TY - GEN A1 - Dorneanu, Bogdan A1 - Zhang, Sushen A1 - Ruan, Hang A1 - Heshmat, Mohamed A1 - Chen, Ruijuan A1 - Vassiliadis, Vassilios S. A1 - Arellano-García, Harvey T1 - Big data and machine learning: A roadmap towards smart plants T2 - Frontiers of Engineering Management N2 - Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as the addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management. Y1 - 2022 UR - https://link.springer.com/article/10.1007/s42524-022-0218-0 U6 - https://doi.org/10.1007/s42524-022-0218-0 VL - 9 SP - 623 EP - 639 ER - TY - GEN A1 - Gao, Yunlong A1 - Luo, Si-Zhe A1 - Pan, Jin-Yan A1 - Chen, Bai-Hua A1 - Zhang, Yi-Song T1 - Robust PCA Using Adaptive Probability Weighting T2 - Acta Automatica Sinica N2 - Principal component analysis (PCA) is an important method for processing high-dimensional data. In recent years, PCA models based on various norms have been extensively studied to improve the robustness. However, on the one hand, these algorithms do not consider the relationship between reconstruction error and covariance; on the other hand, they lack the uncertainty of considering the principal component to the data description. Aiming at these problems, this paper proposes a new robust PCA algorithm. Firstly, the L2,p-norm is used to measure the reconstruction error and the description variance of the projection data. Based on the reconstruction error and the description variance, the adaptive probability error minimization model is established to calculate the uncertainty of the principal component's description of the data. Based on the uncertainty, the adaptive probability weighting PCA is established. The corresponding optimization method is designed. The experimental results of artificial data sets, UCI data sets and face databases show that RPCA-PW is superior than other PCA algorithms. KW - Principle component analysis (PCA) KW - weighted principal component analysis (WPCA) KW - dimensionality reduction KW - robustness Y1 - 2021 U6 - https://doi.org/10.16383/j.aas.c180743 SN - 0254-4156 VL - 47 IS - 4 SP - 825 EP - 838 ER - TY - GEN A1 - Buchanan, Erin M. A1 - Lewis, Savannah C. A1 - Paris, Bastien A1 - Forscher, Patrick S. A1 - Pavlacic, Jeffrey M. A1 - Beshears, Julie E. A1 - Drexler, Shira Meir A1 - Gourdon-Kanhukamwe, Amélie A1 - Mallik, Peter R A1 - Silan, Miguel Alejandro A. A1 - Miller, Jeremy K. A1 - IJzerman, Hans A1 - Moshontz, Hannah A1 - Beaudry, Jennifer L. A1 - Suchow, Jordan W. A1 - Chartier, Christopher R. A1 - Coles, Nicholas A. A1 - Sharifian, MohammadHasan A1 - Todsen, Anna Louise A1 - Levitan, Carmel A. A1 - Azevedo, Flávio A1 - Legate, Nicole A1 - Heller, Blake A1 - Rothman, Alexander J. A1 - Dorison, Charles A. A1 - Gill, Brian P. A1 - Wang, Ke A1 - Rees, Vaughan W. A1 - Gibbs, Nancy A1 - Goldenberg, Amit A1 - Thi Nguyen, Thuy-vy A1 - Gross, James J. A1 - Kaminski, Gwenaêl A1 - von Bastian, Claudia C. A1 - Paruzel-Czachura, Mariola A1 - Mosannenzadeh, Farnaz A1 - Azouaghe, Soufian A1 - Bran, Alexandre A1 - Ruiz-Fernandez, Susana A1 - Santos, Anabela Caetano A1 - Reggev, Niv A1 - Zickfeld, Janis H. A1 - Akkas, Handan A1 - Pantazi, Myrto A1 - Ropovik, Ivan A1 - Korbmacher, Max A1 - Arriaga, Patrícia A1 - Gjoneska, Biljana A1 - Warmelink, Lara A1 - Alves, Sara G. A1 - de Holanda Coelho, Gabriel Lins A1 - Stieger, Stefan A1 - Schei, Vidar A1 - Hanel, Paul H. P. A1 - Szaszi, Barnabas A1 - Fedotov, Maksim A1 - Antfolk, Jan A1 - Marcu, Gabriela-Mariana A1 - Schrötter, Jana A1 - Kunst, Jonas R. A1 - Geiger, Sandra J. A1 - Adetula, Adeyemi A1 - Kocalar, Halil Emre A1 - Kielińska, Julita A1 - Kačmár, Pavol A1 - Bokkour, Ahmed A1 - Galindo-Caballero, Oscar J. A1 - Djamai, Ikhlas A1 - Pöntinen, Sara Johanna A1 - AGESIN, Bamikole Emmanuel A1 - Jernsäther, Teodor A1 - Urooj, Anum A1 - Rachev, Nikolay R. A1 - Koptjevskaja-Tamm, Maria A1 - Kurfalı, Murathan A1 - Pit, Ilse L. A1 - Li, Ranran A1 - Çoksan, Sami A1 - Dubrov, Dmitrii A1 - Paltrow, Tamar Elise A1 - Baník, Gabriel A1 - Korobova, Tatiana A1 - Studzinska, Anna A1 - Jiang, Xiaoming A1 - Aruta, John Jamir Benzon R. A1 - Vintr, Jáchym A1 - Chiu, Faith A1 - Kaliska, Lada A1 - Berkessel, Jana B. A1 - Tümer, Murat A1 - Morales-Izquierdo, Sara A1 - Chuan-Peng, Hu A1 - Vezirian, Kevin A1 - Rosa, Anna Dalla A1 - Bialobrzeska, Olga A1 - Vasilev, Martin R. A1 - Beitner, Julia A1 - Kácha, Ondřej A1 - Žuro, Barbara A1 - Westerlund, Minja A1 - Nedelcheva-Datsova, Mina A1 - Findor, Andrej A1 - Krupić, Dajana A1 - Kowal, Marta A1 - Askelund, Adrian Dahl A1 - Pourafshari, Razieh A1 - Đorđević, Jasna Milošević A1 - Schmidt, Nadya-Daniela A1 - Baklanova, Ekaterina A1 - Szala, Anna A1 - Zakharov, Ilya A1 - Vranka, Marek A. A1 - Ihaya, Keiko A1 - Grano, Caterina A1 - Cellini, Nicola A1 - Białek, Michał A1 - Anton-Boicuk, Lisa A1 - Dalgar, Ilker A1 - Adıgüzel, Arca A1 - Verharen, Jeroen P. H. A1 - Maturan, Princess Lovella G. A1 - Kassianos, Angelos P. A1 - Oliveira, Raquel A1 - Čadek, Martin A1 - Adoric, Vera Cubela A1 - Özdoğru, Asil Ali A1 - Sverdrup, Therese E. A1 - Aczel, Balazs A1 - Zambrano, Danilo A1 - Ahmed, Afroja A1 - Tamnes, Christian K. A1 - Yamada, Yuki A1 - Volz, Leonhard A1 - Sunami, Naoyuki A1 - Suter, Lilian A1 - Vieira, Luc A1 - Groyecka-Bernard, Agata A1 - Kamburidis, Julia Arhondis A1 - Reips, Ulf-Dietrich A1 - Harutyunyan, Mikayel A1 - Adetula, Gabriel Agboola A1 - Allred, Tara Bulut A1 - Barzykowski, Krystian A1 - Antazo, Benedict G A1 - Zsido, Andras N. A1 - Šakan, Dušana Dušan A1 - Cyrus-Lai, Wilson A1 - Ahlgren, Lina Pernilla A1 - Hruška, Matej A1 - Vega, Diego A1 - Manunta, Efisio A1 - Mokady, Aviv A1 - Capizzi, Mariagrazia A1 - Martončik, Marcel A1 - Say, Nicolas A1 - Filip, Katarzyna A1 - Vilar, Roosevelt A1 - Staniaszek, Karolina A1 - Vdovic, Milica A1 - Adamkovic, Matus A1 - Johannes, Niklas A1 - Hajdu, Nandor A1 - Cohen, Noga A1 - Overkott, Clara A1 - Krupić, Dino A1 - Hubena, Barbora A1 - Nilsonne, Gustav A1 - Mioni, Giovanna A1 - Solorzano, Claudio Singh A1 - Ishii, Tatsunori A1 - Chen, Zhang A1 - Kushnir, Elizaveta A1 - Karaarslan, Cemre A1 - Ribeiro, Rafael R. A1 - Khaoudi, Ahmed A1 - Kossowska, Małgorzata A1 - Bavolar, Jozef A1 - Hoyer, Karlijn A1 - Roczniewska, Marta A1 - Karababa, Alper A1 - Becker, Maja A1 - Monteiro, Renan P. A1 - Kunisato, Yoshihiko A1 - Metin-Orta, Irem A1 - Adamus, Sylwia A1 - Kozma, Luca A1 - Czarnek, Gabriela A1 - Domurat, Artur A1 - Štrukelj, Eva A1 - Alvarez, Daniela Serrato A1 - Parzuchowski, Michal A1 - Massoni, Sébastien A1 - Czamanski-Cohen, Johanna A1 - Pronizius, Ekaterina A1 - Muchembled, Fany A1 - van Schie, Kevin A1 - Saçaklı, Aslı A1 - Hristova, Evgeniya A1 - Kuzminska, Anna O. A1 - Charyate, Abdelilah A1 - Bijlstra, Gijsbert A1 - Afhami, Reza A1 - Majeed, Nadyanna M. A1 - Musser, Erica D. A1 - Sirota, Miroslav A1 - Ross, Robert M. A1 - Yeung, Siu Kit A1 - Papadatou-Pastou, Marietta A1 - Foroni, Francesco A1 - Almeida, Inês A. T. A1 - Grigoryev, Dmitry A1 - Lewis, David M. G. A1 - Holford, Dawn L. A1 - Janssen, Steve M. J. A1 - Tatachari, Srinivasan A1 - Batres, Carlota A1 - Olofsson, Jonas K. A1 - Daches, Shimrit A1 - Belaus, Anabel A1 - Pfuhl, Gerit A1 - Corral-Frias, Nadia Sarai A1 - Sousa, Daniela A1 - Röer, Jan Philipp A1 - Isager, Peder Mortvedt A1 - Godbersen, Hendrik A1 - Walczak, Radoslaw B. A1 - Van Doren, Natalia A1 - Ren, Dongning A1 - Gill, Tripat A1 - Voracek, Martin A1 - DeBruine, Lisa M. A1 - Anne, Michele A1 - Očovaj, Sanja Batić A1 - Thomas, Andrew G. A1 - Arvanitis, Alexios A1 - Ostermann, Thomas A1 - Wolfe, Kelly A1 - Arinze, Nwadiogo Chisom A1 - Bundt, Carsten A1 - Lamm, Claus A1 - Calin-Jageman, Robert J A1 - Davis, William E. A1 - Karekla, Maria A1 - Zorjan, Saša A1 - Jaremka, Lisa M. A1 - Uttley, Jim A1 - Hricova, Monika A1 - Koehn, Monica A A1 - Kiselnikova, Natalia A1 - Bai, Hui A1 - Krafnick, Anthony J. A1 - Balci, Busra Bahar A1 - Ballantyne, Tonia A1 - Lins, Samuel A1 - Vally, Zahir A1 - Esteban-Serna, Celia A1 - Schmidt, Kathleen A1 - Macapagal, Paulo Manuel L. A1 - Szwed, Paulina A1 - Zdybek, Przemysław Marcin A1 - Moreau, David A1 - Collins, W. Matthew A1 - Joy-Gaba, Jennifer A. A1 - Vilares, Iris A1 - Tran, Ulrich S. A1 - Boudesseul, Jordane A1 - Albayrak-Aydemir, Nihan A1 - Dixson, Barnaby James Wyld A1 - Perillo, Jennifer T A1 - Ferreira, Ana A1 - Westgate, Erin C. A1 - Aberson, Christopher L. A1 - Arinze, Azuka Ikechukwu A1 - Jaeger, Bastian A1 - Butt, Muhammad Mussaffa A1 - Silva, Jaime R. A1 - Storage, Daniel Shafik A1 - Janak, Allison P A1 - Jiménez-Leal, William A1 - Soto, Jose A. A1 - Sorokowska, Agnieszka A1 - McCarthy, Randy A1 - Tullett, Alexa M A1 - Frias-Armenta, Martha A1 - Ribeiro, Matheus Fernando Felix A1 - Hartanto, Andree A1 - Forbes, Paul A. G. A1 - Willis, Megan L. A1 - del Carmen Tejada R, María A1 - Torres, Adriana Julieth Olaya A1 - Stephen, Ian D A1 - Vaidis, David C. A1 - de la Rosa-Gómez, Anabel A1 - Yu, Karen A1 - Sutherland, Clare A. M. A1 - Manavalan, Mathi A1 - Behzadnia, Behzad A1 - Urban, Jan A1 - Baskin, Ernest A1 - McFall, Joseph P. A1 - Ogbonnaya, Chisom Esther A1 - Fu, Cynthia H. Y. A1 - Rahal, Rima-Maria A1 - Ndukaihe, Izuchukwu L. G. A1 - Hostler, Thomas J. A1 - Kappes, Heather Barry A1 - Sorokowski, Piotr A1 - Khosla, Meetu A1 - Lazarevic, Ljiljana B. A1 - Eudave, Luis A1 - Vilsmeier, Johannes K. A1 - Luis, Elkin O. A1 - Muda, Rafał A1 - Agadullina, Elena A1 - Cárcamo, Rodrigo A. A1 - Reeck, Crystal A1 - Anjum, Gulnaz A1 - Venegas, Mónica Camila Toro A1 - Misiak, Michal A1 - Ryan, Richard M. A1 - Nock, Nora L. A1 - Travaglino, Giovanni A. A1 - Mensink, Michael C. A1 - Feldman, Gilad A1 - Wichman, Aaron L. A1 - Chou, Weilun A1 - Ziano, Ignazio A1 - Seehuus, Martin A1 - Chopik, William J. A1 - Kung, Franki Y. H. A1 - Carpentier, Joelle A1 - Vaughn, Leigh Ann A1 - Du, Hongfei A1 - Xiao, Qinyu A1 - Lima, Tiago J. S. A1 - Noone, Chris A1 - Onie, Sandersan A1 - Verbruggen, Frederick A1 - Radtke, Theda A1 - Primbs, Maximilian A. T1 - The psychological science accelerator’s COVID-19 rapid-response dataset T2 - Scientific Data Y1 - 2023 U6 - https://doi.org/10.1038/s41597-022-01811-7 SN - 2052-4463 VL - 10 IS - 1 SP - 1 EP - 15 PB - Springer Science and Business Media LLC ER - TY - GEN A1 - Gao, Yunlong A1 - Zhang, Yisong A1 - Chen, Baihua A1 - Xiong, Yuhui T1 - Local Neighborhood Reliability Weighted Support Vector Machine T2 - 2020 2nd International Conference on Industrial Artificial Intelligence (IAI) N2 - Support vector machine (SVM) is a classification model, which learns the decision surface that maximizes the margin in the feature space. Such a decision surface has a good classification ability for unknown new samples. In real-world applications, the data set usually contains many noises and outliers, which will affect the learning of the decision surface, thus the maximum margin cannot be obtained, and the generalization ability of SVM will be reduced. In this paper, we introduce an adjacency factor to each input point to characterize the local neighbor relationship between each point. Weighting each sample point by the adjacency factor can let different sample points make different contributions to the learning of the decision surface. Thus, we can filter out the influence of noises and outliers on the decision surface by this weighting method. We propose this new method namely local neighborhood reliability weighted support vector machine (LN-SVM). KW - SVM KW - margin KW - local neighbor KW - adjacency factor KW - outlier Y1 - 2020 SN - 978-1-7281-8216-2 SN - 978-1-7281-8217-9 U6 - https://doi.org/10.1109/IAI50351.2020.9262215 SP - 1 EP - 6 PB - IEEE ER -