Evidence map›Paper›PMID 36141201›Full record

ArticleEntropy (Basel, Switzerland)2022

Multi-Sensor Scheduling Method Based on Joint Risk Assessment with Variable Weight.

Lin Zhou, Jiawei Wu, Qian Wei, Wentao Shi, Yong Jin

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Lin ZhouSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.ORCID 0000-0002-2700-834X
Jiawei WuSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.ORCID 0000-0003-3601-5944
Qian WeiSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.ORCID 0000-0002-3413-9292
Wentao ShiSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
Yong JinSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.ORCID 0000-0001-9325-5966

Funding

Graduate Education Innovation and Quality Improvement Action Plan project SYLKC2022013The Key Research Projects of University in Henan Province, China 21A413002, 20B510001The National Science Foundation Council of China 61976080, 61771006The Programs for Science and Technology Development of Henan Province, China 212102310298, 222102210002, 222102210004, 222102220028, 222102210088
6 · The paper itself

Abstract

In multi-sensor cooperative detection systems, to reduce target threat risk caused by attack tasks and target loss risk induced by uncertain environmental factors, this paper proposes a multi-sensor scheduling method based on joint risk assessment with variable weight. Firstly, considering the target state and prior expert experience of sensor scheduling, this paper gives a new scheme of target threat risk. Then, by combining the given target threat risk and the target loss risk, this paper constructs a joint risk model to meet the diversity of risk assessment. Secondly, a variable-weighted joint risk assessment model is given based on the adaptive weight of target loss risk and target threat risk, and the optimization problem of multi-sensor scheduling is described to minimize the multi-step prediction of the variable-weighted joint risk model. Finally, this paper relaxes above the non-convex optimization problem as a subconvex problem and designs the scheme of multi-sensor scheduling, improving the rapidity and optimization of the sensor scheduling solution. The simulation results show that the proposed method can adaptively schedule sensors and accurately track targets by using minimum sensor resources.

Indexed as

convex optimizationmulti-sensor schedulingmulti-step predictionrisk assessment

Identifiers

PMID36141201
PMCPMC9497982

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LicenceCC BY
Read underepoch 390

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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.