A comparison of instrument response correction methods: post-processing and real-time methods

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

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Sensitivity of GNSS tropospheric gradients to processing options

Annales Geophysicae ◽

10.5194/angeo-37-429-2019 ◽

2019 ◽

Vol 37 (3) ◽

pp. 429-446 ◽

Cited By ~ 8

Author(s):

Michal Kačmařík ◽

Jan Douša ◽

Florian Zus ◽

Pavel Václavovic ◽

Kyriakos Balidakis ◽

...

Keyword(s):

Real Time ◽

Elevation Angle ◽

Mapping Function ◽

Global Navigation Satellite Systems ◽

Gradient Estimates ◽

Post Processing ◽

Data Set ◽

Mapping Functions ◽

Gradient Mapping

Abstract. An analysis of processing settings impacts on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 Global Navigation Satellite Systems (GNSS) reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using precise point positioning (PPP) with the G-Nut/Tefnut software. The impacts of the gradient mapping function, elevation cut-off angle, GNSS constellation, observation elevation-dependent weighting and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather models (NWMs). Tropospheric gradients estimated in post-processing GNSS solutions using final products were in good agreement with NWM outputs. The quality of high-resolution gradients estimated in (near-)real-time PPP analysis still remains a challenging task due to the quality of the real-time orbit and clock corrections. Comparisons of GNSS and NWM gradients suggest the 3∘ elevation angle cut-off and GPS+GLONASS constellation for obtaining optimal gradient estimates provided precise models for antenna-phase centre offsets and variations, and tropospheric mapping functions are applied for low-elevation observations. Finally, systematic errors can affect the gradient components solely due to the use of different gradient mapping functions, and still depending on observation elevation-dependent weighting. A latitudinal tilting of the troposphere in a global scale causes a systematic difference of up to 0.3 mm in the north-gradient component, while large local gradients, usually pointing in a direction of increasing humidity, can cause differences of up to 1.0 mm (or even more in extreme cases) in any component depending on the actual direction of the gradient. Although the Bar-Sever gradient mapping function provided slightly better results in some aspects, it is not possible to give any strong recommendation on the gradient mapping function selection.

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A New Asynchronous RTK Method to Mitigate Base Station Observation Outages

Sensors ◽

10.3390/s19153376 ◽

2019 ◽

Vol 19 (15) ◽

pp. 3376 ◽

Cited By ~ 2

Author(s):

Yuan Du ◽

Guanwen Huang ◽

Qin Zhang ◽

Yang Gao ◽

Yuting Gao

Keyword(s):

Real Time ◽

Base Station ◽

Global Navigation Satellite Systems ◽

Observation Data ◽

Post Processing ◽

Positioning Accuracy ◽

Real Time Processing ◽

Phase Measurements ◽

Satellite Systems ◽

Precise Ephemeris

Real-time kinematic (RTK) positioning is a satellite navigation technique that is widely used to enhance the precision of position data obtained from global navigation satellite systems (GNSS). This technique can reduce or eliminate significant correlation errors via the enhancement of the base station observation data. However, observations received by the base station are often interrupted, delayed, and/or discontinuous, and in the absence of base station observation data the corresponding positioning accuracy of a rover declines rapidly. With the strategies proposed till date, the positioning accuracy can only be maintained at the centimeter-level for a short span of time, no more than three min. To address this, a novel asynchronous RTK method (that addresses asynchronous errors) that can bridge significant gaps in the observations at the base station is proposed. First, satellite clock and orbital errors are eliminated using the products of the final precise ephemeris during post-processing or the ultra-rapid precise ephemeris during real-time processing. Then the tropospheric error is corrected using the Saastamoinen model and the asynchronous ionospheric delay is corrected using the carrier phase measurements from the rover receiver. Finally, a straightforward first-degree polynomial function is used to predict the residual asynchronous error. Experimental results demonstrate that the proposed approach can achieve centimeter-level accuracy for as long as 15 min during interruptions in both real-time and post-processing scenarios, and that the accuracy of the real-time scheme can be maintained for 15 min even when a large systematic error is projected in the U direction.

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Statistical post-processing of ensemble forecasts of the height of new snow

Nonlinear Processes in Geophysics ◽

10.5194/npg-26-339-2019 ◽

2019 ◽

Vol 26 (3) ◽

pp. 339-357 ◽

Cited By ~ 2

Author(s):

Jari-Pekka Nousu ◽

Matthieu Lafaysse ◽

Matthieu Vernay ◽

Joseph Bellier ◽

Guillaume Evin ◽

...

Keyword(s):

Real Time ◽

Spatial Scales ◽

Weather Prediction ◽

Evaluation Criteria ◽

Training Dataset ◽

Post Processing ◽

Probabilistic Forecasting ◽

Ensemble Forecasts ◽

Heterogeneous Model ◽

The Impact

Abstract. Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

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Linear Static, Real-Time Finite Element Computations Based on Element Masks

ASME 2011 World Conference on Innovative Virtual Reality ◽

10.1115/winvr2011-5501 ◽

2011 ◽

Cited By ~ 2

Author(s):

Holger Graf ◽

Andre´ Stork

Keyword(s):

Finite Element ◽

Stress Field ◽

Boundary Conditions ◽

Real Time ◽

Stiffness Matrix ◽

New Method ◽

Stress Distributions ◽

Post Processing ◽

Time Performance ◽

Computational Overhead

This paper presents a new method for the manipulation of a given CAE domain in view of VR based explorations that enables engineers to interactively inspect and analyze a linear static domain. The interactions can ideally be performed in real-time in order to provide an intuitive impression of the changes to the underlying volumetric domain. We take the approach of element masking, i.e. the blending out of computations resulting from computational overhead for inner nodes, based on the inversion of the stiffness matrix. This allows us to optimize the re-simulation loop and to achieve real-time performance for strain and stress distributions with immediate visualization feedback caused by interactively changing boundary conditions. The novelty of the presented approach is a direct coupling of view dependent simulations and its close linkage to post-processing tasks. This allows engineers to also inspect the changes of the stress field inside of the volume during, e.g. cross sectioning.

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A FPGA based real-time post-processing architecture for active stereo vision

The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014) ◽

10.1109/isce.2014.6884341 ◽

2014 ◽

Cited By ~ 1

Author(s):

Seung-min Choi ◽

Jiho Chang ◽

Dae Hwan Hwang

Keyword(s):

Real Time ◽

Stereo Vision ◽

Post Processing ◽

Active Stereo ◽

Processing Architecture

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Note: A 10 Gbps real-time post-processing free physical random number generator chip

Review of Scientific Instruments ◽

10.1063/1.4993494 ◽

2017 ◽

Vol 88 (9) ◽

pp. 096105 ◽

Cited By ~ 3

Author(s):

Yi Qian ◽

Futian Liang ◽

Xinzhe Wang ◽

Feng Li ◽

Lian Chen ◽

...

Keyword(s):

Real Time ◽

Random Number ◽

Random Number Generator ◽

Number Generator ◽

Post Processing

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Precise Point Positioning Model Using Triple GNSS Constellations: GPS, Galileo and BeiDou

Journal of Applied Geodesy ◽

10.1515/jag-2016-0010 ◽

2016 ◽

Vol 10 (4) ◽

Cited By ~ 1

Author(s):

Akram Afifi ◽

Ahmed El-Rabbany

Keyword(s):

Real Time ◽

Urban Areas ◽

Precise Point Positioning ◽

Convergence Time ◽

Dual Frequency ◽

Post Processing ◽

International Gnss Service ◽

Time Service ◽

System Bias ◽

Point Positioning

AbstractThis paper introduces a comparison between dual-frequency precise point positioning (PPP) post-processing model, which combines the observations of three different GNSS constellations, namely GPS, Galileo, and BeiDou and real-time PPP model. A drawback of a single GNSS system such as GPS, however, is the availability of sufficient number of visible satellites in urban areas. Combining GNSS observations offers more visible satellites to users, which in turn is expected to enhance the satellite geometry and the overall positioning solution. However, combining several GNSS observables introduces additional biases, which require rigorous modelling, including the GNSS time offsets and hardware delays. In this paper, a GNSS post-processing PPPP model is developed using ionosphere-free linear combination. The additional biases of the GPS, Galileo, and BeiDou combination are accounted for through the introduction of a new unknown parameter, which is identified as the inter-system bias, in the PPP mathematical model. Natural Resources Canada’s GPSPace PPP software is modified to enable a combined GPS / Galileo / BeiDou PPP solution and to handle the newly inter-system bias. A total of four data sets at four IGS stations are processed to verify the developed PPP model. Precise satellite orbit and clock products from the IGS-MGEX network are used to correct of the GPS, Galileo and BeiDou measurements. For the real-time PPP model the corrections of the satellites orbit and clock are obtained through the international GNSS service (IGS) real-time service (RTS). GPS and Galileo Observations are used for the GNSS RTS-IGS PPP model as the RTS-IGS satellite products are not available for BeiDou satellites. This paper provides the GNSS RTS-IGS PPP model using different satellite clock corrections namely: IGS01, IGC01, IGS01, and IGS03. All PPP models results of convergence time and positioning precision are compared to the traditional GPS-only PPP model. It is shown that combining GPS, Galileo, and BeiDou observations in a PPP model reduces the convergence time by 25 % compared with the GPS-only PPP model.

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A Visible Framework for Virtual Prototype of Electric Tools

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.120.61 ◽

2011 ◽

Vol 120 ◽

pp. 61-64

Author(s):

Jiang Wei Cui ◽

Qiang Gao ◽

Jing Li ◽

Xiang Chen ◽

Xiao Lei Wang

Keyword(s):

Real Time ◽

Virtual Prototype ◽

Post Processing ◽

Linear Vibration ◽

Post Process ◽

Parameterized Modeling ◽

Processing Module ◽

Simulation Results ◽

Analysis System ◽

Geometry Engine

This paper developed real-time virtual prototype analysis system of electric tools that consists of pre-processing module, solver module and post processing module. Adopting the OCC (OpenCASCADE) geometry engine, the functions such as parameterized modeling, virtual assembly and visible post process of the geared rotor system of electric tools were realized, simulation results of the virtual prototype analysis of the non-linear vibration is shown effectively.

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