Evidence map›Paper›PMID 36363924›Full record

ReviewMicromachines2022

Six-Degree-of-Freedom Posture Measurement Technologies Using Position Sensitive Detectors (PSDs): State of the Art.

Xiangxu Meng, Siwei Sun, Xuetao Yan, Fengman Liu, Liqiang Cao, Qidong Wang, Yu Sun

Abstract readReview
In one paragraph

Review in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Xiangxu MengSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Siwei SunSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0002-6568-6707
Xuetao YanSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Fengman LiuSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Liqiang CaoSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.
Qidong WangSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.ORCID 0000-0001-9176-5583
Yu SunSystem Packaging and Integration Research Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China.

Funding

Youth Innovation Promotion Association of the Chinese Academy of Science 2022114
6 · The paper itself

Abstract

Six degree-of-freedom (6-DOF) posture measurement is an important academic research topic which has been broadly applied in many fields. As a high-speed photoelectronic sensor with ultra-high resolution and precision, position sensitive detector (PSD) has shown to be one of the most competitive candidates in 6-DOF measurement. This review presents the research progress of PSD-based 6-DOF posture measurement systems in the field of large-scale equipment assembly, ultra-precision manufacturing and other emerging areas. A total of six methods for implementing 6-DOF measurement are summarized and their advantages and limitations are discussed. Meanwhile, the paper illustrates challenges, potential solutions and future development trends.

Indexed as

6-DOF posture measurementlarge-scale equipment assemblyposition sensitive detectorultra-precision manufacturing

Identifiers

PMID36363924
PMCPMC9695513

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.