Modeling and formal verification of embedded systems based on a Petri net representation

In this paper we concentrate on aspects related to modeling and formal verification of embedded systems. First, we define a formal model of computation for embedded systems based on Petri nets that can capture important features of such systems and allows their representation at different levels of granularity. Our modeling formalism has a well-defined semantics so that it supports a precise representation of the system, the use of formal methods to verify its correctness, and the automation of different tasks along the design process. Second, we propose an approach to the problem of formal verification of embedded systems represented in our modeling formalism. We make use of model checking to prove whether certain properties, expressed as temporal logic formulas, hold with respect to the system model. We introduce a systematic procedure to translate our model into timed automata so that it is possible to use available model checking tools. We propose two strategies for improving the verification efficiency, the first by applying correctness-preserving transformations and the second by exploring the degree of parallelism characteristic to the system. Some examples, including a realistic industrial case, demonstrate the efficiency of our approach on practical applications.     

On optimal pairwise linear classifiers for normal distributions: the d-dimensional case

We consider the well-studied pattern recognition problem of designing linear classifiers. When dealing with normally distributed classes, it is well known that the optimal Bayes classifier is linear only when the covariance matrices are equal. This was the only known condition for classifier linearity. In a previous work, we presented the theoretical framework for optimal pairwise linear classifiers for two-dimensional normally distributed random vectors. We derived the necessary and sufficient conditions that the distributions have to satisfy so as to yield the optimal linear classifier as a pair of straight lines.In this paper we extend the previous work to d-dimensional normally distributed random vectors. We provide the necessary and sufficient conditions needed so that the optimal Bayes classifier is a pair of hyperplanes. Various scenarios have been considered including one which resolves the multi-dimensional Minsky’s paradox for the perceptron. We have also provided some three-dimensional examples for all the cases, and tested the classification accuracy of the corresponding pairwise-linear classifier. In all the cases, these linear classifiers achieve very good performance. To demonstrate that the current pairwise-linear philosophy yields superior discriminants on real-life data, we have shown how linear classifiers determined using a maximum-likelihood estimate (MLE) applicable for this approach, yield better accuracy than the discriminants obtained by the traditional Fisher's classifier on a real-life data set. The multi-dimensional generalization of the MLE for these classifiers is currently being investigated.     

Interactive structural learning of Bayesian networks

We propose an hybrid approach for structure learning of Bayesian networks, in which a computer system and a human expert cooperate to search for the best structure. The system builds an initial tree structure which is graphically presented to the expert, and then the expert can modify this structure according to his knowledge of the domain. The system has several tools for aiding the human in this task: it allows for graphical editing (adding, deleting, inverting arcs) of the network, it shows graphically the correlation between variables, and it gives a measure of the quality and complexity for each structure. A measure which combines both quality and complexity, that we call quality, is defined. We have tested the tool in two domains: atmospheric pollution and car insurance, with good results.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.     

Electrocatalytic properties and electrochemical stability of polyaniline and polyaniline modified with platinum nanoparticles in formaldehyde medium

We report the electro-oxidation of formaldehyde on polyaniline films modified with Pt nanoparticles (PANI-Pt films) in an aqueous sulfuric acid solution. The electrodeposition of Pt particles on the PANI matrix was found to promote a mass loss of the film, with a similar effect occurring after cycling the PANI films in the acidic medium containing formaldehyde. However, despite the electrochemical instability of PANI-Pt films, on films containing larger amounts of incorporated Pt particles, the oxidation of formaldehyde produces adsorbed CO species (CO_ads) at a lower positive potential than on bulk Pt. Our findings also indicate that electro-oxidation of CO_ads on the PANI-Pt film surface depends not only on the thickness of PANI film but also on the amount of Pt particles incorporated into the PANI matrix.     

Benchmarking System for Evaluating Management Practices in the Construction Industry

This paper presents the results obtained for the initial application of a management evaluation system whose objective is to provide a continuous improvement tool for construction companies through benchmarking management practices. The outlined system seeks to support a benchmarking system that has been recently established in the Chilean construction industry by incorporating qualitative management aspects in addition to performance indicators. Different analyses were made to determine trends in the sector and to establish correlations between qualitative aspects coming from surveys and quantitative aspects coming from performance indicators. Thirteen construction companies participated in the initial application of the benchmarking system. A correlation analysis found that safety performance was strongly related to companies having superior planning and control, quality management, cost control, and subcontractor management policies. A factor analysis found that central office priorities focus on strategic management policies having longer-term competitive impact, while site management emphasizes tactical management dimensions having short-term impact. There is scope to elevate the profile of continuous improvement initiatives to strategic significance at the central office level.     

Thermal mixing between subchannels: measurement method and applications

A new thermal tracing technique is presented for the measurement of enthalpy interchange between connected subchannels in one-phase axial flow. The method is simple and versatile, being capable of implementation on compact and non-homogeneous bundles using water as fluid. To present the technique, measurements of turbulent mixing were performed on a triangular array bundle with a relation of pitch to diameter of 1.33, at Reynolds numbers between 10⁴ and 6.8×10⁴. For this simple arrangement the precision of the method is discussed showing that accuracies better than 10% can be readily achieved. Measurements are also performed in presence of a mixing vane showing another field were the method can be advantageously applied.     

Induced ordered weighted geometric operators and their use in the aggregation of multiplicative preference relations

In this article, we introduce the induced ordered weighted geometric (IOWG) operator and its properties. This is a more general type of OWG operator, which is based on the induced ordered weighted averaging (IOWA) operator. We provide some IOWG operators to aggregate multiplicative preference relations in group decision‐making (GDM) problems. In particular, we present the importance IOWG (I‐IOWG) operator, which induces the ordering of the argument values based on the importance of the information sources; the consistency IOWG (C‐IOWG) operator, which induces the ordering of the argument values based on the consistency of the information sources; and the preference IOWG (P‐IOWG) operator, which induces the ordering of the argument values based on the relative preference values associated with each one of them. We also provide a procedure to deal with “ties” regarding the ordering induced by the application of one of these IOWG operators. This procedure consists of a sequential application of the aforementioned IOWG operators. Finally, we analyze the reciprocity and consistency properties of the collective multiplicative preference relations obtained using IOWG operators. © 2004 Wiley Periodicals, Inc.     

Learning Structure from Data and Its Application to Ozone Prediction

In this paper we propose an algorithm for structure learning in predictive expert systems based on a probabilistic network representation. The idea is to have the simplest structure (minimum number of links) with acceptable predictive capability. The algorithm starts by building a tree structure based on measuring mutual information between pairs of variables, and then it adds links as necessary to obtain certain predictive performance. We have applied this method for ozone prediction in México City, where the ozone level is used as a global indicator for the air quality in different parts of the city. It is important to predict the ozone level a day, or at least several hours in advance, to reduce the health hazards and industrial losses that occur when the ozone reaches emergency levels. We obtained as a first approximation a tree-structured dependency model for predicting ozone in one part of the city. We observe that even with only three parameters, its estimations are acceptable.A causal network representation and the structure learning techniques produced some very interesting results for the ozone prediction problem. Firstly, we got some insight into the dependence structure of the phenomena. Secondly, we got an indication of which are the important and not so important variables for ozone forecasting. Taking this into account, the measurement and computational costs for ozone prediction could be reduced. And thirdly, we have obtained satisfactory short term ozone predictions based on a small set of the most important parameters.     

Proteomics Unveils Post-Mortem Changes in Beef Muscle Proteins and Provides Insight into Variations in Meat Quality Traits of Crossbred Young Steers and Heifers Raised in Feedlot

Proteomics has been widely used to study muscle biology and meat quality traits from different species including beef. Beef proteomics studies allow a better understanding of the biological processes related to meat quality trait determination. This study aimed to decipher by means of two-dimensional electrophoresis (2D-PAGE), mass spectrometry and bioinformatics the changes in post-mortem muscle with a focus on proteins differentially expressed in the Longissimus thoracis (LT) muscle of immunocastrated young heifers and steers. Carcass traits, chemical composition, pH, instrumental color (L*, a*, b*), cooking loss and Warner-Bratzler shear force (WBSF) of meat from F1 Montana-Nellore cattle were also evaluated. Backfat thickness (BFT) and intramuscular fat content (IMF) were 46.8% and 63.6% higher in heifers (p < 0.05), respectively, while evaporation losses (EL) were 10.22% lower compared to steers. No differences (p > 0.05) were observed for tenderness evaluated by WBSF (3, 10, and 17 days post-mortem), pH, and color traits (L*, a* and b*) between the experimental groups. The study revealed several proteins to be differentially expressed proteins in heifers compared steers (p < 0.05). In heifers, proteins involved in nutrient transport (TF, ALB, and MB), energy metabolism (ALDOA, GAPDH, and PKM), and oxidative stress and response to stress (HSPA8 and CA3) were associated with a greater BFT and IMF deposition. The higher expression of these proteins indicated greater oxidative capacity and lower glycolytic activity in the LT muscle of heifers. In steers, there was greater abundance of protein expression related to muscle contraction and proteins of structure (ACTA1, TPM2 and TNNT3), energy metabolism (ENO1, ENO3, PYGM, PGM1 and TPI1) and ATP metabolism (ATP5F1B, PEBP1 and AK1), indicating greater glycogenolysis in LT muscle, suggesting a shift in the glycolytic/oxidative fibers of steers.