{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T16:13:52Z","timestamp":1726762432076},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"am","delay-in-days":637,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Chemical Engineering"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1016\/j.compchemeng.2021.107445","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T09:21:07Z","timestamp":1626772867000},"page":"107445","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques"],"prefix":"10.1016","volume":"154","author":[{"given":"Kerul","family":"Suthar","sequence":"first","affiliation":[]},{"given":"Q. Peter","family":"He","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"year":"2018","series-title":"Neural Networks and Deep Learning - A Textbook, Machine Learning","author":"Aggarwal","key":"10.1016\/j.compchemeng.2021.107445_bib0001"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0002","series-title":"2017 2nd International Conference on Communication and Electronics Systems (ICCES). IEEE","first-page":"47","article-title":"Comparison of different diversity techniques in MIMO antennas","author":"Ahamed","year":"2017"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0003","doi-asserted-by":"crossref","DOI":"10.4067\/S0718-221X2020005000304","article-title":"Estimation of moisture in wood chips by near infrared spectroscopy","author":"Amaral","year":"2020","journal-title":"Maderas Cienc. y Tecnol."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0004","first-page":"431","article-title":"Standard test methods for moisture content of wood ASTM D4442","year":"2016","journal-title":"Annu. B. ASTM Stand."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0005","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0006","series-title":"Proceedings of the 12th Python in Science Conference","doi-asserted-by":"crossref","first-page":"13","DOI":"10.25080\/Majora-8b375195-003","article-title":"Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms","author":"Bergstra","year":"2013"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0007","series-title":"Advances in Neural Information Processing Systems","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"Bergstra","year":"2011"},{"year":"2006","series-title":"Machine Learning and Pattern Recognition, Information Science and Statistics","author":"Bishop","key":"10.1016\/j.compchemeng.2021.107445_bib0008"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0009","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1023\/A:1007563306331","article-title":"Pasting small votes for classification in large databases and on-line","volume":"36","author":"Breiman","year":"1999","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0010","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"year":"2015","series-title":"Bandwidth Study on Energy Use and Potential Energy Saving Opportunities in US Pulp and Paper Manufacturing","author":"Brueske","key":"10.1016\/j.compchemeng.2021.107445_bib0011"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0012","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0013","series-title":"Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0014","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.compchemeng.2019.06.025","article-title":"Modeling and control of cell wall thickness in batch delignification","volume":"128","author":"Choi","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0015","doi-asserted-by":"crossref","first-page":"e16589","DOI":"10.1002\/aic.16589","article-title":"Multiscale modeling and control of Kappa number and porosity in a batch-type pulp digester","volume":"65","author":"Choi","year":"2019","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0016","doi-asserted-by":"crossref","first-page":"3699","DOI":"10.1021\/acs.iecr.0c06216","article-title":"Inferential model predictive control of continuous pulping under grade transition","volume":"60","author":"Choi","year":"2021","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0017","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0018","doi-asserted-by":"crossref","DOI":"10.1080\/17480272.2019.1650828","article-title":"Real-time wood moisture-content determination using dual-energy X-ray computed tomography scanning","author":"Couceiro","year":"2019","journal-title":"Wood Mater. Sci. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0019","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1007\/s00226-018-1023-0","article-title":"Moisture content recognition for wood chips in pile using supervised classification","volume":"52","author":"Daassi-Gnaba","year":"2018","journal-title":"Wood Sci. Technol."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0020","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.neucom.2016.09.005","article-title":"Wood moisture content prediction using feature selection techniques and a kernel method","volume":"237","author":"Daassi-Gnaba","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0021","first-page":"52","article-title":"Precision and accuracy in moisture content determination of wood fuel chips using a handheld electric capacitance moisture meter","author":"Fridh","year":"2018","journal-title":"Silva Fenn."},{"year":"2001","series-title":"The Elements of Statistical Learning, Elements","author":"Friedman","key":"10.1016\/j.compchemeng.2021.107445_bib0022"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0023","article-title":"802.11 with multiple antennas for dummies","author":"Halperin","year":"2012","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0024","doi-asserted-by":"crossref","DOI":"10.1145\/1925861.1925870","article-title":"Tool release: gathering 802.11n traces with channel state information","author":"Halperin","year":"2011","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0025","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jprocont.2017.06.012","article-title":"Statistical process monitoring as a big data analytics tool for smart manufacturing","volume":"67","author":"He","year":"2018","journal-title":"J. Process Control"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0026","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/B978-0-444-64241-7.50340-2","article-title":"Statistics pattern analysis: a statistical process monitoring tool for smart manufacturing","volume":"44","author":"He","year":"2018","journal-title":"Comput. Aided Chem. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0027","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1002\/aic.12247","article-title":"Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes","volume":"57","author":"He","year":"2011","journal-title":"AIChE J."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0028","series-title":"Statistics Pattern Analysis Based Virtual Metrology for Plasma etch Processes","first-page":"4897","author":"He","year":"2012"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0029","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.compchemeng.2019.04.010","article-title":"Feature space monitoring for smart manufacturing via statistics pattern analysis","volume":"126","author":"He","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0030","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0031","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.measurement.2018.08.020","article-title":"A flow sensing method of power spectrum based on piezoelectric effect and vortex-induced vibrations","volume":"131","author":"Hu","year":"2019","journal-title":"Measurement"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0032","series-title":"2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings","first-page":"1","article-title":"MiFi: Device-free wheat mildew detection using off-the-shelf wifi devices","author":"Hu","year":"2019"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0033","doi-asserted-by":"crossref","DOI":"10.1007\/s10086-012-1260-z","article-title":"Determination of the moisture content in wood chips of Scots pine and Norway spruce using Mantex desktop scanner based on dual energy X-ray absorptiometry","author":"Hultn\u00e4s","year":"2012","journal-title":"J. Wood Sci."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0034","series-title":"Proceedings of the 13th Python in Science Conference","doi-asserted-by":"crossref","first-page":"32","DOI":"10.25080\/Majora-14bd3278-006","article-title":"Hyperopt-Sklearn: automatic hyperparameter configuration for Scikit-learn","author":"Komer","year":"2014"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0035","series-title":"Improving Energy Efficiency and Greenhouse Gas Reduction in the Pulp and Paper Industry","article-title":"Energy efficiency improvement and cost saving opportunities for the pulp and paper industry","author":"Kramer","year":"2011"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0036","article-title":"Determination of moisture content and basic density of poplar wood chips under various moisture conditions by near-infrared spectroscopy","author":"Liang","year":"2019","journal-title":"For. Sci."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0037","series-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","first-page":"346","article-title":"Ensembles on random patches","author":"Louppe","year":"2012"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0038","doi-asserted-by":"crossref","unstructured":"Martin, N., Anglani, N., Einstein, D., Khrushch, M., Worrell, E., Price, L.K., 2000. Opportunities to improve energy efficiency and reduce greenhouse gas emissions in the US pulp and paper industry. Lawrence Berkeley Natl. Lab.","DOI":"10.2172\/776606"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0039","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ab26a1","article-title":"Resonant half-wave antenna for moisture content assessment in wood chips","author":"Merlan","year":"2019","journal-title":"Meas. Sci. Technol."},{"year":"2016","series-title":"Neural Networks and Deep Learning, Neural Networks and Deep Learning","author":"Nielsen","key":"10.1016\/j.compchemeng.2021.107445_bib0040"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0041","doi-asserted-by":"crossref","first-page":"20","DOI":"10.3390\/s17010020","article-title":"Simultaneous moisture content and mass flow measurements in wood chip flows using coupled dielectric and impact sensors","volume":"17","author":"Pan","year":"2017","journal-title":"Sensors"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.biosystemseng.2015.12.005","article-title":"Predicting moisture content of chipped pine samples with a multi-electrode capacitance sensor","author":"Pan","year":"2016","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0043","article-title":"Scikit-learn: machine learning in python","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0044","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/pr8101231","article-title":"A review on the modeling, control and diagnostics of continuous pulp digesters","volume":"8","author":"Rahman","year":"2020","journal-title":"Processes"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0045","first-page":"66","article-title":"Moisture content by the oven-dry method for industrial testing","author":"Reeb","year":"1999","journal-title":"WDKA"},{"year":"1996","series-title":"Pattern Recognition and Neural Networks","author":"Ripley","key":"10.1016\/j.compchemeng.2021.107445_bib0046"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0047","series-title":"Proceedings of Foundations of Computer Aided Process Operations \/Chemical Process Control","first-page":"66","article-title":"Challenges and opportunities for IoT-enabled cybermanufacturing: what we learned from an IoT-enabled manufacturing technology testbed","author":"Shah","year":"2017"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2020.106970","article-title":"Feature engineering in big data analytics for iot-enabled smart manufacturing\u2013comparison between deep learning and statistical learning","author":"Shah","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0049","series-title":"IFAC-PapersOnLine. Florianopolis, Brazil","first-page":"562","article-title":"An internet-of-things enabled smart manufacturing testbed","author":"Shah","year":"2019"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0050","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jprocont.2019.03.016","article-title":"A feature-based soft sensor for spectroscopic data analysis","volume":"78","author":"Shah","year":"2019","journal-title":"J. Process. Control"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0051","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.compchemeng.2019.05.016","article-title":"Next-generation virtual metrology for semiconductor manufacturing: a feature-based framework","volume":"127","author":"Suthar","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0052","series-title":"Computer Aided Chemical Engineering","doi-asserted-by":"crossref","DOI":"10.1016\/B978-0-444-64241-7.50342-6","article-title":"Feature-based virtual metrology for semiconductor manufacturing","author":"Suthar","year":"2018"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0053","series-title":"Machine Learning","article-title":"Neural networks and deep learning","author":"Theodoridis","year":"2015"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0054","doi-asserted-by":"crossref","first-page":"7858","DOI":"10.1021\/ie901911p","article-title":"Multivariate statistical process monitoring based on statistics pattern analysis","volume":"49","author":"Wang","year":"2010","journal-title":"Ind. Eng. Chem. Res."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0055","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/TMC.2018.2860991","article-title":"Precise power delay profiling with commodity Wi-Fi","volume":"18","author":"Xie","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.compchemeng.2021.107445_bib0056","series-title":"2018 27th International Conference on Computer Communication and Networks (ICCCN). IEEE","first-page":"1","article-title":"Multi-class wheat moisture detection with 5GHz Wi-Fi: a deep LSTM approach","author":"Yang","year":"2018"},{"key":"10.1016\/j.compchemeng.2021.107445_bib0057","series-title":"2018 IEEE International Conference on Communications (ICC). IEEE","first-page":"1","article-title":"Wi-wheat: contact-free wheat moisture detection with commodity WiFi","author":"Yang","year":"2018"}],"container-title":["Computers & Chemical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135421002234?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098135421002234?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T06:00:17Z","timestamp":1711000817000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098135421002234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":57,"alternative-id":["S0098135421002234"],"URL":"https:\/\/doi.org\/10.1016\/j.compchemeng.2021.107445","relation":{},"ISSN":["0098-1354"],"issn-type":[{"type":"print","value":"0098-1354"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multiclass moisture classification in woodchips using IIoT Wi-Fi and machine learning techniques","name":"articletitle","label":"Article Title"},{"value":"Computers & Chemical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compchemeng.2021.107445","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"107445"}}