Complexity reduction in MPC for stochastic max-plus-linear discrete event systems by variability expansion

Model predictive control (MPC) is a popular controller design technique in the process industry. Recently, MPC has been extended to a class of discrete event systems that can be described by a model that is “linear” in the max-plus algebra. In this context both the perturbations-free case and for the case with noise and/or modeling errors in a bounded or stochastic setting have been considered. In each of these cases an optimization problem has to be solved on-line at each event step in order to determine the MPC input. This paper considers a method to reduce the computational complexity of this optimization problem, based on variability expansion. In particular, it is shown that the computational load is reduced if one decreases the level of “randomness” in the system.     

Dwell-time controllers for stochastic systems with switching Markov chain

We study the problem of feedback stabilization of a family of nonlinear stochastic systems with switching mechanism modeled by a Markov chain. We introduce a novel notion of stability under switching, which guarantees a given probability that the trajectories of the system hit some target set in finite time and remain thereinafter. Our main contribution is to prove that if the expectation of the time between two consecutive switching (dwell time) is “sufficiently large”, then the system is stable under switching with guaranteed probability. We illustrate this methodology by constructing measurement feedback controllers for a wide class of stochastic nonlinear systems.     

Toward designing a secure biosurveillance cloud

Biosurveillance is very complex, and it complements traditional public health surveillance to provide both early warning of infectious disease events and leads to situational awareness as well as to signaling any potential threat for using biological agents as weapons of mass destruction. Biosurveillance requires close cooperation and rapid information-sharing among many healthcare partners including primary care units and the biosurveillance hubs. Achieving improvements in this direction has become a bipartisan top priority for governments and institutions. Currently there are many national and international centers envisioned as clouds for intelligence on biological threats, however security obstacles have hindered their progress. This article investigates the requirements for a biosurveillance secure cloud. The investigation identifies the major security components needed to build a trusted environment for cloud based biosurveillance system through the integration of the public health enterprise private cloud with public clouds based on the Distributed OSGi framework along with a distributed authentication service. The trusted environment allows biosurveillance to be conducted over primary care private clouds including patient information from the electronic medical records.     

An assertion-based verification methodology for system-level design

In this paper, we integrate an assertion-based verification methodology with our object-oriented system-level synthesis methodology to address the problem of HW/SW co-verification. In this direction a system-level assertion language is defined. The system-level assertions can be used to monitor the current state of system or flow of transactions. These assertions are automatically converted to “monitor hardware” or “monitor software” during the system-level synthesis process depending on their type and also synthesis style of their corresponding functions. The synthesized assertions are functionally equivalent to their original system-level assertions, and hence, can be reused to verify the system after HW/SW synthesis and also at run-time after system manufacturing. This way, not only system-level assertions are reused in lower-levels of abstraction, but also run-time verification of system is provided. In this paper, we describe the system-level assertion language and explain the corresponding synthesis method in our object-oriented system-level synthesis methodology; however the concept can be applied to any system-level design methodology with modifications to assertion types and synthesis method.     

Improved artificial bee colony algorithm for global optimization

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely “ABC/best/1” and “ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing “ABC/rand/1” and “ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.     

Detection and tracking for robotic visual servoing systems

Robot manipulators require knowledge about their environment in order to perform their desired actions. In several robotic tasks, vision sensors play a critical role by providing the necessary quantity and quality of information regarding the robot's environment. For example, “visual servoing” algorithms may control a robot manipulator in order to track moving objects that are being imaged by a camera. Current visual servoing systems often lack the ability to detect automatically objects that appear within the camera's field of view. In this research, we present a robust “figureiground” framework for visually detecting objects of interest. An important contribution of this research is a collection of optimization schemes that allow the detection framework to operate within the real-time limits of visual servoing systems. The most significant of these schemes involves the use of “spontaneous” and “continuous” domains. The number and location of continuous domains are. allowed to change over time, adjusting to the dynamic conditions of the detection process. We have developed actual servoing systems in order to test the framework's feasibility and to demonstrate its usefulness for visually controlling a robot manipulator.     

Global sensitivity analysis of large-scale numerical landslide models based on Gaussian-Process meta-modeling

Large-scale landslide prediction is typically based on numerical modeling, with computer codes generally involving a large number of input parameters. Addressing the influence of each of them on the final result and providing a ranking procedure may be useful for risk management purposes. This can be performed by a variance-based global sensitivity analysis. Nevertheless, such an analysis requires a large number of computer code simulations, which appears impracticable for computationally demanding simulations, with computation times ranging from several hours to several days. To overcome this difficulty, we propose a “meta-model”-based strategy consisting in replacing the complex simulator by a “statistical approximation” provided by a Gaussian-process (GP) model. This allows computation of sensitivity measures from a limited number of simulations. For illustrative purposes, the proposed methodology is used to rank in terms of importance the properties of the elastoplastic model describing the complex behavior of the slip surface in the La Frasse landslide (Switzerland). One limitation of the GP-based methodology is that the computation of sensitivity measures is associated with uncertainty as the simulator is approximated using a training sample of small size, i.e., a limited knowledge on the “true” simulator. This source of uncertainty can be taken into account by treating the GP model from a Bayesian perspective. This provides the full posterior probability distribution associated with the sensitivity measures, which can be summarized by a confidence interval to outline the regions where the GP model is “unsure.” We show that this methodology is able to provide useful guidelines for the practical decision-making process and suggest further site investigations.     

Ontology-centered syndromic surveillance for bioterrorism

In recent years, public health surveillance has become a priority, driven by concerns of possible bioterrorist attacks and disease outbreaks. Authorities argue that syndromic surveillance, or the monitoring of prediagnostic health-related data for early detection of nascent outbreaks, is crucial to preventing massive illness and death. Syndromic surveillance could prevent widespread illness and death, but public-health analysts face many technical barriers. To meet syndromic surveillance's complex operational and research needs, and as part of DARPA'S national biosurveillance technology program, we've developed BioSTORM (the biological spatio-temporal outbreak reasoning module). BioSTORM is an experimental end-to-end computational framework that integrates disparate data sources and deploys various analytic problem solvers to support public health analysts in interpreting surveillance data and identifying disease outbreaks. BioSTORM can help them by supporting ontology-based data integration and problem-solver deployment.     

On the determination of optimal costly measurement strategies for linear stochastic systems

This paper presents the formulation of a class of optimization problems dealing with selecting, at each instant of time, one measurement provided by one out of many sensors. Each measurement has an associated measurement cost. The basic problem is then to select an optimal measurement policy, during a specified observation time interval, so that a weighted combination of “prediction accuracy” and accumulated “observation cost” is optimized. The current analysis is limited to the class of linear stochastic dynamic systems and measurement subsystems. The problem of selecting the optimal measurement strategy can be transformed into a deterministic optimal control problem. An iterative digital computer algorithm is suggested for obtaining numerical results. It is shown that the optimal measurement policy and the associated “matched” Kalman-type filter can be precomputed, i.e. specified before the measurements actually occur. Numerical results for a third-order system with two possible measurements are presented.     

Policy iteration for customer-average performance optimization of closed queueing systems

We consider the optimization of queueing systems with service rates depending on system states. The optimization criterion is the long-run customer-average performance, which is an important performance metric, different from the traditional time-average performance. We first establish, with perturbation analysis, a difference equation of the customer-average performance in closed networks with exponentially distributed service times and state-dependent service rates. Then we propose a policy iteration optimization algorithm based on this difference equation. This algorithm can be implemented on-line with a single sample path and does not require knowing the routing probabilities of queueing systems. Finally, we give numerical experiments which demonstrate the efficiency of our algorithm. This paper gives a new direction to efficiently optimize the “customer-centric” performance in queueing systems.     

Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization

In a wafer fabrication Fab, the “integrated delivery”, which integrates the automated material handling system (AMHS) with processing tools to automate the material flow, is difficult to implement due to the system complexity and uncertainty. The previous dispatching studies in semiconductor manufacturing have mainly focused on the tool dispatching. Few studies have been done for analyzing combinatorial dispatching rules including lot dispatching, batch dispatching and automated guided vehicle (AGV) dispatching. To handle this problem, a GA (genetic algorithm) based simulation optimization methodology, which consists of the on-line scheduler and the off-line scheduler, is presented in this paper. The on-line scheduler is used to monitor and implement optimal combinatorial dispatching rules to the semiconductor wafer fabrication system. The off-line scheduler is employed to search for optimal combinatorial dispatching rules. In this study, the response surface methodology is adopted to optimize the GA parameters. Finally, an experimental bay of wafer fabrication Fab is constructed and numerical experiments show that the proposed approach can significantly improve the performance of the “integrated delivery system” compared with the traditional single dispatching rule approach.     

Constrained RHC for LPV systems with bounded rates of parameter variations

This paper studies a stabilization problem of polytopically uncertain linear parameter varying systems with input constraints and bounded rates of parameter variations. In the framework of finite receding horizon control (RHC), a system containing “parameter” uncertainties is modified into a system with “parameter-incremental” uncertainties within each horizon. For the system modified in this manner, a robust RHC is designed by solving an optimization problem at each time instant. Based on the feasibility of the problem and the optimality of its solution, the closed-loop system stability is guaranteed. A numerical example is included to illustrate the validity of the results.     

Using basic Statechart to program industrial controllers

Despite the many studies that have attempted to develop “friendly” methods for industrial controller programming, they are still programmed by conventional “trial-and-error” methods and in practice, there is little written documentation on these systems. The ideal solution is to use a computational environment that allows industrial engineers to implement the system using high-level language and that follows international standards. Accordingly, this paper proposes a methodology to model and validate control programs for manufacturing systems that include sequential, parallel and timed operations, using a formalism based on Statecharts, denominated Basic Statechart (BSC). To improve the formal aspects of the original Statecharts, we introduce the semantic of the BSC using only characteristics relevant to the industrial area. We also present an algorithm that translates the control model described in BSC into Ladder diagrams, thereby enabling tests with actual PLCs. Finally, one typical example of application in the manufacturing area is discussed as case study to illustrate the proposed methodology.     

Contending with Terms: “Multimodal” and “Multimedia” in the Academic and Public Spheres

Scholars have begun naming and defining terms that describe the multifaceted kinds of composing practices occurring in their classrooms and scholarship. This paper analyzes the terms “multimedia” and “multimodal,” examining how each term has been defined and presenting examples of documents, surveys, web sites and others to show when and how each term is used in both academic and non-academic/industry contexts. This paper shows that rather than the use of these terms being driven by any difference in their definitions, their use is more contingent upon the context and the audience to whom a particular discussion is being directed. While “multimedia” is used more frequently in public/industry contexts, “multimodal” is preferred in the field of composition and rhetoric. This preference for terms can be best explained by understanding the differences in how texts are valued and evaluated in these contexts. “Multimodal” is a term valued by instructors because of its emphasis on design and process, whereas “multimedia” is valued in the public sphere because of its emphasis on the production of a deliverable text. Ultimately, instructors need to continue using both terms in their teaching and scholarship because although “multimodal” is a term that is more theoretically accurate to describe the cognitive and socially situated choices students are making in their compositions, “multimedia” works as a gateway term for instructors and scholars to interface with those outside of academia in familiar and important ways.     

OWA trees and their role in security modeling using attack trees

We introduce the idea of an OWA node as an extension of the “and/or” node and use them to generalize “and/or” trees to OWA trees. We provide a semantics for an OWA node. Specifically while an “or” node requires only one of the children to be satisfied and an “and” node requires “all” the children be satisfied the OWA node allows us to model situations in which there is some probabilistic uncertainty in the number of children that need be satisfied. We then use these OWA nodes in the security related problem of constructing attack trees. Techniques for the evaluation of an OWA attack tree for the overall probability of success and cost of an attack are provided. A method is provided for describing different types of attackers.     

Control of discrete time nonlinear systems with a time-varying structure

In this paper, we present a control methodology for a class of discrete time nonlinear systems that depend on a possibly exogenous scheduling variable. This class of systems consists of an interpolation of nonlinear dynamic equations in strict feedback form, and it may represent systems with a time-varying nonlinear structure. Moreover, this class of systems is able to represent some cases of gain scheduling control, Takagi-Sugeno fuzzy systems, as well as input-output realizations of nonlinear systems which are approximated via localized linearizations. We present two control theorems, one using what we call a “global” approach (akin to traditional backstepping), and a “local” approach, our main result, where backstepping is again used but the control law is an interpolation of local control terms. An aircraft wing rock regulation problem with varying angle of attack is used to illustrate and compare the two approaches.     

A coordinate descent based method for geometry optimization of trusses

The main objective of the structure optimization is to reduce the manufacturing cost of the structure. However, economical design should not deviate from engineering restrictions and laws. Based on this concept, various methods have been invented to optimize structures. In this research, some variations in one of the very simple and primary methods called “coordinate descent” or “successive coordinate search” is used for geometry optimization of the trusses. In order to increase the optimization speed and decrease the solution time of the problem, fast matrix analysis methods has been employed leading to reduction of the time required for calculating of the objective function. Furthermore, an optimized solution seeking algorithm has been introduced. In this algorithm, some factors have been defined by which the relation between the optimization time and these factors have been calculated, both experimentally and mathematically and the appropriate values of these factors are obtained to reduce the optimization time. Moreover, the effect of increasing the accuracy of the optimum solution on the number of analyses has been studied. Analysis of the presented method shows that regardless of its simplicity, it can be utilized as a fast method for optimizing variety of structures.     

Quantitative Characterization of Event Streams in Analysis of Hard Real-Time Applications

Many real-time embedded systems process event streams that are composed of a finite number of different event types. Each different event type on the stream would typically impose a different workload to the system, and thus the knowledge of possible correlations and dependencies between the different event types could be exploited to get tighter analytic performance bounds of the complete system. We propose an abstract stream model to characterize such an event stream. The model captures the needed information of all possible traces of a class of event streams. Hence, it can be used to obtain hard bounded worst-case and best-case analysis results of a system. We show how the proposed abstract stream model can be obtained from a concrete stream specification, and how it can be used for performance analysis. The applicability of our approach and its advantages over traditional worst-case performance analysis are shown in a case study of a multimedia application.Ernesto Wandeler is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and tools for system-level performance analysis of real-time embedded systems. He holds a Dipl. El.-Ing. degree from ETH Zurich. In 2003, he received the Willi Studer Price and the ETH Medal, both from the Swiss Federal Institute of Technology, Zurich. He is a student member of the IEEE and the ACM.Alexander Maxiaguine is a Ph.D. student at the Computer Engineering and Networks Laboratory of the Swiss Federal Institute of Technology, Zurich. His research interests include models and methods for system-level performance analysis and scheduling of embedded multiprocessor architectures, especially for real-time multimedia applications. Maxiaguine has an M.S. in electrical engineering from the Moscow Technical University of Communications and Informatics. He is a member of the IEEE and the ACM.Lothar Thiele is a full professor of computer engineering at the Swiss Federal Institute of Technology, Zurich. His research interests include models, methods and software tools for the design of embedded systems, embedded software and bioinspired optimization techniques. In 1986 he received the Dissertation Award of the Technical University of Munich, in 1987, the Outstanding Young Author Award of the IEEE Circuits and Systems Society, in 1988, the Browder J. Thompson Memorial Award of the IEEE, and in 2000–2001, the IBM Faculty Partnership Award. In 2004, he joined the German Academy of Natural Scientists Leopoldina.