Verifying collision avoidance behaviours for unmanned surface vehicles using probabilistic model checking

Collision avoidance is an essential safety requirement for unmanned surface vehicles (USVs). Normally, its practical verification is non-trivial, due to the stochastic behaviours of both the USVs and the intruders. This paper presents the probabilistic timed automata (PTAs) based formalism for three collision avoidance behaviours of USVs in uncertain dynamic environments, which are associated with the crossing situation in COLREGs. Steering right, acceleration, and deceleration are considered potential evasive manoeuvres. The state-of-the-art prism model checker is applied to analyse the underlying models. This work provides a framework and practical application of the probabilistic model checking for decision making in collision avoidance for USVs

A Non-heuristic Approach to Time-space Tradeoffs and Optimizations for BKW

Blum, Kalai and Wasserman (JACM 2003) gave the first sub-exponential algorithm to solve the Learning Parity with Noise (LPN) problem. In particular, consider the LPN problem with constant noise $\mu=(1-\gamma)/2$. The BKW solves it with space complexity $2^{\frac{(1+\epsilon)n}{\log n}}$ and time/sample complexity $2^{\frac{(1+\epsilon)n}{\log n}}\cdot 2^{O(n^{\frac{1}{1+\epsilon}})}$ for small constant $\epsilon\to 0^+$. We propose a variant of the BKW by tweaking Wagner\u27s generalized birthday problem (Crypto 2002) and adapting the technique to a $c$-ary tree structure. In summary, our algorithm achieves the following: (Time-space tradeoff). We obtain the same time-space tradeoffs for LPN and LWE as those given by Esser et al. (Crypto 2018), but without resorting to any heuristics. For any $2\leq c\in\mathbb{N}$, our algorithm solves the LPN problem with time/sample complexity $2^{\frac{\log c(1+\epsilon)n}{\log n}}\cdot 2^{O(n^{\frac{1}{1+\epsilon}})}$ and space complexity $2^{\frac{\log c(1+\epsilon)n}{(c-1)\log n}}$, where one can use Grover\u27s quantum algorithm or Dinur et al.\u27s dissection technique (Crypto 2012) to further accelerate/optimize the time complexity. (Time/sample optimization). A further adjusted variant of our algorithm solves the LPN problem with sample, time and space complexities all kept at $2^{\frac{(1+\epsilon)n}{\log n}}$ for $\epsilon\to 0^+$, saving factor $2^{\Omega(n^{\frac{1}{1+\epsilon}})}$ in time/sample compared to the original BKW, and the variant of Devadas et al. (TCC 2017). This benefits from a careful analysis of the error distribution among the correlated candidates, and therefore avoids repeating the same process $2^{\Omega(n^{\frac{1}{1+\epsilon}})}$ times on fresh new samples. (Sample reduction) Our algorithm provides an alternative to Lyubashevsky\u27s BKW variant (RANDOM 2005) for LPN with a restricted amount of samples. In particular, given $Q=n^{1+\epsilon}$ (resp., $Q=2^{n^{\epsilon}}$) samples, our algorithm saves a factor of $2^{\Omega(n)/(\log n)^{1-\kappa}}$ (resp., $2^{\Omega(n^{\kappa})}$) for constant $\kappa \to 1^-$ in running time while consuming roughly the same space, compared with Lyubashevsky\u27s algorithm. We seek to bridge the gaps between theoretical and heuristic LPN solvers, but take a different approach from Devadas et al. (TCC 2017). We exploit weak yet sufficient conditions (e.g., pairwise independence), and the analysis uses only elementary tools (e.g., Chebyshev\u27s inequality)

Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics

Stochastic-gradient sampling methods are often used to perform Bayesian inference on neural networks. It has been observed that the methods in which notions of differential geometry are included tend to have better performances, with the Riemannian metric improving posterior exploration by accounting for the local curvature. However, the existing methods often resort to simple diagonal metrics to remain computationally efficient. This loses some of the gains. We propose two non-diagonal metrics that can be used in stochastic-gradient samplers to improve convergence and exploration but have only a minor computational overhead over diagonal metrics. We show that for fully connected neural networks (NNs) with sparsity-inducing priors and convolutional NNs with correlated priors, using these metrics can provide improvements. For some other choices the posterior is sufficiently easy also for the simpler metrics

Efficient path planning algorithms for Unmanned Surface Vehicle

The C-Enduro Unmanned Surface Vehicle (USV) is designed to operate at sea for extended periods of time (up to 3 months). To increase the endurance capability of the USV, an energy efficient path planning algorithm is developed. The proposed path planning algorithm integrates the Voronoi diagram, Visibility algorithm, Dijkstra search algorithm and takes also into account the sea current data. Ten USV simulated mission scenarios at different time of day and start/end points were analysed. The proposed approach shows that the amount of energy saved can be up to 21%. Moreover, the proposed algorithm can be used to calculate a collision free and energy efficient path to keep the USV safe and improve the USV capability. The safety distance between the USV and the coastline can also be configured by the user

Distributed Learning over Unreliable Networks

Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: {\em Can we design machine learning systems that are tolerant to network unreliability during training?} With this motivation, we focus on a theoretical problem of independent interest---given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability $p$ of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of \textcolor{black}{parameter servers}. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable

Elaboration de composites à matrice métallique d'alliages d'aluminium par projection à froid

Le procédé de projection à froid (cold spray en anglais) est un procédé fondé sur l accélération de particules qui restent à l état solide pour former des dépôts. L un des forts potentiels applicatifs de ce procédé réside dans la réalisation de dépôts composites car l'incorporation des particules céramiques dans des poudres métalliques influence la microstructure et les propriétés des dépôts. Néanmoins, le principe de construction du dépôt composite n est pas encore parfaitement établi. En conséquence, les recherches menées dans cette étude sur la fabrication de dépôts composites s articulent autour de plusieurs domaines, à savoir : La science des matériaux avec des études sur l effet de la taille et de la teneur (15 vol.% - 60 vol.%) de la particule du renfort (SiC); La mécanique des fluides avec des modélisations des vitesses des particules céramiques (SiC) et alliage d aluminium (Al5056) et les simulations du comportement à la déformation de la particule; Les caractérisations des dépôts avec des analyses de microstructure et de microdureté, de la cohésion du dépôt et de comportement en frottement des dépôts;Les résultats montrent que la température du gaz n'a aucun effet sur la teneur en SiC dans les dépôts mais provoque une amélioration du rendement de dépôt. La teneur en SiC dans les dépôts composites d Al5056/SiCp augmente avec l augmentation de la teneur en SiC dans les poudres initiales. L ajout de SiC dans les dépôts d Al5056 augmente la dureté et améliore la résistance à l'usure des dépôts, et puis l amélioration dépend de la teneur en SiC dans les dépôts composites. La force de cohésion des dépôts augmente dans un premier temps avec l augmentation de la teneur en SiC puis diminue à partir d environ 26-27%. Les dépôts composites renforcés par SiC-67 et SiC-27 ont une teneur en SiC semblable dans les dépôts ; Pourtant la microdureté, la force de cohésion et la résistance à l'usure des dépôts formés par Al5056/SiC-67 sont supérieures à celles des dépôts construits par Al5056/SiC-27. Ce phénomène relève l importance de l énergie cinétique des particules renforts.Les résultats expérimentaux ont montré que les particules de SiC ne se déforment pas plastiquement mais qu elles sont susceptibles de créer des cratères sur le substrat ou le revêtement déjà formé ou encore rebondir ou bien de s insérer mécaniquement dans le revêtement déposé. Finalement, un modèle eulérien a été développé pour prédire la vitesse critique à partir de la morphologie de l éjection de matière au moment de l impact. Ce modèle a également été étendu au dépôt composite pour représenter le procédé d empilement des particules pendant la projection. Les résultats calculés montrent la plus grande déformation des particules de la matrice grâce à l impact des renforts.In cold spraying, particles are accelerated in the gas jet to achieve a high velocity and deposit on the substrate with a solid state. One of potential and important applications of cold spray is realizing the composite coatings. The incorporated ceramic particles in the composite coating can greatly influence the microstructure and properties of the coatings. The objective of this thesis was to investigate factors influencing the reinforcement content in the coatings and especially the formation mechanism in cold spraying. Al5056/SiC composite coatings were prepared by cold spraying. The effect of particle size and the reinforcement content in the powders on the reinforcement content in the coatings and thus on the microstructure and the properties of the coatings were studied. A search on the particle deformation and the formation mechanism of the composite coating was also carried out by using software of fluent and Abaqus.The results show that the addition of the SiC particles in the coating increases the hardness and improves the wear resistance of the coatings. However, the cohesion strength of the coatings first increases with the increase of the SiC content in the coating and then at a certain fraction, it decreases. Moreover, under the condition of having a similar SiC content in the coating, larger SiC particles lead to better properties of the coatings.Finally, an eulerian model was used for predicting the critical velocity by the morphology of the material jet. This model has also been extended to the composite model to demonstrate the built-up process of the composite coating during cold spraying. The calculation results show that the matrix particles deform more greatly after being impacted by the reinforcements.BELFORT-UTBM-SEVENANS (900942101) / SudocSudocFranceF

Practical and Secure Outsourcing Algorithms of Matrix Operations Based on a Novel Matrix Encryption Method

With the recent growth and commercialization of cloud computing, outsourcing computation has become one of the most important cloud services, which allows the resource-constrained clients to efficiently perform large-scale computation in a pay-per-use manner. Meanwhile, outsourcing large scale computing problems and computationally intensive applications to the cloud has become prevalent in the science and engineering computing community. As important fundamental operations, large-scale matrix multiplication computation (MMC), matrix inversion computation (MIC), and matrix determinant computation (MDC) have been frequently used. In this paper, we present three new algorithms to enable secure, verifiable, and efficient outsourcing of MMC, MIC, and MDC operations to a cloud that may be potentially malicious. The main idea behind our algorithms is a novel matrix encryption/decryption method utilizing consecutive and sparse unimodular matrix transformations. Compared to previous works, this versatile technique can be applied to many matrix operations while achieving a good balance between security and efficiency. First, the proposed algorithms provide robust confidentiality by concealing the local information of the entries in the input matrices. Besides, they also protect the statistic information of the original matrix. Moreover, these algorithms are highly efficient. Our theoretical analysis indicates that the proposed algorithms reduce the time overhead on the client side from O(n 2.3728639 ) to O(n 2 ). Finally, the extensive experimental evaluations demonstrate the practical efficiency and effectiveness of our algorithms