An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.     

Sensory analysis of polystyrene packaging material taint in cocoa powder for drinks and chocolate flakes

Polystyrene packaging material taint was sensorily evaluated in cocoa powder for drinks and chocolate flakes using short-cut signal detection measures on differences between control and test samples and on recognition of styrene. No differences were observed in cocoa powder for drinks and plain chocolate flakes treated with 0.5 dm2 polystyrene of 1 mm thickness. However, differences were detected in milk chocolate flakes and plain chocolate flakes, which were in contact with a larger area or thicker polystyrene packaging material. The latter results were confirmed by the styrene recognition test, so polystyrene is a potential source of off-flavour for chocolate products. The amount of residual styrene in the polystyrene used was about 320 ppm, while the amounts of styrene ranged from 7 to 132 ppb in cocoa drinks and from 414 to 1447 ppb in chocolate flakes.     

Online and Offline Rotary Regression Analysis of Torque Estimator for Switched Reluctance Motor Drives

A new torque estimator for switched reluctance motor (SRM) drives based on 2-D rotary regression analysis is presented in this paper. The proposed torque estimator is composed of a bicubic regressive polynomial as a function of rotor position and input current. The regressive coefficients can be computed offline or online from the torque characteristics acquired either experimentally or from numerical computation. Furthermore, a torque estimation method by taking mutual coupling into consideration is proposed. It can be seen that the estimated and experimentally obtained self-coupling and mutual-coupling torque characteristics are in good agreement with each other. In addition, the dynamic torque waveforms with and without the mutual coupling, estimated by the proposed estimator, are found to be virtually the same as those obtained from the bicubic spline interpolation for SRM drives with single-pulse voltage, hysteresis current chopping, as well as with voltage pulse width modulation control. The success of all the case studies being reported is a good validation of the usefulness and accuracy of the proposed real-time torque estimator that, as described in this paper, can be used to quickly estimate the instantaneous output torque of SRM drives.     

Entropy of multidimensional cellular automata

Since the topological entropy of a vast class of two-dimensional cellular automata (CA) is infinite, of interest is the possibility to renormalize it so that to obtain a positive finite value. We find the asymptotics of the information function of a multidimensional CA and, accordingly, introduce the renormalized topological entropy as a coefficient of this asymptotics. We describe some properties of the introduced quantity, in particular, its positivity for CA of the type of “The Game of Life.” Also, we give an example of an explicit evaluation of this parameter for a particular cellular automaton.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Sequential Bayesian bit error rate measurement

As bit error rates decrease, the time required to measure a bit error rate (BER) or perform a BER test (i.e., to determine that a particular communications device's BER is less than some acceptable limit) increases dramatically. One cause of long measurement times is the difficulty of deciding a priori how many bits to measure to establish the BER to within a predetermined confidence interval width. This paper explores a new approach to deciding how many bits to measure, namely a sequential Bayesian approach. As measurement proceeds, the posterior distribution of BER is checked to see if the conclusion can be made that the BER rate is known to be within the desired range with high enough probability. Desired properties of the posterior distribution such as the maximum a postiori estimate and confidence limits can be computed quickly using off-the-shelf numerical software. Examples are given of using this method on bit error data measured with an Agilent 81250 parallel BER tester.     

A XRD study of Co/Au multilayers using a laboratory microdiffractometer

In this paper we show that by the analysis of 2D images collected with a laboratory X-ray microdiffractometer it is possible to non-destructively evaluate the structure, the microstructure, and the preferred orientation of films. In particular, the structural analysis of Co/Au multilayers on Si(1 0 0) deposited at different Ar pressures are reported and discussed.