http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113985853-A

Outgoing Links

Predicate Object
assignee http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d6a6f422b091ba12ea61d4adbf1b0e8e
classificationCPCAdditional http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B2219-24065
classificationCPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B23-0243
classificationIPCInventive http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G05B23-02
filingDate 2021-11-01-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8cae2c87c559e6f86d4e1350836bf0fb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0c60203e15970547d64965baf1f4cff3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6b19d8cce92f4ca915c542d1f1a8873f
publicationDate 2022-01-28-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber CN-113985853-A
titleOfInvention Industrial process visual monitoring method based on data dependence kernel discriminant analysis
abstract The invention discloses a polyethylene process visual monitoring method based on data dependence kernel discriminant analysis, which comprises the steps of firstly collecting normal working condition data and abnormal working condition data of an industrial process, establishing intra-class compactness and inter-class separation, constructing a space structure constraint term based on t distribution similarity and KL divergence, further establishing a data dependence kernel discriminant analysis optimization function, then calculating by using an interior point method to obtain a model numerical solution, and establishing a visual process monitoring model by using Diloni triangulation. Compared with the traditional algorithm, the method can greatly improve the accuracy of process monitoring and can provide more intuitive system running state and abnormal track for process operators.
isCitedBy http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116307407-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-116307407-A
priorityDate 2021-11-01-04:00^^<http://www.w3.org/2001/XMLSchema#date>
type http://data.epo.org/linked-data/def/patent/Publication

Incoming Links

Predicate Subject
isDiscussedBy http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID43672
http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419535645

Total number of triples: 18.