Evidence map›Paper›PMID 30866551›Full record

ArticleSensors (Basel, Switzerland)2019

Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data.

Sebin Park, Myeong-Seon Gil, Hyeonseung Im, Yang-Sae Moon

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sebin ParkDepartment of Computer Science, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea. sebinpark@kangwon.ac.kr.
Myeong-Seon GilDepartment of Computer Science, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea. gils@kangwon.ac.kr.
Hyeonseung ImDepartment of Computer Science, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea. hsim@kangwon.ac.kr.ORCID 0000-0002-3901-0834
Yang-Sae MoonDepartment of Computer Science, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea. ysmoon@kangwon.ac.kr.ORCID 0000-0002-2396-0405

Funding

Korea Electric Power Corporation R18XA05National Research Foundation of Korea 2016R1C1B1015095National Research Foundation of Korea 2017R1A2B4008991
6 · The paper itself

Abstract

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the

Indexed as

denoising autoencoderfiltering accuracykalman filteringmeasurement noisesensor datawavelet transform

Identifiers

PMID30866551
PMCPMC6427546

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.