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.     

ON THE ROBUST GLOBAL STABILIZATION OF nth-ORDER REACTIONS

In this paper, we employ a nonlinear adaptive mechanism to globally stabilize n^th-order reactions.     

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.     

A new voltage control equipment using fuzzy inference

Power system control equipment needs higher sensitivity and operational reliability. Advanced voltage control equipment is needed for reducing the frequency of tap changes and improving the characteristics (the relationship between the actual voltage and reference voltage) of the voltage to meet today's power system requirements. However, these objectives are in a trade-off relationship. Studies of voltage control derived from a knowledge base suitable for electric power systems can satisfy these objectives using fuzzy inference. Compared with corresponding conventional equipment, the new equipment improved the deviation of 30 min average voltage of 30 percent. This paper describes the design concept of new voltage control equipment using fuzzy inference. In addition, field test results are described along with rules of fuzzy inference, membership functions, and the deviation of 30 min average voltage through detailed simulation.     

浆砌石衬砌在渠道防渗中的应用及对策

总结了浆砌石应用的历史,介绍了浆砌石的结构特性和施工工艺,从造价方面、防渗效果及施工三方面分析了浆砌石衬砌主要存在的问题,并提出了相应的解决对策,指出浆砌石衬砌在渠道防渗应用中应控制施工质量,从而更好地提高防渗效果。     

凤滩水电厂自动发电控制策略

介绍了凤滩水电厂自动发电控制(AGC)策略的实现方法,重点介绍了修正等容量比例有功分配方法及相应措施.程序设计的原则是,机组紧跟目标负荷的情况下,在最佳效率下运行,同时减少机组开/停机次数、有功调节频率和跨越振动区次数,从而减少机组磨损,兼顾运行效率和机组运行安全.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

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     

Influence of polymer modulus on the percolation threshold of latex-based composites

Monodispersed copolymer emulsions with different glass transition temperatures were synthesized to investigate the effect of room temperature polymer matrix modulus on the electrical properties of carbon black (CB) filled segregated network composites. The emulsion with the highest modulus at room temperature produced composites with the lowest percolation threshold. The threshold for a composite made from a copolymer latex containing an equal ratio of butyl acrylate and methyl methacrylate (BA5) is 1.5 vol%, while the percolation threshold for the much lower modulus BA7 (7:3 BA/MMA ratio) is 4.93 vol%. The microstructure of each composite shows significant differences in the level of CB dispersion within the polymer matrix. Higher modulus polymer particles push the CB more efficiently into the interstitial space between them, resulting in a lower percolation threshold. This modulus effect was confirmed by increasing the drying temperature, where the moduli of latexes (BA5, BA5.5, and BA6) were more similar and the percolation thresholds for three composites also become closer to one another.     

Memory of Distributed Computer Systems: Distinctive Features of Their Architectural-Structural Organization and Use

Distinctive features of architectural-structural organization and use of memory networks are considered with the aim of choosing an optimal configuration in designing distributed computer systems.