Heuristics and Diagnostics for Complex Systems

International audience

International audience

International audience

We propose an assessing method of mixture model in a cluster analysis setting with integrated completed likelihood. For this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. Then, the integrated completed likelihood (ICL) is approximated using the Bayesian information criterion (BIC). Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of dusters leading to a sensible partitioning of the data.

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called .632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.

We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach in-cludes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solu-tion benefits from unlabeled data. The method challenges mixture mod-els when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the “cluster assumption”. Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces. 1

In this paper, we present a controller design and its implementation on a mini rotorcraft having four rotors. The dynamic model of the four-rotor rotorcraft is obtained via a Lagrange approach. The proposed controller is based on Lyapunov analysis using a nested saturation algorithm. The global stability analysis of the closed-loop system is presented. Real-time experiments show that the controller is able to perform autonomously the tasks of taking off, hovering, and landing.

Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.

This paper gives a broad overview of the stability and control of time-delay systems. Emphasis is on the more recent progress and engineering applications. Examples of practical problems, mathematical descriptions, stability and performance analysis, and feedback control are discussed.

Although virtual reality (VR) has many applications, only few studies have investigated user acceptance of this type of immersive technology. We propose an extended version of the Technology Acceptance Model (TAM) that addresses some aspects of VR. Our model includes variables from the TAM, user experience, variables specific to VR, and variables relating to user characteristics. This model was tested with 89 users who performed an aeronautical assembly task in VR. Results suggest that intention to use VR is positively influenced by perceived usefulness and negatively influenced by cybersickness. Hedonic quality-stimulation and personal innovativeness are predictors of perceived usefulness. Perceived ease of use does not have a significant impact on intention to use and it is only influenced by pragmatic quality. These findings have a number of implications regarding user acceptance of VR.

Unquestionably, communicating entities ( object , or things ) in the Internet of Things (IoT) context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA (Defense Advanced Research Projects Agency) as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the IoT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach . The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the IoT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current standardization activities are surveyed and discussed to the ensure the security of IoT components and applications.

This paper presents the control of an underactuated two-link robot called the Pendubot. We propose a controller for swinging the linkage and raise it to its uppermost unstable equilibrium position. The balancing control is based on an energy approach and the passivity properties of the system.

International audience

7R20. Non-linear Control for Underactuated Mechanical Systems. - I Fantoni and R Lozano (UMR CNRS 6599, Univ de Technologie de Compiegne, BP 20529, Compiegne, 60205, France). Springer-Verlag London Ltd, Surrey, UK. 2002. 295 pp. ISBN 1-85233-423-1. $109.00. Reviewed by SC Sinha (Dept of Mech Eng, Auburn Univ, 202 Ross Hall, Auburn AL 36849-5341).This is an application-oriented book that is intended for engineers, graduate students, and researchers who are interested in the design of nonlinear controllers for underactuated mechanical systems. Underactuated systems are defined as systems with lesser number of independent control actuators than the degrees of freedom to be controlled. Readers with a background in intermediate level dynamics and control should have no trouble following the material. After an introduction in Chapter 1, some definitions and background material are presented in Chapter 2. These include Lyapunov stability concepts, Krasovskii-LaSalle invariance principle, passivity criteria, and controllability condition. The next nine chapters deal with the development of control algorithms for a number of underactuated mechanical systems, most of which have served as academic benchmarks. In Chapter 1, an energy-based nonlinear controller is developed for the classical problem of cart-inverted pendulum. Simulations and experimental results are also included. Similar analysis is presented in Chapter 4 that deals with a convey-crane system. Using the passivity property, an energy-based control law is proposed for a pendubot system in Chapter 5.In Chapter 6, modeling, analysis, and control of the Furuta pendulum (named after K Furuta of Tokyo Institute of Technology) is discussed. The reaction wheel pendulum is presented in Chapter 7. Chapters 8 and 9 are devoted to the design of control algorithms for planar robots. In Chapter 8, the authors present analysis and control of two as well as three link planar robots with flexible joints while in Chapter 9, the problem of a planar robot with two prismatic and one revolute (PPR) joints is considered. It is to be observed that the PPR system has four degrees of freedom and only three control inputs. The control strategy, once again, is based on an energy approach and passivity properties of the system. The ball-beam control is described in Chapter 10, and in this case, the control force is assumed to be acting on the ball rather than the beam. The controller for a simple hovercraft model is designed in Chapter 11. Chapter 12 deals with the control problem associated with a planar vertical take-off and landing (PVTOL) aircraft. A Lyapunov function using the forwarding technique is used to obtain the control law. The last three chapters are devoted to the modeling and control of helicopters. In Chapter 13, after some general considerations, a simplified model called the helicopter-platform model is analyzed. This model has three degrees of freedom with two control inputs. The nonlinear control strategy guarantees an asymptotic tracking of the desired trajectories. A Lagrangian formulation of the helicopter dynamics is presented in Chapter 14 and a Lyapunov approach is used to design a tracking controller. The same problem is discussed in Chapter 15 via the Newtonian approach. In this case, the control algorithm was developed using the backstepping and Lyapunov techniques. In summary, Non-linear Control for Underactuated Mechanical Systems is an excellent source for the development of control strategies for an important class of problems, viz, the underactuated mechanical systems. The book is clearly written, and the ideas are conveyed well by the authors. This book is a must for engineering graduate students, researchers, teachers, as well as practicing engineers. It is strongly recommended for individuals and libraries.

A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the class membership of each prototype are determined by minimizing the mean squared differences between the classifier outputs and target values. After training, the classifier computes for each input vector a BBA that provides a description of the uncertainty pertaining to the class of the current pattern, given the available evidence. This information may be used to implement various decision rules allowing for ambiguous pattern rejection and novelty detection. The outputs of several classifiers may also be combined in a sensor fusion context, yielding decision procedures which are very robust to sensor failures or changes in the system environment. Experiments with simulated and real data demonstrate the excellent performance of this classification scheme as compared to existing statistical and neural network techniques.

Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the ego-vehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.

The fast-paced development of Unmanned Aerial Vehicles (UAVs) and their use in different domains, opens a new paradigm on their use in natural disaster management. In UAV-assisted disaster management applications, UAVs not only survey the affected area but also assist in establishing the communication network between the disaster survivors, rescue teams and nearest available cellular infrastructure. This paper identifies main disaster management applications of UAV networks and discusses open research issues related to UAV-assisted disaster management.

International audience