Robust control strategies for multi-inventory systems with average flow constraints

In this paper, we consider multi-inventory systems in the presence of uncertain demand. We assume that (i) demand is unknown but bounded in an assigned compact set and (ii) the control inputs (controlled flows) are subject to assigned constraints. Given a long-term average demand, we select a nominal flow that feeds such a demand. In this context, we are interested in a control strategy that meets at each time all possible current demands and achieves the nominal flow in the average. We provide necessary and sufficient conditions for such a strategy to exist and we characterize the set of achievable flows. Such conditions are based on linear programming and thus they are constructive. In the special case of a static flow (i.e. a system with 0-capacity buffers) we show that the strategy must be affine. The dynamic problem can be solved by a linear-saturated control strategy (inspired by the previous one). We provide numerical analysis and illustrative examples.     

Enhancing a dependable multiserver operating system with temporal protection via resource reservations

Nowadays, microkernel-based systems are getting studied and adopted with a renewed interest in a wide number of IT scenarios. Their advantages over classical monolithic solutions mainly concern the dependability domain. By being capable of dynamically detect and solve non-expected behaviours within its core components, a microkernel-based OS would eventually run forever with no need to be restarted. Dependability in this context mainly aims at isolating components from a spatial point of view: a microkernel-based system may definitely not be adopted in the context of real-time environments, simply basing on this kind of protection only. One of the most active real-time research areas concerns adding temporal protection mechanisms to general purpose operating systems. By making use of such mechanisms, these systems become suitable for being adopted in the context of time-sensitive domains. Microkernel-based systems have always been thought of as a kind of platform not suited to real-time contexts, due to the high latencies introduced by the message passing technique as the only inter-process communication (IPC) facility within the system. With computer performances growing at a fairly high rate, this overhead becomes negligible with respect to the typical real-time processing times. In the last years, many algorithms belonging to the class of the so-called Resource Reservations (RRES) have been devised in order to provide the systems with the needed temporal isolation. By introducing a RRES-aware scheduler in the context of a microkernel-based system, we may enrich it with the temporal benefits it needs in order to be deployed within domains with real-time requirements. In this paper we propose a generic way to implement these mechanisms, dependent for a very small part on the underlying OS mechanisms. In order to show the generality of our RRES framework we implemented it in the context of Minix 3, a highly dependable microkernel-based OS with an impressive users base.     

Lane-change detection using a computational driver model

OBJECTIVE: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. METHOD: Using a "model tracing" methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model's simulated behavior with a driver's actual observed behavior and thus continually infers the driver's unobservable intentions from her or his observable actions. RESULTS: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. CONCLUSION: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. APPLICATION: By providing robust real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.     

MATTER: A tool for generating end-to-end IoT test scripts

Software Quality Journal - In the last few years, Internet of Things (IoT) systems have drastically increased their relevance in many fundamental sectors. For this reason, assuring their quality is...     

Lessons learned from programmers' experiences with one‐way constraints

One‐way constraints have been incorporated in many graphical user interface toolkits because they are simple to learn, easy to write, and can express many types of useful graphical relationships. This paper is an evaluative paper that examines users' experience with one‐way constraints in two user interface development toolkits, Garnet and Amulet, over a 15‐year time span. The lessons gained from this examination can help guide the design of future constraint systems. The most important lessons are that (1) constraints should be allowed to contain arbitrary code that is written in the underlying toolkit language and does not require any annotations, such as parameter declarations, (2) constraints are difficult to debug and better debugging tools are needed, and (3) programmers will readily use one‐way constraints to specify the graphical layout of an application, but must be carefully and time‐consumingly trained to use them for other purposes. Copyright © 2005 John Wiley & Sons, Ltd.     

Flying over the reality gap: From simulated to real indoor airships

Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approach. Jean-Christophe Zufferey and Alexis Guanella contributed equally to this work.     

Evolutionary morphogenesis for multi-cellular systems

With a gene required for each phenotypic trait, direct genetic encodings may show poor scalability to increasing phenotype length. Developmental systems may alleviate this problem by providing more efficient indirect genotype to phenotype mappings. A novel classification of multi-cellular developmental systems in evolvable hardware is introduced. It shows a category of developmental systems that up to now has rarely been explored. We argue that this category is where most of the benefits of developmental systems lie (e.g. speed, scalability, robustness, inter-cellular and environmental interactions that allow fault-tolerance or adaptivity). This article describes a very simple genetic encoding and developmental system designed for multi-cellular circuits that belongs to this category. We refer to it as the morphogenetic system. The morphogenetic system is inspired by gene expression and cellular differentiation. It focuses on low computational requirements which allows fast execution and a compact hardware implementation. The morphogenetic system shows better scalability compared to a direct genetic encoding in the evolution of structures of differentiated cells, and its dynamics provides fault-tolerance up to high fault rates. It outperforms a direct genetic encoding when evolving spiking neural networks for pattern recognition and robot navigation. The results obtained with the morphogenetic system indicate that this “minimalist” approach to developmental systems merits further study.

Hybrid automata, reachability, and Systems Biology

Hybrid automata are a powerful formalism for the representation of systems evolving according to both discrete and continuous laws. Unfortunately, undecidability soon emerges when one tries to automatically verify hybrid automata properties. An important verification problem is the reachability one that demands to decide whether a set of points is reachable from a starting region.If we focus on semi-algebraic hybrid automata the reachability problem is semi-decidable. However, high computational costs have to be afforded to solve it. We analyse this problem by exploiting some existing tools and we show that even simple examples cannot be efficiently solved. It is necessary to introduce approximations to reduce the number of variables, since this is the main source of runtime requirements. We propose some standard approximation methods based on Taylor polynomials and ad hoc strategies. We implement our methods within the software SAHA-Tool and we show their effectiveness on two biological examples: the Repressilator and the Delta-Notch protein signaling.     

Proactive thermal management in green datacenters

The increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Because of the increase in heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. Presently, thermal management techniques make an effort to thermally profile and control datacenters’ cooling equipment to increase their efficiency. In conventional thermal management techniques, cooling systems are triggered by the temperature crossing predefined thresholds. Such reactive approaches result in delayed response as the temperature may already be too high, which can result in performance degradation of hardware.     

Additive manufacturing of copper alloys: influence of process parameters and alloying elements

Owing to the physical properties of copper and its alloys it is challenging to achieve good surface quality and low porosity by the widely used laser-based additive manufacturing processes. This paper deals with the role of alloy composition, powder size and process parameters in additive manufacturing with laser beam melting machine (with power up to 100 W). Test parts were produced in pure copper and CuNiSi(Cr) alloys. The porosity was investigated as a function of different process parameters and powder size ranges. The effects of the alloy physical properties (reflectivity, thermal conductivity, melting range and surface tension) are discussed. Moreover, the effect of thermal treatment on the properties of CuNiSi parts was assessed in conventional two-step heat treatments.