- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Guangzhou, China (2019.5.19-2019.5.22)] 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT) - Substrate Integrated Waveguide Quasi-Elliptic Millimeter Wave Filter
摘要： We present an innovative region-growing-based technique that permits to improve the surface displacement time-series retrieval capability of the two-scale Small BAseline Subset (SBAS) Differential Interferometric Synthetic Aperture Radar (DInSAR) approach in medium-to-low coherence regions. Starting from a sequence of multitemporal differential SAR interferograms, computed at the full spatial resolution scale, the developed method “propagates” the information on the deformation relevant to a set of high coherent SAR pixels [referred to as source pixels (SPs)], in correspondence to which SBAS-DInSAR deformation measurements have previously been estimated, to their less coherent neighbouring ones. In this framework, a minimum-norm constrained optimization problem, relying on the use of constrained Delaunay triangulations (CDTs), is solved, where the constraints represent the displacement values at the SP locations. Such DInSAR processing scheme, referred to as Constrained-Network Propagation (C-NetP), is easy to implement and, although specifically developed to work within the two-scale SBAS framework, it can be extended to wider DInSAR scenarios. The validity of the method has been investigated by processing a SAR dataset acquired over the city of Rome (Italy) by the Cosmo-SkyMed constellation from July 2010 to October 2012. The achieved results demonstrate that the proposed C-NetP method is capable to significantly increase the spatial density of the SBAS-DInSAR measurements, reaching an improvement of about 250%. Such an improvement allows revealing deformation patterns that are partially or completely hidden, by applying the conventional two-scale SBAS processing. This is particularly relevant in urban areas where the assessment and management of the risk associated to the deformation affecting infrastructures is strategic for decision makers and local authorities.
关键词： Delaunay triangulations,deformation,time series,Constrained optimization problems,Small BAseline Subset (SBAS),Differential Interferometric Synthetic Aperture Radar (DInSAR)
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Macau, China (2019.11.3-2019.11.8)] 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Free-Space Features: Global Localization in 2D Laser SLAM Using Distance Function Maps
摘要： A method for selecting a graphical model -vector-valued stationary Gaussian time series was recently proposed by Matsuda and uses the Kullback–Leibler divergence measure to define a test statistic. This statistic was used in a backward selection procedure, but the algorithm is prohibitively expensive for large . A high degree of sparsity is not assumed. We show that reformulation in terms of a multiple hypothesis test and simulations support the reduces computation time by the assertion that power levels are attained at least as good as those achieved by Matsuda’s much slower approach. Moreover, the new scheme is readily parallelizable for even greater speed gains.
关键词： multiple hypothesis test,Kullback–Leibler divergence,vector-valued time series,Undirected graph
更新于2025-09-23 15:19:57
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Pulverized coal combustion application of laser-based temperature sensing system using computed tomography a?? Tunable diode laser absorption spectroscopy (CT-TDLAS)
摘要： The investigation of combustion phenomena in pulverized coal flames is significant for combustion optimization related to energy conservation and emission reduction. Real-time two dimensional (2D) temperature and concentration distributions play an important role for combustion analysis. The non-contact and fast response 2D temperature and concentration distribution measurement method was developed in this study. The method is based on a combination of computed tomography (CT) and tunable diode laser absorption spectroscopy (TDLAS). The accuracy evaluation of developed 32-path CT-TDLAS demonstrated its feasibility of 2D temperature measurement. 32-path CT-TDLAS was applied to CH4 and 5 kg/h coal combustion fields for 2D temperature measurement. The time-series 2D temperature distribution in coal combustion furnace was measured using 32-path CT-TDLAS measurement cell with kHz time resolution. The transient temperature field of combustion flame directly reflects the combustion mode and combustion stability. The measurement results demonstrate its applicability of CT-TDLAS to various types of combustor, especially the combustion fields with coal and ash particles. CT-TDLAS method with kHz response time enables the real-time 2D temperature measurement to be applicable for combustion analysis.
关键词： 2D temperature measurement,Computed tomography (CT),Tunable diode laser absorption spectroscopy (TDLAS),Time-series distribution,Coal combustion
更新于2025-09-23 15:19:57
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[IEEE 2019 National Power Electronics Conference (NPEC) - Tiruchirappalli, India (2019.12.13-2019.12.15)] 2019 National Power Electronics Conference (NPEC) - Input Regulated Soft Switched Ripple Free Current LED Driver
摘要： The main contribution of this paper is the construction of the efficient privacy-preserving protocol for smart metering systems (EPPP4SMS), which brings together features of the best privacy-preserving protocols in the literature for smart grids. In addition, EPPP4SMS is faster on the meter side—and in the whole round (encryption, aggregation, and decryption)—than other protocols based on homomorphic encryption and it is still scalable. Moreover, EPPP4SMS enables energy suppliers and customers to verify the billing information and measurements without leaking private information. Since the energy supplier knows the amount of generated electricity and its flow throughout electrical substations, the energy supplier can use this verification to detect energy loss and fraud. Different from verification based on digital signature, our verification enables new features; for example, smart meters and their energy supplier can compute the verification without storing the respective encrypted measurements. Furthermore, EPPP4SMS may be suitable to many other scenarios that need aggregation of time-series data keeping privacy protected, including electronic voting, reputation systems, and sensor networks. In this paper, we present theoretical results of EPPP4SMS and their validation by implementation of algorithms and simulation using real-world measurement data.
关键词： Homomorphic encryption,privacy,smart grid,time series,security,protocol
更新于2025-09-23 15:19:57
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Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area
摘要： It is of great signi?cance to timely, accurately, and e?ectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the in?uence of revisiting periods and weather, it is di?cult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Re?ectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classi?cation method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coe?cient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and e?cient technical method for the e?ective extraction of land use/cover information in the highly heterogeneous regions.
关键词： Land use/land cover,MODIS,Multisource fusion,Landsat8,Time series
更新于2025-09-19 17:15:36
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MONITORAMENTO E PROJE??O FUTURA DA VEGETA??O NO PARQUE NACIONAL DO ITATIAIA ATRAVéS DE SENSORIAMENTO REMOTO
摘要： Satellite images of earth observation and meteorological sensors have been used for monitoring land use. Recently products obtained from satellite images have been disseminated, among them, several vegetation indices. EUMETSAT, through the Land –SAF, offers, among other products, the Leaf Area Index (LAI). Daily LAI products have been acquired in raster format corresponding from 01/01/2010 to 30/12/2010. From a pixel located in the central portion of the Itatiaia National Park, a time series was generated, which was analyzed aiming at assessing the dynamics of leaf area index. The tendency observed in this period indicates that LAI decreased during 2010. It was possible to observe that changes in vegetation have close relationship with changes in rainfall and fires that affect the region. The ARIMA (7 1 0) model was able to describe the behavior of the LAI series, producing white noise and indicating correlations among 1, 6 and 7 days among the past observations. The prediction for future values resulted in an average error of 2.74%, indicating the potential of the model to identify changes in vegetation. Models of ARIMA class, in conjunction with orbital products, stand out as promises for use in the analysis of the vegetation of protected areas.
关键词： Leaf area index,Time Series,ARIMA Model,Conservation units
更新于2025-09-19 17:15:36
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[IEEE 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Miramar Beach, FL, USA (2019.8.19-2019.8.21)] 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID) - Invited Talk: "High Resolution Space/Time Imaging of Shockwaves Generated by Remote Laser Plasmas Produced by Light Filaments"
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： PCA,dimensionality reduction,electric load prediction,smart grid,genetic algorithm,forecasting,echo state network,Time-series
更新于2025-09-19 17:13:59
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[IEEE 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - Waikoloa Village, HI (2018.6.10-2018.6.15)] 2018 IEEE 7th World Conference on Photovoltaic Energy Conversion (WCPEC) (A Joint Conference of 45th IEEE PVSC, 28th PVSEC & 34th EU PVSEC) - A Fast Quasi-Static Time Series Simulation Method for PV Smart Inverters with VAR Control using Linear Sensitivity Model
摘要： Fast deployment of renewable energy resources in distribution networks, especially solar photovoltaic (PV) systems, have motivated the need for inverter-based voltage regulation. Integration studies are often necessary to fully understand the potential impacts of PV inverter settings on the various elements of the distribution system, including voltage regulators and capacitor banks. A year long quasi-static time series (QSTS) at second-level granularity provides a comprehensive assessment of these impacts, however the computational burden associated with running QSTS limits its applicability. This paper proposes a fast QSTS simulation technique capable of modeling the smart inverter dynamic VAR control functionality and accurately estimating the states of controllable elements including voltage regulators and capacitor banks at each time step. Consequently, the complex interactions between various legacy voltage regulation devices is also captured. The efficacy of the proposed algorithm is demonstrated on the IEEE 13-bus test case with a 98% reduction in computation time.
关键词： multiple linear regression,smart inverter,voltage control,Quasi-static time series,PV impact studies
更新于2025-09-19 17:13:59
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Resonance of the Annihilation Channel of a Laser-Assisted Electron-Positron Scattering
摘要： Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
关键词： time series,missing data prediction,Apache Spark,Matrix factorization
更新于2025-09-19 17:13:59
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Generating Maximal Entanglement between Spectrally Distinct Solid-State Emitters
摘要： Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
关键词： missing data prediction,time series,Apache Spark,Matrix factorization
更新于2025-09-19 17:13:59