Research on Weigh-in-Motion Algorithm of Vehicles Based on BSO-BP.

Suan Xu, Xing Chen, Yaqiong Fu, Hongwei Xu, Kaixing Hong

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.7field-weighted citation impact, top 37% of its field

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

1 citing paper in PubMed, 9 citations in OpenAlex.

Weigh-in-Motion System Based on an Improved Kalman and LSTM-Attention Algorithm.Sensors (Basel, Switzerland) · 2022
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

5 authors at 1 institution in 1 country.

Suan XuSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Xing ChenSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Yaqiong FuSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Hongwei XuSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

Kaixing HongSchool of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.

China Jiliang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.

Indexed as

ColeopteraAlgorithmsAnimalsMotionNeural Networks, ComputerWavelet AnalysisBP neural networkBSO algorithmwavelet transformWIM

Identifiers

PMID35336283

DOI10.3390/s22062109

PMCPMC8948758

OpenAlexW4220794175

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.