Hybrid cam mechanisms

This paper investigates the problem of driving a machine with several reciprocating heavy inertias. The drive should also realize some programmable flexibility of the motion, so that the operation of the machine, e,g., synchronization between different motions and duration of standstills, can easily be modified. The use of a single servomotor for every motion is not the best solution, since it does not allow for energy recuperation. This means that the installed servomotor power will be much higher than the required net peak power for the machine as a whole. The presented solution, however, reduces the servomotor peak torque and power by one order of magnitude, and makes energy transfer between the motions possible. The hybrid cam mechanism consists of a hybrid drive, which is a combination of a servomotor, a constant velocity motor and a cam follower mechanism. The operation principle exploits the nonlinear characteristics of the cam to add flexibility at low cost of energy. The hybrid solution is particularly successful for motions involving high peak acceleration. In addition, the concept can be used to reject disturbances. Simulations show the performance of the hybrid cam mechanism.     

Tri-Reforming of Methane over NdM0.25Ni0.75O3 (M = Cr,Fe) Catalysts and the Effect of CO2 Composition

Catalysis Letters - The NdM0.25Ni0.75O3 (M?=?Cr, Fe) named NCN and NFN catalysts precursors were synthesized and characterized. The CO2 utilization in the feed was studied in the...     

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants.     

Single machine scheduling with simple linear deterioration to minimize total absolute deviation of completion times

We consider a single machine scheduling problem with simple linear deterioration. Job processing times are assumed to be a simple linear function of a job-dependent growth rate and the job's starting time. We seek an optimal schedule, so as to minimize the total absolute deviation of completion times (TADC). We prove several interesting properties of an optimal schedule, and introduce two efficient heuristics which are tested against a lower bound.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Production of perovskite-like La1−xCaxMnO3+δ ferromagnetic thin films by electrochemical reduction

Ferromagnetic and perovskite-like thin films (<1m) of La1–xCaxMnO3+ have been routinely prepared by heat treatment of an amorphous La–Ca–Mn precursor. The precursor was electrodeposited cathodically in the absence of oxygen and water onto polished silver substrates from a nonaqueous solution of the components' nitrates. Analysis by X-ray diffraction and SQUID magnetometry shows these materials exhibit the appropriate structural and magnetic phases indicative of colossal magnetoresistance.