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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Borghesan, F., Chioua, M., & Thornhill, N. F. (2018, July). Forecasting of process disturbances using k-nearest neighbours, with an application in process control [Paper]. 13th International Symposium on Process Systems Engineering (PSE 2018), San Diego, CA. Published in Computers & Chemical Engineering, 128. External link
Borghesan, F., Chioua, M., & Thornhill, N. F. (2018, July). Forecast of persistent disturbances using k-nearest neighbour methods [Paper]. 13th International Symposium on Process Systems Engineering (PSE 2018), San Diego, CA. Published in Computer Aided Chemical Engineering, 44. External link
Borghesan, F., Chioua, M., & Thornhill, N. F. (2018, June). An MPC with Disturbance Forecasting for the Control of the Level of a Tank with Limited Buffer Capacity [Paper]. 26th Mediterranean Conference on Control and Automation (MED 2018), Zadar, Croatia (9 pages). External link
Chioua, M., Bauer, M., Chen, S.-L., Schlake, J. C., Sand, G., Schmidt, W., & Thornhill, N. F. (2016). Plant-wide root cause identification using plant key performance indicators (KPIs) with application to a paper machine. Control Engineering Practice, 49, 149-158. External link
Lucke, M., Chioua, M., & Thornhill, N. F. (2022). From oscillatory to non-oscillatory disturbances: A comparative review of root cause analysis methods. Journal of Process Control, 113, 42-67. External link
Lucke, M., Stief, A., Chioua, M., Ottewill, J. R., & Thornhill, N. F. (2020). Fault detection and identification combining process measurements and statistical alarms. Control Engineering Practice, 94, 12 pages. External link
Lucke, M., Chioua, M., Grimholt, C., Hollender, M., & Thornhill, N. F. (2020). Integration of alarm design in fault detection and diagnosis through alarm-range normalization. Control Engineering Practice, 98, 12 pages. External link
Lucke, M., Chioua, M., Grimholt, C., Hollender, M., & Thornhill, N. F. (2019). Advances in alarm data analysis with a practical application to online alarm flood classification. Journal of Process Control, 79, 56-71. External link
Lucke, M., Mei, X., Stief, A., Chioua, M., & Thornhill, N. F. (2019, April). Variable Selection for Fault Detection and Identification based on Mutual Information of Alarm Series [Paper]. 12th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS 2019), Florianópolis, Brazil. Published in IFAC-PapersOnLine, 52(1). External link
Lucke, M., Chioua, M., Grimholt, C., Hollender, M., & Thornhill, N. F. (2018, August). On improving fault detection and diagnosis using alarm-range normalisation [Paper]. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS 2018), Warsaw, Poland. Published in IFAC-PapersOnLine, 51(24). External link
Lucke, M., Chioua, M., Grimholt, C., Hollender, M., & Thornhill, N. F. (2018, July). Online Alarm Flood Classification Using Alarm Coactivations [Paper]. 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 2018), Shenyang, China. Published in IFAC-PapersOnLine, 51(18). External link
Zhou, B., Chioua, M., Bauer, M., Schlake, J. C., & Thornhill, N. F. (2019). Improving Root Cause Analysis by Detecting and Removing Transient Changes in Oscillatory Time Series with Application to a 1,3-Butadiene Process. Industrial & Engineering Chemistry Research, 58(26), 11234-11250. External link
