IEEE Industrial Electronics Society
IEEE Industrial Electronics Society
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2009 - The Year of Growth - Richard Zurawski
Introducing Associate Editor - Weng Khuen Ho
Guest Editorial: Special Section on In-Vehicle Embedded Systems
Françoise Simonot-Lion
1. A Synchronization Protocol for Temporal Isolation of Software Components in Vehicular Systems
Nolte, Thomas; Shin, Insik; Sjödin, Mikael; Behnam, Moris
Abstract: We present a method that allows for integration of individually developed functions of software components into a predictable real-time system. The method has been designed to provide a lightweight mechanism that gives temporal firewalls between functions, preventing unpredictable side effects during function integration. The method maps well to the AUTOSAR software component model and can thus be used to facilitate seamless and predictable integration and isolation of AUTOSAR components that have been developed by different manufacturers.
Specifically, this paper presents a protocol for synchronization in a hierarchical real-time scheduling framework. Using our protocol, a software component does not need to know, and is not dependent on, the timing behavior of software components belonging to other functions; even though they share mutually exclusive resources. In this paper we also prove the correctness of our approach and evaluate its efficiency and cost in terms of system load in a vehicular context.
2. Stochastic Analysis of Distributed Real-time Automotive Systems
Zeng, Haibo; Di Natale, Marco; Giusto, Paulo; Sangiovanni-Vincentelli, Alberto
Abstract: Distributed architectures supporting the execution of real-time applications are common in automotive systems. Many applications, including most of those developed for active safety and chassis systems, must comply with hard real-time deadlines, but are nevertheless sensitive to the average latency of the end-to-end computations from sensors to actuators. A characterization of the timing behavior of functions is used to estimate the quality of an architecture configuration in the early stages of architecture selection. In this paper, we extend previous work on stochastic analysis of response times for software tasks to Controller Area Network messages, then compose them to compute probability distributions of end-to-end latencies. We present the results of the analysis on a realistic complex distributed automotive system. The distributions predicted by our method are very close to the probability of latency values measured on a simulated system. However, the faster computation time of the stochastic analysis is much better suited to the architecture exploration process, allowing a much larger number of configurations to be analyzed and evaluated.
3. Response Time Analysis in Multicore ECUs with Shared Resources
Schliecker, Simon; Negrean, Mircea; Ernst, Rolf
Abstract: As multiprocessor systems are increasingly used in automotive real-time environments, scheduling and synchronization analysis of these platforms receive growing attention. Upcoming multicore ECUs allow the integration of previously separated functionality for body electronics or sensor fusion onto a single unit, and allow the parallelization of complex computations over multiple cores. Multicore CPUs turn an ECU into a highly integrated "networked system" microcosm, in which complex interdependencies can be observed due to the use of shared resources even in partitioned scheduling. To deliver predictable performance, resource arbitration protocols are required and have been proposed in literature. This paper presents an novel analytical approach to provide the worst-case response time for real-time tasks in multiprocessor systems with shared resources. The method supports realistic, event- or time-driven task activation schemes and allows to calculate tight bounds on the estimated system performance.
4. Traffic Shaping for Resource-Efficient In-Vehicle Communication
Rahmani, Mehrnoush; Tappayuthpijarn, Ktawut; Krebs, Benjamin; Bogenberger, Richard; Steinbach, Eckehard
5. Towards Reliable Wireless Industrial Communication with Real-Time Guarantees
Jonsson, Magnus; Kunert, Kristina
Abstract: Increased mobility coupled with a possible reduction of cabling costs and deployment time makes wireless communication an attractive alternative for the automation industry and related application areas. Methods compensating for the high probability of bit errors accompanying wireless transmissions are, however, needed. This is predominantly important in industrial applications with strict reliability and timing requirements, which cannot be met by standard communication protocols as e.g. TCP. In this paper, way of combining retransmissions with real-time worst-case scheduling analysis is presented that can offer both a high grade of reliability and hard real-time support. The presented solution handles one or several retransmission attempts of erroneous data without jeopardizing already guaranteed delay bounds of other packets. A real-time analysis for a full-duplex, asymmetric link, utilizing the novel retransmission scheme and supporting both piggybacked and non-piggybacked acknowledgements, is provided. A simulation study is presented that evaluates the performance of the retransmission scheme for bit error rates typically experienced in wireless communication. The results clearly indicate a possible reduction of the message error rate by several orders of magnitude.
6. Formal approach to multi-modal control design: application to mode switching
Faraut, Gregory; Piétrac, Laurent; Niel, Eric
Abstract: A framework based on Supervisory Control Theory (SCT) is proposed to assist the design of multi-modal control for Discrete-Event Systems (DES). Our purpose handled modes which are conceptualized by using multi-model approach. Each mode represents a running part of the system, depending on the requirements to enforce and resources to activate. The resulted framework aims to design each mode independently first, and resolves conflicting connections between them secondly. The proposal carries out a formal way to build the final ready-to-use control laws. A flexible manufacturing system illustrates this approach.
7. Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification
Zhou, JH; Pang, Chee Khiang; Lewis, Frank; and Zhong, Zhao-Wei
Abstract: Identification and prediction of lifetime of industrial cutting tools using minimal sensors is crucial to reduce production costs and down-time in engineering systems. In this paper, we provide a formal decision software tool to extract the dominant features enabling tool wear prediction. This decision tool is based on a formal mathematical approach that selects dominant features using the singular value decomposition of real-time measurements from the sensors of an industrial cutting tool. Selection of dominant features is important, as retaining only essential features allows reduced signal processing or even reduction in the number of required sensors, which cuts costs. It is shown that the proposed method of dominant feature selection is optimal in the sense that it minimizes the least-squares estimation error. The identified dominant features are used with the Recursive Least Squares (RLS) algorithm to identify parameters in forecasting the time series of cutting tool wear. Experimental results on an industrial high speed milling machine show the effectiveness in predicting the tool wear using only the dominant features.
8. Value-at-Risk-Based Two-Stage Fuzzy Facility Location Problems
Wang, Shuming; Watada, Junzo; Pedrycz, Witold
Abstract: Reducing risks in location decisions when coping with imprecise information is critical in supply chain management so as to increase competitiveness and profitability. In this paper, a two-stage fuzzy facility location problem with Value-at-Risk (VaR), called VaR-FFLP, is proposed, which results in a two-stage fuzzy zero-one integer programming problem. Some properties of the VaR-FFLP, including the value of perfect information (VPI), the value of fuzzy solution (VFS), and the bounds of the fuzzy solution, are discussed. Since the fuzzy parameters of the location problem are represented in the form of continuous fuzzy variables, the determination of VaR is inherently an infinite-dimensional optimization problem that cannot be solved analytically. Therefore, a method based on the discretization of the fuzzy variables is proposed to approximate the VaR. The Approximation Approach converts the original problem into a finite-dimensional optimization problem. A pertinent convergence theorem for the Approximation Approach is proved. Subsequently, by combining the Simplex Algorithm, the Approximation Approach, and a mechanism of genotype-phenotype-mutation-based Binary Particle Swarm Optimization (GPM-BPSO), a hybrid GPM-BPSO algorithm is being exploited to solve the VaR-FFLP. A numerical example illustrates the effectiveness of the hybrid GPM-BPSO algorithm and shows its enhanced performance in comparison with the results obtained by other approaches using Genetic Algorithm (GA), Tabu Search (TS), and Boolean BPSO (B-BPSO).
9. Fuzzy Spectral and Spatial Feature Integration for Classification of Non-ferrous Materials in Hyper-spectral Data
Picon, Artzai; Ghita, Ovidiu; Whelan, Paul; Iriondo, Pedro
Abstract: Hyper-spectral data allows the construction of more elaborate models to sample the properties of the non-ferrous materials than the standard RGB color representation. In this paper, the non ferrous waste materials are studied as they cannot be sorted by classical procedures due to their color, weight and shape similarities. The experimental results presented in this paper reveal that factors such as the various levels of oxidization of the waste materials and the slight differences in their chemical composition preclude the use of the spectral features in a simplistic manner for robust material classification. To address these problems, the proposed FUSSER (FUzzy Spectral and Spatial classifiER) algorithm detailed in this paper merges the spectral and spatial features to obtain a combined feature vector that is able to better sample the properties of the non ferrous materials than the single pixel spectral features when applied to the construction of Multivariate Gaussian Distributions. This approach allows the implementation of statistical region merging techniques in order to increase the performance of the classification process. To achieve an efficient implementation, the dimensionality of the hyper-spectral data is reduced by constructing bio-inspired spectral fuzzy sets that minimize the amount of redundant information contained in adjacent hyper-spectral bands. The experimental results indicate that the proposed algorithm increased the overall classification rate from 44% using RGB data up to 98% when the spectral-spatial features are used for non-ferrous material classification.
10. Fulfillment of retailer demand by using the MDL-optimal neural network prediction and decision policy
Ning, Andrew; Lau, Henry; Zhao, Yi; Wong, Eric
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