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Detection of Glucose Sensor Faults in an Artificial Pancreas via Whiteness Test on Kalman Filter Residuals

IFAC-PapersOnLine 2021年第7期54卷 274-279页

作者： Eleonora Manzoni Mirco Rampazzo Simone Del Favero Department of Information Engineering University of Padova Via G. Gradenigo 6/B 35131 Padova Italy

Continuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans.

关键词： Fault detection diagnosis Biomedical system modeling simulation visualization Artificial pancreas or organs Statistical methods signal analysis for FDI real-time algorithms scheduling programming

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Technical Devices Degradation Self-Analysis for Self-Maintenance Strategy: Crane Case Study

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IFAC-PapersOnLine 2021年第1期54卷 729-736页

作者： Janusz Szpytko Yorlandys Salgado Duarte AGH University of Science and Technology Ave A. Mickiewicza 30 PL 30-059 Krakow Poland

Self-learning from historical data and using the resulting information to improve the future self-decision-making focused on technical objects, results machine learning approach, is a major goal in today’s devices exploitation process, including also self-maintenance. Simulation-based risk-oriented approach into the broad machine learning machinery usually fits independent theorical distributions to historical degradation data of the device in order to forecast risk indicators and evaluate in the final stage the maintenance process standards. The study case of the paper are critical overhead cranes operating with hazardous and continuous operation conditions. The subject of the paper is a technical devices degradation self-analysis for self-maintenance strategy discussed on crane case study, also including device representative data sensitivity analysis. The proposed subject directly impacts the device risk control management and maintenance scheduling processes, and the analysis proves the robustness of the intelligent self-analysis system by introducing additional information for future decision making. The devices degradation representative data analysis impact on the preferred exploitation focused decisions have been discussed. The device features have been presented via stochastic non-linear optimization model with bounded constraint that assess a risk global-system indicator based on Monte Carlo simulations.

关键词： real-time algorithms scheduling programming Intelligent maintenance systems Cyber-Physical Systems Robustness analysis Simulation of stochastic systems Modeling for control optimization

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A control-inspired iterative algorithm for memory management in NUMA multicores

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IFAC Proceedings Volumes 2014年第3期47卷 6117-6122页

作者： Marcello Farina Davide Zoni William Fornaciari DEIB Politecnico di Milano Milano ITALY

The multiprocessor revolution allows to have different processors accessing their local memory controller and the relative RAM portion, while the possibility for each processor to access the remote memory on another processor enables the so-called Non-Uniform-Memory-Access (NUMA) multiprocessor paradigm. This paper discusses a control-inspired iterative algorithm to allocate the memory requests by different applications in a NUMA systems, trying to maximize the RAM utilization under the locality requirement. The proposed method is tested in two case studies.

关键词： real-time algorithms memory allocation multicore systems

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real-time capable software agents on IEC 61131 systems – Developing a tool supported method

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IFAC Proceedings Volumes 2011年第1期44卷 9164-9169页

作者： Ursula Frank Josef Papenfort Daniel Schütz Beckhoff Automation GmbH Verl Germany Automation and Information Systems Technische Universität München München Germany

Abstract In this paper an approach that utilizes the paradigm of agent-orientation for the software development of automation systems, is presented. We discuss different methods for agent-oriented software development in general and motivate a novel approach that meets the special requirements for control software in production automation. We show how this novel approach can enhance the dependability of control software by integrating concepts for diagnosis and failure compensation in real-time. Furthermore, we present a modelling approach, that will be integrated into a market leading IEC 61131 software development tool and by that, allow application engineers to implement agent control software.

关键词： real-time algorithms Programmable logic controllers Knowledge-based control

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A Tractable Class of algorithms for Reliable Routing in Stochastic Networks

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Procedia - Social and Behavioral Sciences 2011年 17卷 341-363页

作者： S. Samaranayake S. Blandin A. Bayen PhD student Systems Engineering University of California Berkeley 621 Sutardja Dai Hall Berkeley CA 94720-1764 USA Associate Professor Systems Engineering University of California Berkeley 642 Sutardja Dai Hall Berkeley CA 94720-1764 USA

The goal of this article is to provide the theoretical basis for enabling tractable solutions to the “arriving on time” problem and enabling its use in real-time mobile phone applications. Optimal routing in transportation networks with highly varying traffic conditions is a challenging problem due to the stochastic nature of travel-times on links of the network. The definition of optimality criteria and the design of solution methods must account for the random nature of the travel-time on each link. Most common routing algorithms consider the expected value of link travel-time as a sufficient statistic for the problem and produce least expected travel-time paths without consideration of travel-time variability. However, in numerous practical settings the reliability of the route is also an important decision factor. In this article, we consider the following optimality criterion: maximizing the probability of arriving on time at a destination given a departure time and a time budget . We present an efficient algorithm for finding an optimal routing policy with a well bounded computational complexity, improving on an existing solution that takes an unbounded number of iterations to converge to the optimal solution. A routing policy is an adaptive algorithm that determines the optimal solution based on en route travel-times and therefore provides better reliability guarantees than an a-priori solution. Novel speed-up techniques to efficiently compute the adaptive optimal strategy and methods to prune the search space of the problem are also investigated. Finally, an extension of this algorithm which allows for both timevarying traffic conditions and spatio-temporal correlations of link travel-time distributions is presented. The dramatic runtime improvements provided by the algorithm are demonstrated for practical scenarios in California.

关键词： stochastic routing adaptive routing dynamic programming traffic information systems real-time algorithms

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Model Predictive Control Prototyping and Validation for a Large Central Cooling System

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IFAC-PapersOnLine 2022年第16期55卷 338-343页

作者： Rahmat Heidari Julio H. Braslavsky Commonwealth Scientific and Industrial Research Organisation CSIRO Energy Centre Newcastle Australia The University of Newcastle Australia

In this paper we present an application of Model Predictive Control (MPC) to a commercial building cooling system. The MPC models are a combination of white, grey and black box models derived from the experimental dataset and ensure energy and mass conservation is the system. Individual equipment models are validated against the actual measurements to ensure they accurately represent the physical system. A comparison of MPC performance to that of a business-as-usual operation scenario indicates up to 25% savings in operating costs with MPC.

关键词： Modelling for control optimisation Industrial applications of optimal control Building automation real-time algorithms scheduling programming Grey box modelling

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Approximate Collaborative Fleet Routing with a Pointer Generation Neural Network Approach ⁎

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IFAC-PapersOnLine 2021年第2期54卷 195-202页

作者： Sascha Hamzehi Philipp Franeck Bernd Kaltenhäuser Klaus Bogenberger Chair of Traffic Engineering and Control TUM Department of Civil Geo and Environmental Engineering Technical University Munich BY 80333 Germany BMW Group Munich BY 80333 Germany Department of Technical Management Baden-Württemberg Cooperative State University Villingen-Schwenningen BW 78054 Germany

With rapid emergence of high-performance computing platforms, the availability of client big data and new machine learning techniques, the application domain of platform-based mobility services supports the research of new optimization techniques for discrete combinatorial optimization problems. Within this research field, particularly large-scale transportation domain specific problems, e.g. multi-vehicle-request matching and collaborative vehicle fleet routing problems are of high interest. In this contribution we present our novel combinatorial Deep Reinforcement Learning for solving symmetric and asymmetric multi-vehicle-request weighted assignment or matching problems by learning and predicting an efficient solution heuristic automatically. The solved assignment problem is characterized by defining different node classes for vehicles, requests and service stations. Our results contain algorithm benchmarks based on reproducible artificial data and statistical evaluations towards solution accuracy and efficiency with respect to different problem complexities. We contribute additional comparisons between different algorithms and heuristics such as naive Greedy, k-Regret and exact (globally optimal) solutions with the simplex method by the Mixed-Integer Programming (MIP) solver Cplex. Further, we compare the results with our model with respect to solution accuracy and efficiency We conclude that our proposed model solves the presented problem setting globally optimal up to an upper graph complexity bound defined via node degree. Furthermore, our results show that our proposed method outperforms all other algorithms with respect to the solution time required.

关键词： Adaptive control by neural networks Stochastic control game theory Coordination of multiple vehicle systems Large scale optimization problems real-time algorithms scheduling programming Reinforcement learning control Job activity scheduling Efficient strategies for large scale complex systems Cooperative navigation Mission planning decision making Multi-vehicle systems

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Schedulability Analysis of Hierarchical Systems with Arbitrary Scheduling in the Global Level

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IFAC-PapersOnLine 2015年第10期48卷 264-269页

作者： Guasque Ana Balbastre Patrieia Brocal Vicent Crespo Alfons Institute of Control Systems and Industrial Computing (ai2) Valencia Spain Fent Innovative Software Solutions (FentISS)

Schedulability analysis of hierarchical real-time systems is based in the previous knowledge of the scheduling algorithms both in the local and the global levels. In a partitioned system with safety and security issues and certification assurance levels, the global scheduling is usually generated using a static table. Therefore, each partition must allocate task jobs only in the temporal windows reserved for that partition. Even if the static table can come originally from a periodic server or other scheduling policy, the final plan can suffer from modifications due to changes in the system requirements. As a consequence, the CPU assignment to a partition does not have to correspond to any known policy. In this case, it is not possible to use existing scheduling analysis for hierarchical systems. This paper provides a schedulability analysis when the global level policy is not known but provided as a set of arbitrary time windows. The paper also provides a method to determine the more restrictive CPU assignment for a task set, as a mean of stablishing minimum temporal requirements for the partition.

关键词： Partitioned embedded systems real-time algorithms scheduling schedulability temporal predictability time analysis

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Fast Allan Variance (FAVAR) and Dynamic Fast Allan Variance (D-FAVAR) algorithms for both Regularly and Irregularly Sampled Data ⁎

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IFAC-PapersOnLine 2021年第20期54卷 26-31页

作者： Satya Prasad Maddipatla Hossein Haeri Kshitij Jerath Sean Brennan The Pennsylvania State University University Park PA 16802 USA University of Massachusetts Lowell Lowell MA 01854 USA

This work develops algorithms demonstrating fast implementations of Allan variance (AVAR) for regularly and irregularly sampled signals. AVAR is a technique first developed to study the frequency stability of atomic clocks. Typical AVAR algorithms calculate changes in means between differently-sized groupings of data and thus are useful in many data aggregation processes: to select the appropriate window length or timescales for estimating a signal’s moving average, to find the minimum variance of a signal, or to estimate the change in variance of a signal with complex noise contributions as a function of the number of collected data points. Unfortunately, AVAR typically involves very large signal lengths, yet the typical time required to compute AVAR increases quickly with the length of the time-series data. This paper presents a recursive algorithm inspired by the Fast Fourier Transform (FFT), specifically data organization into power-of-two groupings. This enables a fast AVAR implementation, called FAVAR, shown first for regularly sampled data. The results show a computational speed increase of three orders of magnitude versus typical AVAR calculations for data lengths often used with AVAR. Next, the FAVAR algorithm is extended to compute AVAR of irregularly sampled data by modeling these data as weighted but regularly sampled data clusters. Finally, this work analyzes Dynamic Allan variance implementations of FAVAR, called D-FAVAR, wherein AVAR is calculated at every timestep to capture window-varying statistical properties of the data stream. The recursion methods used in FAVAR, when extended to compute D-FAVAR, further increase computational speed by an additional factor of ten compared to computing the FAVAR at every timestep. They result in approximately four orders of magnitude speed improvements versus repeated calculation of AVAR with typical methods. These fast algorithms are demonstrated on signals that illustrate classical Allan variance curves, and th

关键词： Allan variance Dynamic Allan variance real-time algorithms

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