Initial development and subsequent calibration of discrete event simulation models for complex systems require accurate identification of dynamically changing process characteristics. Existing data driven change point methods (DD-CPD) assume changes are extraneous to the system, thus cannot utilize available process knowledge. This work proposes a unified framework for process-driven multi-variate change point detection (PD-CPD) by combining change point detection models with machine learning and process-driven simulation modeling. The PD-CPD, after initializing with DD-CPD's change point(s), uses simulation models to generate system level outputs as time-series data streams which are then used to train neural network models to predict system characteristics and change points. The accuracy of the predictive models measures the likelihood that the actual process data conforms to the simulated change points in system characteristics. PD-CPD iteratively optimizes change points by repeating simulation and predictive model building steps until the set of change point(s) with the maximum likelihood is identified. Using an emergency department case study, we show that PD-CPD significantly improves change point detection accuracy over DD-CPD estimates and is able to detect actual change points.
Active Learning and Novel Model Calibration Measurements for Automated Visual Inspection in Manufacturing
Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Qlector d.o.o.（Qlector有限责任公司）；Faculty of Electrical Engineering, University of Ljubljana（卢布尔雅那大学电气工程学院）；Philips Consumer Lifestyle BV（飞利浦消费生活方式公司）
Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework
University of Vermont Larner College of Medicine（佛蒙特大学拉纳医学院）
Deep Learning-Based Discrete Calibrated Survival Prediction
Institute of Medical Systems Biology（医学生物学研究所）；Center for Biomedical AI (bAIome)（生物医学人工智能中心（bAIome））；Center for Molecular Neurobiology (ZMNH)（分子神经生物学中心（ZMNH））；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
Recurrent Spectral Network (RSN): shaping the basin of attraction of a discrete map to reach automated classification
Università di Firenze（佛罗伦萨大学）；INFN（意大利国家核物理研究所）；CSDC（复杂系统动力学研究中心）；University of Namur（那慕尔大学）；University of Lisbon（里斯本大学）；Instituto de Telecomunicacoes（电信研究所）
