Evolution of the capillary network in a reactive powder concrete during hydration process

Ultrasonic waves in echographic mode, combined with autogenous shrinkage measurements, were used to study the evolution of the capillary network of reactive powder concrete (RPC) from the time after the mixing. Two characteristic porous classes have been identified: the first, between 10 and 20 nm, begins when the material reaches its solid hyperstatic state, and the second about 1 or 2 nm. The first class is associated with the porous space between the C-S-H hydrate clusters and the second with the internal porosity of the hydrate. The evolution of the active capillary radius as a function of the degree of hydration allows us to understand the strong interaction between the capillary network size and the chemical activity given by the dissipated calorimetric power curve. Indeed, the maximum point of the chemical activity marks the transition of the first class of pores to the second one. Finally, measurements of electrical conductivity through RPC samples show that after the maximum of the dissipated power, the curve of this electrical conductivity presents the same evolution as the capillary radius. As the electrical conductivity clearly depends on the evolution of the capillary network, the similarity between the results confirms our analysis in pore classes.     

Generation of adaptive gait patterns for quadruped robot with CPG network including motor dynamic model

This paper describes the generation of adaptive gait patterns using new Central Pattern Generators (CPGs) including motor dynamic models for a quadruped robot under various environments. The CPGs act as the flexible oscillators of the joints and adjust joint angles to required values. The CPGs are interconnected with each other and sets of their coupling parameters are adjusted by a genetic algorithm so that the quadruped robot can realize stable and adequate gait patterns. Generation of gait patterns results in the formation of the CPG networks suitable for the formation of not only a straight walking pattern but also of rotating gait patterns. Experimental results demonstrate that the proposed CPG networks are effective for the automatic adjustment of the adaptive gait patterns for the tested quadruped robot under various environments. Furthermore, the target tracking control based on image processing is achieved by combining the general gait patterns. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(1): 35–43, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20225     

APPLICATION OF RESPONSE SURFACE METHODOLOGY FOR THE MAXIMIZATION OF CONCORA CRUSH RESISTANCE OF PAPERBOARD

Response surface methodology is often used by researchers in different fields to determine the optimum values for controlled variables to maximize or minimize the response variables. Either maximization or minimization might be necessary depending on the response property. For example, if the response variable represents the yield of a process, maximization could be necessary; on the other hand, if the response variable is the biological oxygen demand (BOD) of an effluent the aim would definitely be minimization

Response surface methodology can be used two ways. It can be applied to the full-scale production or it can be scaled to a laboratory or the pilot plant. When applied to the full-scale production, the method is known as evolutionary operation (EVOP). EVOP is the continuous optimization of a process. The optimum conditions in a production plant can change depending on many factors such as raw material, ambient temperature, and equipment wear. Therefore, controlled variables should be optimized continuously to keep the response variable as close as possible to the maximum or minimum value. Hence, controlled variables are systematically changed around a center point to depict any shift of the response variable from the extreme. A thorough discussion of EVOP is given by Box, Evolutionary Operation: A Method for Increasing Industrial Productivity, Appl. Statist., 6, 81-101 (1957).     

TRVs and fault clearing stresses in extra-high-voltage radial networks

To cope with the high rate of increase of power demand in the main industrialized districts in Japan, 550-kV transmission systems covering the districts have been reinforced, with most of the main power plants connected directly to these systems. Through 550-/300-kV substations, the majority of power to the districts is supplied by 300-kV systems. To limit the excess short-circuit capacity in the 300-kV systems, they tend to be reconstructed as so-called radial networks. In radial networks with high short-circuit capacity and relatively small number of transmission lines connected to the substation busbars, the rate of rise of TRV can be far higher than standard value. This paper analyzes the transient recovery voltages (TRV) in such extra-high-voltage radial networks in Japan, together with the relevant stresses to circuit-breakers during fault clearings. Future system conditions have also been introduced. As the typical rate of rise of the TRV values, more than twice that of today's standard ones are probable.     

Transformations in machining. Part 1. enhancement of wavelet transformation neural network (WT-NN) combination with a preprocessor

Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was used to classify the experimental cutting force data of milling operations previously. Preprocessing (PreP) of the approximation coefficients of the WT is proposed just before the classification by using the Adaptive Resonance Theory (ART2) type NNs. Genetic Algorithm (GA) was used to estimate the weights of each coefficient of the PreP. The WT-PreP-NN (ART2) combination worked at lower vigilances by creating only a few meaningful categories without any errors. The WT-NN (ART2) combination could obtain the same error rate only if very high vigilances are used and many categories are allowed.     

Thermodynamic state of reinforced interpenetrating polymer networks

The free energies of mixing of two networks in the interpenetrating polymer network based on crosslinked polyurethane and poly(ester acrylate) have been determined by the vapour sorption method. It was established that the constituent networks in the IPN are not miscible. The introduction of fillers of different chemical nature increases the compatibility. The thermodynamic affinity of the fillers to the individual networks and IPN was estimated. It was established that when the free energy of interaction of one or both components of the IPN with the filler is negative, reinforcement leads to the formation of a compatible and equilibrium system. For fillers having no affinity to the polymers, compatibilization is observed, which is connected with slowing down of phase separation in the system in the presence of filler.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008