Evidence map›Paper›PMID 32235737›Full record

ArticleSensors (Basel, Switzerland)2020

Model Predictive Control via Output Feedback Neural Network for Improved Multi-Window Greenhouse Ventilation Control.

Dae-Hyun Jung, Hak-Jin Kim, Joon Yong Kim, Taek Sung Lee, Soo Hyun Park

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Dae-Hyun JungSmart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea.
Hak-Jin KimDepartment of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Joon Yong KimDepartment of Biosystems and Biomaterial Engineering, College of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Taek Sung LeeSmart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea.
Soo Hyun ParkSmart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung-si, Gangwon-do 25451, Korea.ORCID 0000-0002-5846-4233

Funding

National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea. Research Program for Agricultural Science & Technology Development (Project No. PJ01389103)
6 · The paper itself

Abstract

Maintaining environmental conditions for proper plant growth in greenhouses requires managing a variety of factors; ventilation is particularly important because inside temperatures can rise rapidly in warm climates. The structure of the window installed in a greenhouse is very diverse, and it is difficult to identify the characteristics that affect the temperature inside the greenhouse when multiple windows are driven, respectively. In this study, a new ventilation control logic using an output feedback neural-network (OFNN) prediction and optimization method was developed, and this approach was tested in multi-window greenhouses used for strawberry production. The developed prediction model used 15 inputs and achieved a highly accurate performance (R

Indexed as

greenhouse climate controlgreenhouse climate modelingmachine learningmulti-window ventilation

Identifiers

PMID32235737
PMCPMC7146503

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.