[{"publication":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","date_created":"2023-09-14T05:50:47Z","type":"conference_speech","user_id":"64952","date_updated":"2025-03-13T13:48:05Z","oa":"1","keyword":["Ethanol","Pipelines","Metals","Nose","Electronic noses","Sensor systems","Sensors","Quartz crystals","Linear discriminant analysis","Sulfur"],"language":[{"iso":"eng"}],"_id":"10326","doi":"https://doi.org/10.1109/INDIN51400.2023.10217912","title":"Key Indicators for the Discrimination of Wines by Electronic Noses","citation":{"ieee":"J. Wörner, H. Dörksen, and M. Pein-Hackelbusch, <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>. 2023. doi: <a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>.","ama":"Wörner J, Dörksen H, Pein-Hackelbusch M. <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>.; 2023. doi:<a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>","din1505-2-1":"<span style=\"font-variant:small-caps;\">Wörner, Julius</span> ; <span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Pein-Hackelbusch, Miriam</span>: <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>, 2023","chicago-de":"Wörner, Julius, Helene Dörksen und Miriam Pein-Hackelbusch. 2023. <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>. <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>. doi:<a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>, .","short":"J. Wörner, H. Dörksen, M. Pein-Hackelbusch, Key Indicators for the Discrimination of Wines by Electronic Noses, 2023.","chicago":"Wörner, Julius, Helene Dörksen, and Miriam Pein-Hackelbusch. <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>. <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>, 2023. <a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>.","apa":"Wörner, J., Dörksen, H., &#38; Pein-Hackelbusch, M. (2023). Key Indicators for the Discrimination of Wines by Electronic Noses. In <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>. 21st International Conference on Industrial Informatics (INDIN), Lemgo. <a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>","ufg":"<b>Wörner, Julius/Dörksen, Helene/Pein-Hackelbusch, Miriam</b>: Key Indicators for the Discrimination of Wines by Electronic Noses, o. O. 2023.","bjps":"<b>Wörner J, Dörksen H and Pein-Hackelbusch M</b> (2023) <i>Key Indicators for the Discrimination of Wines by Electronic Noses</i>. .","havard":"J. Wörner, H. Dörksen, M. Pein-Hackelbusch, Key Indicators for the Discrimination of Wines by Electronic Noses, 2023.","mla":"Wörner, Julius, et al. “Key Indicators for the Discrimination of Wines by Electronic Noses.” <i>2023 IEEE 21st International Conference on Industrial Informatics (INDIN)</i>, 2023, <a href=\"https://doi.org/10.1109/INDIN51400.2023.10217912\">https://doi.org/10.1109/INDIN51400.2023.10217912</a>.","van":"Wörner J, Dörksen H, Pein-Hackelbusch M. Key Indicators for the Discrimination of Wines by Electronic Noses. 2023 IEEE 21st International Conference on Industrial Informatics (INDIN). 2023."},"publication_status":"published","author":[{"first_name":"Julius","last_name":"Wörner","id":"79011","full_name":"Wörner, Julius"},{"id":"46416","last_name":"Dörksen","full_name":"Dörksen, Helene","first_name":"Helene"},{"orcid":"0000-0002-7920-0595","full_name":"Pein-Hackelbusch, Miriam","id":"64952","last_name":"Pein-Hackelbusch","first_name":"Miriam"}],"main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/document/10217912"}],"year":"2023","department":[{"_id":"DEP4000"},{"_id":"DEP4028"}],"conference":{"start_date":"2023-07-18","end_date":"2023-07-20","location":"Lemgo","name":"21st International Conference on Industrial Informatics (INDIN)"},"abstract":[{"lang":"eng","text":"In the food industry, and especially in wines as products thereof, ethanol and sulfur dioxide play an equally important role. Both substances are important wine quality characteristics as they influence the taste and odor. As both substances comprise volatile matter, electronic noses should be applicable to discriminate the different qualities of wines. Our study investigates the influence of alcohol and sulfur dioxide on the discrimination ability of wines (especially those of the same grape variety) using two different electronic nose systems. One system is equipped with metal oxide sensors and the other with quartz crystal microbalance sensors. Contrary to indications in literature, where the alcohol content is discussed to have a large influence on e-nose results, it was shown that a difference of 1 % ethanol was not sufficient to allow accurate discrimination using Linear Discriminant Analysis by any system. On the positive side, the analyzed concentrations of ethanol (about 12 %) did not superimpose other volatile information. So difference in sulfur dioxide content gave an accuracy for sample discrimination of up to 90.6 % with MOS nose. Thus, we are so far partially able to discriminate wines with electronic noses based on their volatile imprint."}],"status":"public"},{"title":"Linear Classification of Badly Conditioned Data. ","citation":{"van":"Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). Torino, Italy; 2018.","mla":"Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned Data. .” <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, 2018, doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">10.1109/ETFA.2018.8502485</a>.","bjps":"<b>Dörksen H and Lohweg V</b> (2018) Linear Classification of Badly Conditioned Data. . <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy.","havard":"H. Dörksen, V. Lohweg, Linear Classification of Badly Conditioned Data. , in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","ufg":"<b>Dörksen, Helene/Lohweg, Volker (2018)</b>: Linear Classification of Badly Conditioned Data. , in: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>, Torino, Italy.","apa":"Dörksen, H., &#38; Lohweg, V. (2018). Linear Classification of Badly Conditioned Data. . In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. <a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">https://doi.org/10.1109/ETFA.2018.8502485</a>","chicago":"Dörksen, Helene, and Volker Lohweg. “Linear Classification of Badly Conditioned Data. .” In <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018. <a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">https://doi.org/10.1109/ETFA.2018.8502485</a>.","short":"H. Dörksen, V. Lohweg, in: 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Torino, Italy, 2018.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy, 2018","chicago-de":"Dörksen, Helene und Volker Lohweg. 2018. Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485,\">10.1109/ETFA.2018.8502485,</a> .","ama":"Dörksen H, Lohweg V. Linear Classification of Badly Conditioned Data. . In: <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>. Torino, Italy; 2018. doi:<a href=\"https://doi.org/10.1109/ETFA.2018.8502485\">10.1109/ETFA.2018.8502485</a>","ieee":"H. Dörksen and V. Lohweg, “Linear Classification of Badly Conditioned Data. ,” in <i>23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</i>,  Turin, Italy , 2018."},"department":[{"_id":"DEP5023"}],"conference":{"location":" Turin, Italy ","name":" IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018","start_date":"2018-09-04","end_date":"2018-09-07"},"author":[{"id":"46416","full_name":"Dörksen, Helene","last_name":"Dörksen","first_name":"Helene"},{"id":"1804","full_name":"Lohweg, Volker","last_name":"Lohweg","first_name":"Volker","orcid":"0000-0002-3325-7887"}],"year":2018,"main_file_link":[{"open_access":"1","url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8502485"}],"abstract":[{"text":"We present a method for the fast and robust linear classification of badly conditioned data. In our considerations, badly conditioned data are such data which are numerically difficult to handle. Due to, e.g. a large number of features or a large number of objects representing classes as well as noise, outliers or incompleteness, the common software computation of the discriminating linear combination of features between classes fails or is extremely time consuming. The theoretical foundations of our approach are based on the single feature ranking, which allows fast calculation of the approximative initial classification boundary. For the increasing of classification accuracy of this boundary, the refinement is performed in the lower dimensional space. Our approach is tested on several datasets from UCI Reposi-tiory. Experimental results indicate high classification accuracy of the approach. For the modern real industrial applications such a method is especially suitable in the Cyber-Physical-System environments and provides a part of the workflow for the automated classifier design","lang":"eng"}],"status":"public","type":"conference","date_created":"2019-11-25T08:35:44Z","publication":"23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)","date_updated":"2023-03-15T13:49:38Z","user_id":"15514","oa":"1","language":[{"iso":"eng"}],"place":"Torino, Italy","keyword":["Task analysis","Software","Linear discriminant analysis","Dimensionality reduction","Mathematical model","Covariance matrices","Measurement"],"doi":"10.1109/ETFA.2018.8502485","_id":"2005"},{"abstract":[{"lang":"eng","text":"We concentrate our research activities on the multivariate feature selection, which is one important part of many machine learning tasks. In partucular, Linear Discriminant Analysis [1] belongs to the state-of-the-art methods for the multivariate analysis. From the theoretical point of view, it is the well-known fact that LDA is best suitable in the case the features are Gaussian distributed.\r\nIn the theoretical part of the presented paper, we analyse the properties of the multivariate discriminant analysis with respect to the feature selection. In this context, we consider a binary supervised learning task and assume that the features are Gaussian distributed. The discriminant analysis solves the mentioned supervised learning task by maximising of the discriminant value, calculated for the linear combination of the features.\r\nThe initial LDA solution a 2 Rd is considered for all given features from the feature space X \u001a Rd. The corresponding discriminant is calculated by the formula:\r\nd(a; x1, . . . , xd) := (μ+ − μ−)2\r\n\u001b2+\r\n+ \u001b2−\r\n,\r\nwhere μ+/− are projected class means and \u001b2 +/− are projected class variances (with respect to a). We proof several propositions with the aim to find subsets of the features having higher discriminant value as original d(a; x1, . . . , xd). For the suitability in the real world settings, here we are interested in fast searching for such subsets.\r\nThe performance of the mentioned propositions is examined experimentally on datasets from UCI repository [2]. Several application scenarien will be discussed and tested on the datasets. In addition, tests show that the performance can be achieved also in the case the features are not Gaussian distributed."}],"department":[{"_id":"DEP5023"}],"conference":{"end_date":"2018-07-06","start_date":"2018-07-04","name":"European Conference on Data Analysis","location":"Paderborn"},"author":[{"id":"46416","first_name":"Helene","full_name":"Dörksen, Helene","last_name":"Dörksen"},{"orcid":"0000-0002-3325-7887","full_name":"Lohweg, Volker","first_name":"Volker","last_name":"Lohweg","id":"1804"}],"year":2018,"status":"public","title":"Multivariate Gaussian Feature Selection. ","citation":{"chicago":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018.","apa":"Dörksen, H., &#38; Lohweg, V. (2018). Multivariate Gaussian Feature Selection. . In <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","havard":"H. Dörksen, V. Lohweg, Multivariate Gaussian Feature Selection. , in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.","mla":"Dörksen, Helene, and Volker Lohweg. “Multivariate Gaussian Feature Selection. .” <i>European Conference on Data Analysis (ECDA2018)</i>, 2018.","bjps":"<b>Dörksen H and Lohweg V</b> (2018) Multivariate Gaussian Feature Selection. . <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","ufg":"<b>Dörksen, Helene/Lohweg, Volker (2018)</b>: Multivariate Gaussian Feature Selection. , in: <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, Germany.","van":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: European Conference on Data Analysis (ECDA2018). Paderborn, Germany; 2018.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Dörksen, Helene</span> ; <span style=\"font-variant:small-caps;\">Lohweg, Volker</span>: Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany, 2018","chicago-de":"Dörksen, Helene und Volker Lohweg. 2018. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany.","short":"H. Dörksen, V. Lohweg, in: European Conference on Data Analysis (ECDA2018), Paderborn, Germany, 2018.","ieee":"H. Dörksen and V. Lohweg, “Multivariate Gaussian Feature Selection. ,” in <i>European Conference on Data Analysis (ECDA2018)</i>, Paderborn, 2018.","ama":"Dörksen H, Lohweg V. Multivariate Gaussian Feature Selection. . In: <i>European Conference on Data Analysis (ECDA2018)</i>. Paderborn, Germany; 2018."},"_id":"2008","language":[{"iso":"eng"}],"place":"Paderborn, Germany","keyword":["multivariate feature selection","Gaussian distribution","linear discriminant analysis"],"type":"conference","date_created":"2019-11-25T08:35:48Z","publication":"European Conference on Data Analysis (ECDA2018)","date_updated":"2023-03-15T13:49:38Z","user_id":"15514"}]