Q. M. Chen, A. Finzi, T. Mancini, I. Melatti, and E. Tronci. "MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks." Fundamenta Informaticae 174 (2020): 229–258. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1941.
Abstract: In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes . As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance ). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.
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S. Sinisi, V. Alimguzhin, T. Mancini, E. Tronci, F. Mari, and B. Leeners. "Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins." Fundamenta Informaticae 174 (2020): 283–310. IOS Press. ISSN: 1875-8681. DOI: 10.3233/FI-2020-1943.
Abstract: In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
Keywords: Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation
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Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software. Vol. abs/1210.2276. CoRR, Technical Report, 2012.
Abstract: Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software.
Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis.
In this paper, we present a map-reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis.
We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS.
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Y. Driouich, M. Parente, and E. Tronci. "Modeling cyber-physical systems for automatic verification." In 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017), 1–4., 2017. DOI: 10.1109/SMACD.2017.7981621.
Keywords: cyber-physical systems;formal verification;maximum power point trackers;power engineering computing;Modelica;automatic verification;complex power electronics systems;cyber-physical systems modeling;distributed maximum power point tracking system;open standard modeling language;Computational modeling;Control systems;Integrated circuit modeling;Mathematical model;Maximum power point trackers;Object oriented modeling;Radiation effects;Automatic Formal Verification;Cyber-Physical Systems;DMPPT;Modeling;Photovoltaic systems;Simulation;System Analysis and Design
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T. Mancini, F. Mari, I. Melatti, I. Salvo, E. Tronci, J. Gruber, B. Hayes, M. Prodanovic, and L. Elmegaard. "Parallel Statistical Model Checking for Safety Verification in Smart Grids." In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 1–6., 2018. DOI: 10.1109/SmartGridComm.2018.8587416.
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T. Mancini, F. Mari, A. Massini, I. Melatti, I. Salvo, S. Sinisi, E. Tronci, R. Ehrig, S. Röblitz, and B. Leeners. "Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/g864.
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T. Mancini, E. Tronci, A. Scialanca, F. Lanciotti, A. Finzi, R. Guarneri, and S. Di Pompeo. "Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/grw9.
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Marco Martinelli, Enrico Tronci, Giovanni Dipoppa, and Claudio Balducelli. "Electric Power System Anomaly Detection Using Neural Networks." In 8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES), edited by M. G. Negoita, R. J. Howlett and L. C. Jain, 1242–1248. Lecture Notes in Computer Science 3213. Wellington, New Zealand: Springer, 2004. ISSN: 3-540-23318-0. DOI: 10.1007/978-3-540-30132-5_168.
Abstract: The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend.
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F. Maggioli, T. Mancini, and E. Tronci. "SBML2Modelica: Integrating biochemical models within open-standard simulation ecosystems." Bioinformatics 36, no. 7 (2019): 2165–2172. ISSN: 1367-4803. DOI: 10.1093/bioinformatics/btz860.
Abstract: SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g., enable in-silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models.Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2 -compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate, and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialised SBML simulators.SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica
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S. Sinisi, V. Alimguzhin, T. Mancini, E. Tronci, and B. Leeners. "Complete populations of virtual patients for in silico clinical trials." Bioinformatics (2021): 1–8. ISSN: 1367-4803. DOI: 10.1093/bioinformatics/btaa1026.
Abstract: Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies, or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g., reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patient (VPs), i.e., populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours.We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalised Mean Absolute Error of 15%, 20%, and 35% (90% of the latter dataset is covered within 20% error).
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