Conformant Planning via Symbolic Model Checking

We tackle the problem of planning in nondeterministic domains, by presenting a new approach to conformant planning. Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal despite the nondeterminism of the domain. Our approach is based on the representation of the planning domain as a finite state automaton. We use Symbolic Model Checking techniques, in particular Binary Decision Diagrams, to compactly represent and efficiently search the automaton. In this paper we make the following contributions. First, we present a general planning algorithm for conformant planning, which applies to fully nondeterministic domains, with uncertainty in the initial condition and in action effects. The algorithm is based on a breadth-first, backward search, and returns conformant plans of minimal length, if a solution to the planning problem exists, otherwise it terminates concluding that the problem admits no conformant solution. Second, we provide a symbolic representation of the search space based on Binary Decision Diagrams (BDDs), which is the basis for search techniques derived from symbolic model checking. The symbolic representation makes it possible to analyze potentially large sets of states and transitions in a single computation step, thus providing for an efficient implementation. Third, we present CMBP (Conformant Model Based Planner), an efficient implementation of the data structures and algorithm described above, directly based on BDD manipulations, which allows for a compact representation of the search layers and an efficient implementation of the search steps. Finally, we present an experimental comparison of our approach with the state-of-the-art conformant planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems from the distribution packages of these systems, plus other problems defined to stress a number of specific factors. Our approach appears to be the most effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all the problems where a comparison is possible, CMBP outperforms its competitors, sometimes by orders of magnitude

Twin-waves propagation phenomena in magnetically-coupled structures

The use of magnetic dipoles embedding in an elastic support introduces long-range interaction forces. This is a completely new paradigm in structural mechanics, classically based on local short-range particle interaction. The features of long-range forces produce very new mechanical coupling effects. This paper examines the case in which two identical rod-like structures, each with a dipole distribution embedded, vibrate side by side. Waves generated in one of the rods propagate also in the second and vice versa creating a new effect we name twin-waves. The present investigation unveils the existence of an infinite number of propagation modes even in one-dimensional infinite structures, a new and unus al behaviour in classical waveguides. The physics behind this phenomenon is further investigated also by numerical simulations

Privacy-Preserving Deep Learning With Homomorphic Encryption: An Introduction

Privacy-preserving deep learning with homomorphic encryption (HE) is a novel and promising research area aimed at designing deep learning solutions that operate while guaranteeing the privacy of user data. Designing privacy-preserving deep learning solutions requires one to completely rethink and redesign deep learning models and algorithms to match the severe technological and algorithmic constraints of HE. This paper provides an introduction to this complex research area as well as a methodology for designing privacy-preserving convolutional neural networks (CNNs). This methodology was applied to the design of a privacy-preserving version of the well-known LeNet-1 CNN, which was successfully operated on two benchmark datasets for image classification. Furthermore, this paper details and comments on the research challenges and software resources available for privacy-preserving deep learning with HE

Frequency intermittency and energy pumping by linear attachments

Cataloged from PDF version of article.The present paper considers the problem of realizing an effective targeted energy pumping from a linear oscillator to a set of ungrounded linear resonators attached to it. Theoretical as well as numerical results demonstrate the efficacy of using a complex attachment as a passive absorber of broadband energy injected into the primary structure. The paper unveils also the existence of an instantaneous frequency associated with the master response characterized by intermittency: a rather surprising result for a linear autonomous system. Comparison with nonlinear energy sinks demonstrates that the two systems have some analogies in this respect and that the linear complex attachment is a very efficient energy trap. (C) 2014 Elsevier Ltd. All rights reserved

TinyML for UWB-radar based presence detection

Tiny Machine Learning (TinyML) is a novel research area aiming at designing machine and deep learning models and algorithms able to be executed on tiny devices such as Internet-of-Things units, edge devices or embedded systems. In this paper we introduce, for the first time in the literature, a TinyML solution for presence-detection based on UltrawideBand (UWB) radar, which is a particularly promising radar technology for pervasive systems. To achieve this goal we introduce a novel family of tiny convolutional neural networks for the processing of UWB-radar data characterized by a reduced memory footprint and computational demand so as to satisfy the severe technological constraints of tiny devices. From this technological perspective, UWB-radars are particularly relevant in the presence-detection scenario since they do not acquire sensitive information of users (e.g., images, videos or audio), hence preserving their privacy.The proposed solution has been successfully tested on a public-available benchmark for the indoor presence detection and on a real-world application of in-car presence detection

Experimenting sensors network for innovative optimal control of car suspensions

This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution

Damage diagnostic technique combining machine learning approach with a sensor swarm

A Model-free approach is particularly valuable for Structural Health Monitoring because real structures are often too complex to be modelled accurately, requiring anyhow a large quantity of sensor data to be processed. In this context, this paper presents a machine learning technique that analyses data acquired by swarm of a sensor. The proposed algorithm uses unsupervised learning and is based on the use principal component analysis and symbolic data analysis: PCA extracts features from the acquired data and use them as a template for clustering. The algorithm is tested with numerical experiments. A truss bridge is modelled by a finite element model, and structural response is produced in healthy and several damaged scenarios. The present research shows also the importance of considering a sufficient number of measurements points along the structure, i.e. the swarm of sensors. This technology, which nowadays is easily attainable with the application of optical Fiber Bragg Grating strain sensors. The difficulties related to the early stage damage detection in complex structures can be skipped, especially when ambient, narrow band, moving loads are considered, enhancing the prediction capabilities of the proposed algorithm

Safe and secure control of swarms of vehicles by small-world theory

The present paper investigates a new paradigm to control a swarm of moving individual vehicles, based on the introduction of a few random long-range communications in a queue dominated by short-range car-following dynamics. The theoretical approach adapts the small-world theory, originally proposed in social sciences, to the investigation of these networks. It is shown that the controlled system exhibits properties of higher synchronization and robustness with respect to communication failures. The considered application to a vehicle swarm shows how safety and security of the related traffic dynamics are strongly increased, diminishing the collision probability even in the presence of a hacker attack to some connectivity channels

Damping control of polodes, inertia and natural frequencies: Theory and application to automotive suspensions

This paper shows how tunable dampers can help control the instant centre of rotation of a 2D rigid body and its polode in planar motion, which in turn implies that the inertia tensor can also be controlled. For mechanisms equipped with some elasticity the results show that damping can also control their natural frequencies. The foundation of a general theory to control the polode is presented, exploring the chance of an optimal control formulation of the problem via a variational control principle, approached by the LQR (Linear Quadratic Regulator) method, after a suitable linearization. Application to automotive suspension linkages is presented that demonstrates the control of the instant roll centre and axis and consequently its instant roll vibration frequency to optimize the response, when excited by lateral inertia forces

OPTYRE—Real time estimation of rolling resistance for intelligent tyres

The study of the rolling tyre is a problem framed in the general context of nonlinear elasticity. The dynamics of the related phenomena is still an open topic, even though few examples and models of tyres can be found in the technical literature. The interest in the dissipation effects associated with the rolling motion is justified by their importance in fuel-saving and in the context of an eco-friendly design. However, a general lack of knowledge characterizes the phenomenon, since not even direct experience on the rolling tyre can reveal the insights of the correlated different dissipation effects, as the friction between the rubber and the road, the contact kinematics and dynamics, the tyre hysteretic behaviour and the grip. A new technology, based on fibre Bragg grating strain sensors and conceived within the OPTYRE project, is illustrated for the specific investigation of the tyre dissipation related phenomena. The remarkable power of this wireless optical system stands in the chance of directly accessing the behaviour of the inner tyre in terms of stresses when a real-condition-rolling is experimentally observed. The ad hoc developed tyre model has allowed the identification of the instant grip conditions, of the area of the contact patch and allows the estimation of the instant dissipated power, which is the focus of this paper