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Conference on Control and Fault-Tolerant Systems

作者： Adel Haghani Steven X. Ding Torsten Jeinsch Haiyang Hao Hao Luo Institute of Automation University of Rostock Institute for Automatic Control and Complex Systems (AKS) University of Duisburg-Essen

ISBN: (纸本)9781479928569

This paper addresses the problem of optimal maintenance action generation in industrial processes. After successful detection and isolation of the faults, the system has to be recovered from the abnormal situation. The faulty component should be repaired or replaced or a proper control strategy reconfiguration should be applied. It is the scope of this paper to design a decision support system to guide the human operator to perform the most proper corrective operation based on the assessment of the current plant situation and constraints. In this paper, it is shown, how the results of fault diagnosis techniques, in the form of residual signals or performance monitoring indices can be used in condition-based maintenance to generate proper maintenance operations. To this aim, the proposed system integrates the fault diagnosis results with economical evaluation of possible corrective operations to find the best possible correction with high impact on the plant's overall performance and low costs.

关键词： Fault detection and isolation Quality monitoring Decision making data-driven methods Corrective action

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Learning to Continuously Optimize Wireless Resource in Episodically Dynamic Environment

Learning to Continuously Optimize Wireless Resource in Episo...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Haoran Sun Wenqiang Pu Minghe Zhu Xiao Fu Tsung-Hui Chang Mingyi Hong University of Minnesota Minneapolis MN USA Shenzhen Research Institute of Big Data Shenzhen China The Chinese University of Hong Kong Shenzhen China School of EECS Oregon State University Corvallis OR USA

There has been a growing interest in developing data-driven, in particular deep neural network (DNN) based methods for modern communication tasks. For a few popular tasks such as power control, beamforming, and MIMO detection, these methods achieve state-of-the-art performance while requiring less computational efforts, less channel state information (CSI), etc. However, it is often challenging for these approaches to learn in a dynamic environment where parameters such as CSIs keep changing. This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. Specifically, we consider an "episodically dynamic" setting where the environment changes in "episodes", and in each episode the environment is stationary. We propose a continual learning (CL) framework for wireless systems, which can incrementally adapt the learning models to the new episodes, without forgetting models learned from the previous episodes. Our design is based on a novel min-max formulation which ensures certain "fairness" across different episodes. Finally, we demonstrate the effectiveness of the CL approach by customizing it to a popular DNN based model for power control, and testing using both synthetic and real data.

关键词： Deep learning continual learning wireless communication data-driven methods

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CCP: Configurable Crowd Profiles 22

CCP: Configurable Crowd Profiles

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ACM SIGGRAPH 2022 Conference Proceedings

作者： Andreas Panayiotou Theodoros Kyriakou Marilena Lemonari Yiorgos Chrysanthou Panayiotis Charalambous V-EUPNEA: Living Breathing Virtual Worlds CYENS - Centre of Excellence Cyprus Computer Science University of Cyprus Cyprus Computer Science University of Cyprus Cyprus and Research CYENS - Centre of Excellence Cyprus

ISBN: (纸本)9781450393379

Diversity among agents’ behaviors and heterogeneity in virtual crowds in general, is an important aspect of crowd simulation as it is crucial to the perceived realism and plausibility of the resulting simulations. Heterogeneous crowds constitute the pillar in creating numerous real-life scenarios such as museum exhibitions, which require variety in agent behaviors, from basic collision avoidance to more complex interactions both among agents and with environmental features. Most of the existing systems optimize for specific behaviors such as goal seeking, and neglect to take into account other behaviors and how these interact together to form diverse agent profiles. In this paper, we present a RL-based framework for learning multiple agent behaviors concurrently. We optimize the agent policy by varying the importance of the selected behaviors (goal seeking, collision avoidance, interaction with environment, and grouping) while training; essentially we have a reward function that changes dynamically during training. The importance of each separate sub-behavior is added as input to the policy, resulting in the development of a single model capable of capturing as well as enabling dynamic run-time manipulation of agent profiles; thus allowing configurable profiles. Through a series of experiments, we verify that our system provides users with the ability to design virtual scenes; control and mix agent behaviors thus creating personality profiles, and assign different profiles to groups of agents. Moreover, we demonstrate that interestingly the proposed model generalizes to situations not seen in the training data such as a) crowds with higher density, b) behavior weights that are outside the training intervals and c) to scenes with more intricate environment layouts. Code, data and trained policies for this paper are at https://***/veupnea/CCP.

关键词： user control crowd authoring crowd simulation reinforcement learning. data-driven methods

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Subgrouping and structural brain connectivity of Parkinson's disease – past studies and future directions

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Neuroscience Informatics 2022年第4期2卷 100100-100100页

作者： Samantaray, Tanmayee Saini, Jitender Gupta, Cota Navin Neural Engineering Lab Department of Biosciences and Bioengineering Indian Institute of Technology Guwahati India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences India

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help understand the disease etiology and thereby contribute toward specific treatment. However, clinical symptoms have proven to be overlapping, arbitrary, and non-reliable in several cases, often biasing the deciphered subgroups. Moreover, the prodromal phase complicates diagnosis and subgrouping as it is characterized by limited clinical symptom expression. Hence, recent studies have used data-driven machine learning and deep learning methods to data-mine the heterogeneity and obtain subgroups. Structural Magnetic Resonance Imaging (sMRI) is a non-invasive approach for visualization and analysis of anatomical tissue properties of brain. It has enabled the detection of brain abnormalities and is a potential modality for subgrouping. This review article starts with a comprehensive discussion of clinical symptoms-based and data-driven structural neuroimaging-based subgrouping approaches in PD. Secondly, we summarize the work done in brain connectivity studies using structural MRI for PD. We give an overview of mathematical definitions, connectivity metrics, brain connectivity software, and widespread network atlases. Finally, we discuss the inherent challenges and give practical suggestions on selecting methods that could be attempted for subgrouping and connectivity analysis using structural MRI data for future Parkinson's research. © 2022 The Author(s)

关键词： Clinical symptoms data-driven methods Parkinson's disease Structural brain network connectivity Structural neuroimaging-based subgrouping

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Co-design of reduced-order models and observers from thermo-fluid data

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IFAC JOURNAL OF SYSTEMS AND CONTROL 2022年 19卷

作者： Vijayshankar, Sanjana Chakrabarty, Ankush Grover, Piyush Nabi, Saleh Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA Univ Nebraska Dept Mech & Mat Engn Lincoln NE 68588 USA

This paper presents a method of co-design of models and observers for buoyancy-driven turbulent flows. Recent work on data-driven techniques for estimating turbulent flows typically involve obtaining a dynamical model using Dynamical Mode Decomposition (DMD) and using the model to design estimators. Unfortunately, such a sequential design could result in state-space models that do not possess control-theoretic properties (such as detectability) that ensure guaranteed performance of the observer. In this paper, we propose semi-definite programs (SDPs) that allow us to simultaneously construct observer gains, along with DMD models which exhibit desired properties. Since DMD models for turbulent flows are typically high-dimensional, we provide a tractable algorithm for solving the high-dimensional SDP. We demonstrate the potential of our proposed approach on an industrial application using real-world data, and illustrate that the co-design significantly outperforms sequential design. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Co-design State estimation data-driven methods Linear matrix inequalities Partial differential equations Turbulent flows Model order reduction

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Learning data-driven Patient Risk Stratification Models for Clostridium difficile

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OPEN FORUM INFECTIOUS DISEASES 2014年第2期1卷 ofu045页

作者： Wiens, Jenna Campbell, Wayne N. Franklin, Ella S. Guttag, John V. Horvitz, Eric MIT Dept Elect Engn & Comp Sci Cambridge MA 02139 USA Union Mem Hosp Div Infect Dis MedStar Hlth Baltimore MD USA Natl Ctr Human Factors Healthcare MedStar Washington DC USA Microsoft Res Redmond WA USA

Background. Although many risk factors are well known, Clostridium difficile infection (CDI) continues to be a significant problem throughout the world. The purpose of this study was to develop and validate a data-driven, hospital-specific risk stratification procedure for estimating the probability that an inpatient will test positive for C difficile. methods. We consider electronic medical record (EMR) data from patients admitted for >= 24 hours to a large urban hospital in the U.S. between April 2011 and April 2013. Predictive models were constructed using L2-regularized logistic regression and data from the first year. The number of observational variables considered varied from a small set of well known risk factors readily available to a physician to over 10 000 variables automatically extracted from the EMR. Each model was evaluated on holdout admission data from the following year. A total of 34 846 admissions with 372 cases of CDI was used to train the model. Results. Applied to the separate validation set of 34 722 admissions with 355 cases of CDI, the model that made use of the additional EMR data yielded an area under the receiver operating characteristic curve (AUROC) of 0.81 (95% confidence interval [CI], .79-.83), and it significantly outperformed the model that considered only the small set of known clinical risk factors, AUROC of 0.71 (95% CI, .69-.75). Conclusions. Automated risk stratification of patients based on the contents of their EMRs can be used to accurately identify a high-risk population of patients. The proposed method holds promise for enabling the selective allocation of interventions aimed at reducing the rate of CDI.

关键词： Clostridium difficile data-driven methods electronic medical records machine learning risk stratification

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A review of automated and data-driven approaches for pathway determination and reaction monitoring in complex chemical systems

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DIGITAL CHEMICAL ENGINEERING 2022年 2卷

作者： Puliyanda, Anjana Srinivasan, Karthik Sivaramakrishnan, Kaushik Prasad, Vinay Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

In this work, we review the state of the art on approaches for the determination of reaction networks and the real-time monitoring of reactions in complex chemical systems consisting of multiple reactive components using automated and data-driven methods. This complexity of the system results in uncertainty about both the dominant species and reactions in the system. Automated approaches to reaction network or pathway determination include rule-based or algorithmically extracted methods, quantum mechanical simulations, and machine learning approaches. We also identify the effect of explicit pathway determination on the approach for reaction monitoring. Furthermore, we compare and contrast the automated and data-driven approaches for reaction pathway determination with some heuristics commonly used to develop reaction mechanisms in complex chemistries.

关键词： Reaction monitoring Reaction network data-driven methods Automated network generation

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Individual benefits and collective challenges: Experts' views on data-driven approaches in medical research and healthcare in the German context

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BIG data & SOCIETY 2022年第1期9卷

作者： Buhr, Lorina Schicktanz, Silke Univ Med Ctr Gottingen Dept Med Eth & Hist Med Gottingen Germany Univ Erfurt Fac Econ Law & Social Sci Erfurt Germany

Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities of digitalisation. The aim of the study was to explore expert opinions regarding current challenges and opportunities related to data-driven medicine and medical research and to provide a methodological framework for empirically grounded, continuous normative reflection. To this end, we developed a heuristic tool to map and structure empirical findings for normative analysis. Using this tool, our interview material points to a polarisation between individualistic and collectivistic orientated argumentations among experts. The study shows that a multilevel analysis is required to deal with complex normative implications of data-driven approaches in medical research and healthcare.

关键词： data-driven methods digital health medical research artificial intelligence mobile health devices empirical-normative analysis interview study experts Germany

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data-driven indicators for the detection and prediction of stuck-pipe events in oil&gas drilling operations

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UPSTREAM OIL AND GAS TECHNOLOGY 2021年 7卷

作者： Brankovic, Aida Matteucci, Matteo Restelli, Marcello Ferrarini, Luca Piroddi, Luigi Spelta, Andrea Zausa, Fabrizio Univ Queensland Brisbane Qld 4072 Australia Politecn Milan Dipartimento Elettron Informaz Bioingn Via Ponzio 34-5 Milan Italy ENI Drilling Complet Risk Anal Dept San Donato Milanese Italy

Stuck-pipe phenomena can have disastrous effects on drilling performance, with outcomes that can range from time delays to loss of expensive machinery. In this work, we develop three indicators based on mudlog data, which aim to detect three different physical phenomena associated with the insurgence of a sticking. In particular, two indices target respectively the detection of translational and rotational motion issues, while the third index concerns the wellbore pressure. A statistical model that relates these features to documented stuck-pipe events is then developed using advanced machine learning tools. The resulting model takes the form of a depth-based map of the risk of incurring into a stuck-pipe, updated in real-time. Preliminary experimental results on the available dataset indicate that the use of the proposed model and indicators can help mitigate the stuck-pipe issue.

关键词： Oil&Gas Drilling Stuck-pipe Detection Prediction data-driven methods Rare events

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A data-driven Approach to the Fragile Families Challenge: Prediction through Principal-components Analysis and Random Forests

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Socius 2019年 5卷

作者： Compton, Ryan University of California Santa Cruz CA United States

Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and predictive model optimization to address a sociological data challenge. The author’s group develops improved generalizable models to identify at-risk families. Principal-components analysis and decision tree modeling are used to predict six main dependent variables in the Fragile Families Challenge, successfully modeling one binary variable but no continuous dependent variables in the diagnostic data set. This indicates that some binary dependent variables are more predictable using a reduced set of uncorrelated independent variables, and continuous dependent variables demand more complexity. © SAGE Publications Inc.. All rights reserved.

关键词： data-driven methods diagnostics feature engineering Fragile Families Challenge machine learning principal-components analysis random forests

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