http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109960873-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_4c0ed78c4d5c4951a22374d61ea1f178 |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/Y02W90-00 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B13-042 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B13-048 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F30-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G05B13-027 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06Q10-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-50 |
| filingDate | 2019-03-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_45377b4a3f2234931a6d2dea8fc4bc12 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c80cfd7308df23dcbd7a590a1d2c6fe9 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_54c8eab7ed0e23ac07b8972f2c0b4146 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_7879918f2acf560cff0732d9e784ce00 |
| publicationDate | 2019-07-02-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-109960873-A |
| titleOfInvention | A soft measurement method for dioxin emission concentration in urban solid waste incineration process |
| abstract | The invention discloses a DXN emission concentration soft measurement method based on multi-source latent feature selective integration (SEN) modeling. First, the MSWI process data is divided into subsystems from different sources according to the industrial process, and the potential features are extracted by principal component analysis (PCA), and the multi-source potential features are initially selected according to the threshold of the principal component contribution rate preset by experience; then , using mutual information (MI) to measure the correlation between the potential features of the primary selection and DXN, and adaptively determine the upper and lower bounds and thresholds of the potential feature re-selection; finally, based on the re-selection potential features, adopt a hyperparameter adaptive selection mechanism. The least squares-support vector machine (LS-SVM) algorithm is used to establish DXN emission concentration sub-models for different subsystems, and a strategy based on branch and bound (BB) and prediction error information entropy weighting algorithm is used to optimize the selection of sub-models and calculation weights coefficient to construct the SEN soft-sensor model of DXN emission concentration. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023083009-A1 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023165635-A1 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023138140-A1 http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020192166-A1 |
| priorityDate | 2019-03-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 32.