an in-house sky imager system for the purpose of cloud cover estimation and characterization. The system captures a multispectral image every 5 min, and the analysis is done with a method based on an optimized neural network classification procedure and a genetic algorithm. The method discriminates between clear sky and two cloud classes: opaque and thin clouds. It also divides the image into sectors and finds the percentage of clouds in those different regions. We have validated the classification algorithm on two levels: image level, using the cloud observations included in the METAR register performed at the closest meteorological station, and pixel level, determining whether the final classification is correct. (c) 2007 Optical Society of America.

关键词： sorting algorithm cloud cover Charge Coupled Device camera images imaging instrument sky Clouds Imagery (Psychotherapy) Sectoring

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Design and control of battery charger for electric vehicles using modular multilevel converters

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IET POWER ELECTRONICS 2021年第1期14卷 140-157页

作者： Quraan, Mahran Abu-Khaizaran, Muhammad Sa'ed, Jaser Hashlamoun, Wael Tricoli, Pietro Birzeit Univ Dept Elect & Comp Engn Ramallah Palestine Univ Birmingham Sch Elect Elect & Syst Engn Birmingham W Midlands England

Double-star modular multilevel converters with embedded battery cells are a ground breaking technology for the power converter of electric vehicles. In this topology, the battery cells are connected in series via half bridge DC-DC buck converter and can be independently discharged and recharged. This paper proposes a novel control system for this converter topology that can charge the battery cells from the grid at unity power factor and instantaneously balance them in terms of their state of charges without affecting the grid voltages and currents. The grid current controller is designed and implemented in the stationary reference frame using a proportional-resonant controller, while the state of charge balancing algorithm is designed using sorting algorithm and circulating current control. The simulation and experimental results have demonstrated that the proposed control strategy can effectively charge the battery cells with a negligible distortion of the grid current and can ensure the balance of cells during the recharge without affecting the grid current.

关键词： power converter power factor electric vehicles double-star modular multilevel converters Control of electric power systems novel control system proportional-resonant controller unity power factor control strategy sorting algorithm half bridge DC-DC buck converter embedded battery cells grid voltages Current control DC-DC power convertors power grids Transportation electric current control power convertors charge balancing algorithm circulating current control Power convertors and power supplies to apparatus battery chargers battery charger converter topology grid current controller Power electronics, supply and supervisory circuits

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