UWB anchor nodes self-calibration in NLOS conditions: a machine learning and adaptive PHY error correction approach

2021 ◽  
Author(s):  
Matteo Ridolfi ◽  
Jaron Fontaine ◽  
Ben Van Herbruggen ◽  
Wout Joseph ◽  
Jeroen Hoebeke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Self Calibration ◽  
Anchor Nodes

Machine learning enabled self-calibration single fiber endoscopic imaging

2021 ◽  
Author(s):  
Huiying Zhang ◽  
Xu Wang ◽  
Hanwen Du ◽  
Haiyang Yu ◽  
Jinghao WU ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Fiber ◽  
Endoscopic Imaging ◽  
Self Calibration

scholarly journals Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ji-Hun Ha ◽  
Yong-Hyuk Kim ◽  
Hyo-Hyuc Im ◽  
Na-Young Kim ◽  
Sangjin Sim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Atmospheric Pressure ◽  
Observation Interval ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Observation Data ◽  
Pressure Data ◽  
Weather Forecasts ◽  
Weather Events

Severe weather events occur more frequently due to climate change; therefore, accurate weather forecasts are necessary, in addition to the development of numerical weather prediction (NWP) of the past several decades. A method to improve the accuracy of weather forecasts based on NWP is the collection of more meteorological data by reducing the observation interval. However, in many areas, it is economically and locally difficult to collect observation data by installing automatic weather stations (AWSs). We developed a Mini-AWS, much smaller than AWSs, to complement the shortcomings of AWSs. The installation and maintenance costs of Mini-AWSs are lower than those of AWSs; Mini-AWSs have fewer spatial constraints with respect to the installation than AWSs. However, it is necessary to correct the data collected with Mini-AWSs because they might be affected by the external environment depending on the installation area. In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.

scholarly journals MNCE: Multi-Hop Node Localization Algorithm for Wireless Sensor Network Based on Error Correction

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 269
Author(s):  
Yinghui Meng ◽  
Yuewen Chen ◽  
Qiuwen Zhang ◽  
Weiwei Zhang
Keyword(s):  
Error Correction ◽  
Large Error ◽  
Least Square ◽  
Localization Algorithm ◽  
Target Node ◽  
Node Localization ◽  
Simulation Experiments ◽  
Connectivity Information ◽  
Localization Algorithms ◽  
Anchor Nodes

Considering the problems of large error and high localization costs of current range-free localization algorithms, a MNCE algorithm based on error correction is proposed in this study. This algorithm decomposes the multi-hop distance between nodes into several small hops. The distance of each small hop is estimated by using the connectivity information of adjacent nodes; small hops are accumulated to obtain the initial estimated distance. Then, the error-correction rate based on the error-correction concept is proposed to correct the initial estimated distance. Finally, the location of the target node is resolved by total least square methods, according to the information on the anchor nodes and estimated distances. Simulation experiments show that the MNCE algorithm is superior to the similar types of localization algorithms.

scholarly journals Lab-built terrestrial laser scanner self-calibration using mounting angle error correction

2018 ◽  
Vol 26 (11) ◽  
pp. 14444 ◽  
Author(s):  
Xiaolu Li ◽  
Yunye Li ◽  
Xinhao Xie ◽  
Lijun Xu
Keyword(s):  
Error Correction ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Angle Error ◽  
Self Calibration

scholarly journals Two-Step Predict and Correct Non-Intrusive Parametric Model Order Reduction for Changing Well Locations Using a Machine Learning Framework

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1765
Author(s):  
Hardikkumar Zalavadia ◽  
Eduardo Gildin
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Error Correction Model ◽  
Parametric Model ◽  
Model Order ◽  
Correction Model ◽  
Reduced Order ◽  
Parametric Model Order Reduction

The objective of this paper is to develop a two-step predict and correct non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a two-step PMOR procedure, where, in the first step, a Proper Orthogonal Decomposition (POD)-based strategy that is non-intrusive to the simulator source code is introduced, as opposed to the convention of using POD as a simulator intrusive procedure. The non-intrusiveness of the proposed technique stems from formulating a novel Machine Learning (ML)-based framework used with POD. The features of the ML model (Random Forest was used here) are designed such that they take into consideration the temporal evolution of the state solutions and thereby avoid simulator access for the time dependency of the solutions. The proposed PMOR method is global, since a single reduced-order model can be used for all the well locations of interest in the reservoir. We address the major challenge of the explicit representation of the well location change as a parameter by introducing geometry-based features and flow diagnostics-inspired physics-based features. In the second step, an error correction model based on reduced model solutions is formulated to correct for discrepancies in the state solutions at well grid blocks expected from POD basis for new well locations. The error correction model proposed uses Artificial Neural Networks (ANNs) that consider the physics-based reduced model solutions as features, and is proved to reduce the error in QoI (Quantities of Interest), such as oil production rates and water cut, significantly. This workflow is applied to a simple homogeneous reservoir and a heterogeneous channelized reservoir using a section of SPE10 model that showed promising results in terms of model accuracy. Speed-ups of about 50×–100× were observed for different cases considered when running the test scenarios. The proposed workflow for Reduced-Order Modeling is “non-intrusive” and hence can increase its applicability to any simulator used. Additionally, the method is formulated such that all the simulation time steps are independent and hence can make use of parallel resources very efficiently and also avoid stability issues that can result from error accumulation over time steps.

scholarly journals Hercules: a profile HMM-based hybrid error correction algorithm for long reads

2017 ◽  
Author(s):  
Can Firtina ◽  
Ziv Bar-Joseph ◽  
Can Alkan ◽  
A. Ercument Cicek
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Machine Learning Algorithms ◽  
Read Length ◽  
Correction Algorithm ◽  
Short Reads ◽  
Error Profile ◽  
Long Reads ◽  
Long Read ◽  
Error Correction Algorithm

AbstractMotivationChoosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies.ResultsWe designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platform’s error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads.AvailabilityHercules source code is available at https://github.com/BilkentCompGen/Hercules

scholarly journals A comparison of combined data assimilation and machine learning methods for offline and online model error correction

2021 ◽  
pp. 101468
Author(s):  
Alban Farchi ◽  
Marc Bocquet ◽  
Patrick Laloyaux ◽  
Massimo Bonavita ◽  
Quentin Malartic
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Error Correction ◽  
Model Error ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Combined Data
Sign in / Sign up

Export Citation Format

Share Document