FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362471239> ?p ?o ?g. }

• W4362471239 endingPage "651" @default.
• W4362471239 startingPage "651" @default.
• W4362471239 abstract "Most of the current, popular approaches to monitoring real driving NOx emissions are based on direct measurement. However, due to the uncertainty of sensor-based measurements, such methods cannot always be used to accurately screen out the malfunctions of an emission control system. In this paper, a random forest (RF) model which extracts information from on-board diagnostics (OBD) data streams transmitted by a remote emission management vehicle terminal (REMVT) is proposed to provide a specific emission method for the online screening of high NOx emissions. First, two particular forms of modeling, random forest and logistic regression (LR), are laid out as representatives of nonparametric models and specified linear models. These two models were trained, validated and compared using OBD data collected from three China-VI heavy-duty diesel vehicles (HDDVs). The results show that as a data-driven, highly adaptive and robust learning method, the RF model can more accurately identify an abnormal emission state. Finally, a further validation was conducted, in which another China-VI HDDV was tested in two typical states, including a fault state and a normal state. The results indicated that the RF model could clearly distinguish the out-of-control emission condition from the normal operation state. The outcome of this research verifies the feasibility of using a machine learning model to process remote OBD data on HD vehicles and to identify high emissions in the case of an in-use fleet. On this basis, more sophisticated combined models and multi-stage models could be developed." @default.
• W4362471239 created "2023-04-05" @default.
• W4362471239 creator A5002389298 @default.
• W4362471239 creator A5003112585 @default.
• W4362471239 creator A5011084958 @default.
• W4362471239 creator A5012479343 @default.
• W4362471239 creator A5021263344 @default.
• W4362471239 creator A5051100284 @default.
• W4362471239 creator A5079063924 @default.
• W4362471239 creator A5083014172 @default.
• W4362471239 date "2023-03-30" @default.
• W4362471239 modified "2023-10-06" @default.
• W4362471239 title "Machine Learning-Aided Remote Monitoring of NOx Emissions from Heavy-Duty Diesel Vehicles Based on OBD Data Streams" @default.
• W4362471239 cites W1626737261 @default.
• W4362471239 cites W1983590837 @default.
• W4362471239 cites W1995945562 @default.
• W4362471239 cites W2020925091 @default.
• W4362471239 cites W2050297026 @default.
• W4362471239 cites W2074682976 @default.
• W4362471239 cites W2075490785 @default.
• W4362471239 cites W2099621088 @default.
• W4362471239 cites W2118978333 @default.
• W4362471239 cites W2122825543 @default.
• W4362471239 cites W2138019504 @default.
• W4362471239 cites W2148487094 @default.
• W4362471239 cites W2151440169 @default.
• W4362471239 cites W2216355671 @default.
• W4362471239 cites W2574781439 @default.
• W4362471239 cites W2787894218 @default.
• W4362471239 cites W2807839453 @default.
• W4362471239 cites W2911964244 @default.
• W4362471239 cites W2962727190 @default.
• W4362471239 cites W3021230159 @default.
• W4362471239 cites W3036704958 @default.
• W4362471239 cites W3082395187 @default.
• W4362471239 cites W3098834468 @default.
• W4362471239 cites W3106108064 @default.
• W4362471239 cites W3133647606 @default.
• W4362471239 cites W3174016383 @default.
• W4362471239 cites W3197578004 @default.
• W4362471239 cites W4283066205 @default.
• W4362471239 cites W4284887356 @default.
• W4362471239 cites W4285035468 @default.
• W4362471239 cites W4297957988 @default.
• W4362471239 doi "https://doi.org/10.3390/atmos14040651" @default.
• W4362471239 hasPublicationYear "2023" @default.
• W4362471239 type Work @default.
• W4362471239 citedByCount "0" @default.
• W4362471239 crossrefType "journal-article" @default.
• W4362471239 hasAuthorship W4362471239A5002389298 @default.
• W4362471239 hasAuthorship W4362471239A5003112585 @default.
• W4362471239 hasAuthorship W4362471239A5011084958 @default.
• W4362471239 hasAuthorship W4362471239A5012479343 @default.
• W4362471239 hasAuthorship W4362471239A5021263344 @default.
• W4362471239 hasAuthorship W4362471239A5051100284 @default.
• W4362471239 hasAuthorship W4362471239A5079063924 @default.
• W4362471239 hasAuthorship W4362471239A5083014172 @default.
• W4362471239 hasBestOaLocation W43624712391 @default.
• W4362471239 hasConcept C105923489 @default.
• W4362471239 hasConcept C119857082 @default.
• W4362471239 hasConcept C127313418 @default.
• W4362471239 hasConcept C127413603 @default.
• W4362471239 hasConcept C169258074 @default.
• W4362471239 hasConcept C171146098 @default.
• W4362471239 hasConcept C178790620 @default.
• W4362471239 hasConcept C185592680 @default.
• W4362471239 hasConcept C203032635 @default.
• W4362471239 hasConcept C39432304 @default.
• W4362471239 hasConcept C41008148 @default.
• W4362471239 hasConcept C62649853 @default.
• W4362471239 hasConcept C79403827 @default.
• W4362471239 hasConceptScore W4362471239C105923489 @default.
• W4362471239 hasConceptScore W4362471239C119857082 @default.
• W4362471239 hasConceptScore W4362471239C127313418 @default.
• W4362471239 hasConceptScore W4362471239C127413603 @default.
• W4362471239 hasConceptScore W4362471239C169258074 @default.
• W4362471239 hasConceptScore W4362471239C171146098 @default.
• W4362471239 hasConceptScore W4362471239C178790620 @default.
• W4362471239 hasConceptScore W4362471239C185592680 @default.
• W4362471239 hasConceptScore W4362471239C203032635 @default.
• W4362471239 hasConceptScore W4362471239C39432304 @default.
• W4362471239 hasConceptScore W4362471239C41008148 @default.
• W4362471239 hasConceptScore W4362471239C62649853 @default.
• W4362471239 hasConceptScore W4362471239C79403827 @default.
• W4362471239 hasIssue "4" @default.
• W4362471239 hasLocation W43624712391 @default.
• W4362471239 hasOpenAccess W4362471239 @default.
• W4362471239 hasPrimaryLocation W43624712391 @default.
• W4362471239 hasRelatedWork W1549947417 @default.
• W4362471239 hasRelatedWork W1967635675 @default.
• W4362471239 hasRelatedWork W1991159943 @default.
• W4362471239 hasRelatedWork W2187479699 @default.
• W4362471239 hasRelatedWork W2319003078 @default.
• W4362471239 hasRelatedWork W2358437126 @default.
• W4362471239 hasRelatedWork W2362936149 @default.
• W4362471239 hasRelatedWork W2790695452 @default.
• W4362471239 hasRelatedWork W2899084033 @default.
• W4362471239 hasRelatedWork W3161778363 @default.

• next