Credible autocoding of convex optimization algorithms

The efficiency of modern optimization methods, coupled with increasing computational resources, has led to the possibility of real-time optimization algorithms acting in safety-critical roles. There is a considerable body of mathematical proofs on on-line optimization algorithms which can be leveraged to assist in the development and verification of their implementation. In this paper, we demonstrate how theoretical proofs of real-time optimization algorithms can be used to describe functional properties at the level of the code, thereby making it accessible for the formal methods community. The running example used in this paper is a generic semi-definite programming solver. Semi-definite programs can encode a wide variety of optimization problems and can be solved in polynomial time at a given accuracy. We describe a top-down approach that transforms a high-level analysis of the algorithm into useful code annotations. We formulate some general remarks on how such a task can be incorporated into a convex programming autocoder. We then take a first step towards the automatic verification of the optimization program by identifying key issues to be addressed in future work.     

Trajectory planning/re-planning for satellite systems in rendezvous mission in the presence of actuator faults based on attainable efforts analysis

The objective of fault-tolerant control (FTC) is to minimise the effect of faults on system performance (stability, trajectory tracking, etc.). However, the majority of the existing FTC methods continue to force the system to follow the pre-fault trajectories without considering the reduction in available control resources caused by actuator faults. Forcing the system to follow the same trajectories as before fault occurrence may result in actuator saturation and system's instability. Thus, pre-fault objectives should be redefined in function of the remaining resources to avoid potential saturation. The main contribution of this paper is a flatness-based trajectory planning/re-planning method that can be combined with any active FTC approach. The work considers the case of over-actuated systems where a new idea is proposed to evaluate the severity of faults occurred. In addition, the trajectory planning/re-planning approach is formulated as an optimisation problem based on the analysis of attainable efforts domain in fault-free and fault cases. The proposed approach is applied to two satellite systems in rendezvous mission.     

Cover Image,Volume 66, Issue 10

The cover image, by Bruna A. Bregadiolli et al., is based on the Research Article Towards the synthesis of poly(azafulleroid)s: main chain fullerene oligomers for organic photovoltaic devices, DOI: 10.1002/pi.5419 .

     

Packing spherical discrete elements for large scale simulations

We introduce a new geometric method to generate sphere packings with restricted overlap values. Sample generation is an important, but time-consuming, step that precedes a calculation performed with the discrete element method (DEM). At present, there does not exist any software dedicated to DEM which would be similar to the mesh software that exists for finite element methods (FEM). A practical objective of the method is to build very large sphere packings (several hundreds of thousands) in a few minutes instead of several days as the current dynamic methods do. The developed algorithm uses a new geometric procedure to position very efficiently the polydisperse spheres in a tetrahedral mesh. The algorithm, implemented into YADE-OPEN DEM (open-source software), consists in filling tetrahedral meshes with spheres. In addition to the features of the tetrahedral mesh, the input parameters are the minimum and maximum radii (or their size ratio), and the magnitude of authorized overlaps. The filling procedure is stopped when a target solid fraction or number of spheres is reached. Based on this method, an efficient tool can be designed for DEMs used by researchers and engineers. The generated packings can be isotropic and the number of contacts per sphere is very high due to its geometric procedure. In this paper, different properties of the generated packings are characterized and examples from real industrial problems are presented to show how this method can be used. The current C++ version of this packing algorithm is part of YADE-OPEN DEM [20] available on the web (https://yade-dem.org).     

Design of a General‐Purpose European Compound Screening Library for EU‐OPENSCREEN

This work describes a collaborative effort to define and apply a protocol for the rational selection of a general‐purpose screening library, to be used by the screening platforms affiliated with the EU‐OPENSCREEN initiative. It is designed as a standard source of compounds for primary screening against novel biological targets, at the request of research partners. Given the general nature of the potential applications of this compound collection, the focus of the selection strategy lies on ensuring chemical stability, absence of reactive compounds, screening‐compliant physicochemical properties, loose compliance to drug‐likeness criteria (as drug design is a major, but not exclusive application), and maximal diversity/coverage of chemical space, aimed at providing hits for a wide spectrum of drugable targets. Finally, practical availability/cost issues cannot be avoided. The main goal of this publication is to inform potential future users of this library about its conception, sources, and characteristics. The outline of the selection procedure, notably of the filtering rules designed by a large committee of European medicinal chemists and chemoinformaticians, may be of general methodological interest for the screening/medicinal chemistry community. The selection task of 200K molecules out of a pre‐filtered set of 1.4M candidates was shared by five independent European research groups, each picking a subset of 40K compounds according to their own in‐house methodology and expertise. An in‐depth analysis of chemical space coverage of the library serves not only to characterize the collection, but also to compare the various chemoinformatics‐driven selection procedures of maximal diversity sets. Compound selections contributed by various participating groups were mapped onto general‐purpose self‐organizing maps (SOMs) built on the basis of marketed drugs and bioactive reference molecules. In this way, the occupancy of chemical space by the EU‐OPENSCREEN library could be directly compared with distributions of known bioactives of various classes. This mapping highlights the relevance of the selection and shows how the consensus reached by merging the five different 40K selections contributes to achieve this relevance. The approach also allows one to readily identify subsets of target‐ or target‐class‐oriented compounds from the EU‐OPENSCREEN library to suit the needs of the diverse range of potential users. The final EU‐OPENSCREEN library, assembled by merging five independent selections of 40K compounds from various expert groups, represents an excellent example of a Europe‐wide collaborative effort toward the common objective of building best‐in‐class European open screening platforms.     

A fuzzy constraint-based approach to data reconciliation in material flow analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.     

Recasting MLF

The language ML^F is a proposal for a new type system that supersedes both ML and System F, allows for efficient, predictable, and complete type inference for partially annotated terms. In this work, we revisit the definition of ML^F, following a more progressive approach and focusing on the design-space and expressiveness. We introduce a Curry-style version iML^F of ML^F and provide an interpretation of iML^F types as instantiation-closed sets of Dash System-F types, from which we derive the definition of type-instance in iML^F. We give equivalent syntactic definition of the type-instance, presented as a set of inference rules. We also show an encoding of iML^F into the closure of Curry-style System F by let-expansion. We derive the Church-style version eML^F by refining types of iML^F so as to distinguish between given and inferred polymorphism. We show an embedding of ML in eML^F and a straightforward encoding of System F into eML^F.     

Automatic fog detection and estimation of visibility distance through use of an onboard camera

In this paper, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and patented. The approach consists of dynamically implementing Koschmieder's law. Our method enables computing the meteorological visibility distance, a measure defined by the International Commission on Illumination (CIE) as the distance beyond which a black object of an appropriate dimension is perceived with a contrast of less than 5%. Our proposed solution is an original one, featuring the advantage of utilizing a single camera and necessitating the presence of just the road and sky in the scene. As opposed to other methods that require the explicit extraction of the road, this method offers fewer constraints by virtue of being applicable with no more than the extraction of a homogeneous surface containing a portion of the road and sky within the image. This image preprocessing also serves to identify the level of compatibility of the processed image with the set of Koschmieder's model hypotheses. Nicolas Hautiére graduated from the École Nationale des Travaux Publics de l'État, France (2002). He received his M.S. and Ph.D. degree in computer vision, respectively, in 2002 and 2005 from Saint-Étienne University (France). From 2002, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France. His research interests include trafic engineering, computer vision, and pattern recognition. Jean-Philippe Tarel graduated from the École Nationale des Ponts et Chaussées, Paris, France (1991). He received his Ph.D. degree in Applied Mathematics from Paris IX-Dauphine University in 1996 and he was with the Institut National de Recherche en Informatique et Automatique (INRIA) from 1991 to 1996. From 1997 to 1998, he was a research associate at Brown University, USA. From 1999, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France, and from 2001 to 2003 in the INRIA. His research interests include computer vision, pattern recognition, and shape modeling. Jean Lavenant graduated from the École Nationale des Travaux Publics de l'État, Lyon, France (2001). He received the M.S. degree in Computer Vision from Jean Monnet university of Saint-Étienne in 2001. In 2001, he was a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC). In 2002, he was a system engineer in Chicago (USA). He is currently an engineer for the french ministry of transports. Didier Aubert received the M.S. and Ph.D. degree, respectively, in 1985 and 1989 from the National Polytechnical Institut of Grenoble (INPG). From 1989--1990, he worked as a research scientist on the development of an automatic road following system for the NAVLAB at Carnegie Mellon University. From 1990–1994, he worked in the research department of a private company (ITMI). During this period he was the project leader of several projects dealing with computer vision. He is currently a researcher at INRETS since 1995 and works on Road traffic measurements, crowd monitoring, automated highway systems, and driving assistance systems for vehicles. He is an image processing expert for several companies, teaches at Universities (Paris VI, Paris XI, ENPC, ENST) and is at the editorial board of RTS (Research - Transport - Safety).     

Advanced LMI based analysis and design for Acrobot walking

This article aims to further improve previously developed design for Acrobot walking based on partial exact feedback linearisation of order 3. Namely, such an exact system transformation leads to an almost linear system where error dynamics along trajectory to be tracked is a 4-dimensional linear time-varying system having three time-varying entries only, the remaining entries being either zero or one. In such a way, exponentially stable tracking can be obtained by quadratically stabilising a linear system with polytopic uncertainty. The current improvement is based on applying linear matrix inequalities (LMI) methods to solve this problem numerically. This careful analysis significantly improves previously known approaches. Numerical simulations of Acrobot walking based on the above-mentioned LMI design are demonstrated as well.     

Hybrid Approach for Addressing Uncertainty in Risk Assessments

Parameter uncertainty is a major aspect of the model-based estimation of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, other uncertainty theories have been proposed as alternative approaches to address model parameter uncertainty in situations where available information is insufficient to identify statistically representative probability distributions, due in particular to data scarcity. The simplest such theory is possibility theory, which uses so-called fuzzy numbers to represent model parameter uncertainty. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there are sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. This paper proposes an approach (termed a hybrid approach) which combines Monte Carlo random sampling of probability distribution functions with fuzzy calculus. The approach is applied to a real case of estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a way that is consistent with the information at hand. Also, because the hybrid approach takes advantage of the “rich” information provided by probability distributions, while retaining the conservative character of fuzzy calculus, it is believed to hold value in terms of a “reasonable” application of the precautionary principle.