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Charme |
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Conference Article |
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2003 |
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Lecture Notes in Computer Science |
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2860 |
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Springer |
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Geist, D.; Tronci, E. |
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3-540-20363-X |
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yes |
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Call Number |
Sapienza @ mari @ editor-charme03 |
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37 |
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Author |
Pappagallo, A.; Massini, A.; Tronci, E. |
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Title |
Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review |
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Journal Article |
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2020 |
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Information |
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11 |
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558 |
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no |
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Call Number |
MCLab @ davi @ |
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181 |
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Mancini, T.; Mari, F.; Massini, A.; Melatti, I.; Salvo, I.; Sinisi, S.; Tronci, E.; Ehrig, R.; Röblitz, S.; Leeners, B. |
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Title |
Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction |
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Conference Article |
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2018 |
Publication |
25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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no |
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Call Number |
MCLab @ davi @ |
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175 |
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Mancini, T.; Tronci, E.; Scialanca, A.; Lanciotti, F.; Finzi, A.; Guarneri, R.; Di Pompeo, S. |
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Title |
Optimal Fault-Tolerant Placement of Relay Nodes in a Mission Critical Wireless Network |
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Conference Article |
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2018 |
Publication |
25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018) |
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no |
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MCLab @ davi @ |
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174 |
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Author |
Alimguzhin, Vadim; Mari, Federico; Melatti, Igor; Salvo, Ivano; Tronci, Enrico |
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Title |
A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software |
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Report |
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2012 |
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abs/1210.2276 |
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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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CoRR, Technical Report |
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yes |
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Call Number |
Sapienza @ mari @ |
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101 |
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Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Tronci, E.; Gruber, J.; Hayes, B.; Prodanovic, M.; Elmegaard, L. |
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Title |
Parallel Statistical Model Checking for Safety Verification in Smart Grids |
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Conference Article |
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Year |
2018 |
Publication |
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
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1-6 |
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MCLab @ davi @ mancini-etal:2018:smartgridcomm |
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170 |
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Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Leeners, B. |
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Title |
Complete populations of virtual patients for in silico clinical trials |
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Journal Article |
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Year |
2021 |
Publication |
Bioinformatics |
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1-8 |
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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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1367-4803 |
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MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 |
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182 |
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Melatti, I.; Mari, F.; Mancini, T.; Prodanovic, M.; Tronci, E. |
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Title |
A Two-Layer Near-Optimal Strategy for Substation Constraint Management via Home Batteries |
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Journal Article |
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2021 |
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IEEE Transactions on Industrial Electronics |
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1-1 |
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Within electrical distribution networks, substation constraints management requires that aggregated power demand from residential users is kept within suitable bounds. Efficiency of substation constraints management can be measured as the reduction of constraints violations w.r.t. unmanaged demand. Home batteries hold the promise of enabling efficient and user-oblivious substation constraints management. Centralized control of home batteries would achieve optimal efficiency. However, it is hardly acceptable by users, since service providers (e.g., utilities or aggregators) would directly control batteries at user premises. Unfortunately, devising efficient hierarchical control strategies, thus overcoming the above problem, is far from easy. We present a novel two-layer control strategy for home batteries that avoids direct control of home devices by the service provider and at the same time yields near-optimal substation constraints management efficiency. Our simulation results on field data from 62 households in Denmark show that the substation constraints management efficiency achieved with our approach is at least 82% of the one obtained with a theoretical optimal centralized strategy. |
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To appear |
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no |
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Call Number |
MCLab @ davi @ ref9513535 |
Serial |
190 |
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Author |
Mancini, T.; Melatti, I.; Tronci, E. |
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Title |
Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
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2021 |
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IEEE Transactions on Software Engineering |
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1-1 |
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Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking. |
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1939-3520 |
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To appear |
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MCLab @ davi @ ref9527998 |
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191 |
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Author |
Cesta, Amedeo; Fratini, Simone; Orlandini, Andrea; Finzi, Alberto; Tronci, Enrico |
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Title |
Flexible Plan Verification: Feasibility Results |
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Journal Article |
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2011 |
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Fundamenta Informaticae |
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107 |
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2 |
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111-137 |
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yes |
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Sapienza @ mari @ fi11 |
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15 |
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