Summary Swelling equilibrium of polyelectrolyte copolymer gels containing of acrylamide (AAm) and 2-acrylamido-2-methyl-1-propanesulfonic
acid sodium salt (AMPS) have been studied as a function of copolymer composition. AAm/AMPS hydrogels were prepared by free
radical solution polymerization in aqueous solution of AAm with AMPS as anionic comonomer and two multifunctional crosslinkers
such as ethylene glycol dimethacrylate (EGDMA) and trimethylolpropane triacrylate (TMPTA). Swelling experiments were performed
in water at 25 °C, gravimetrically. The influence of AMPS content in hydrogels was examined. Swelling of AAm/AMPS hydrogels
was increased up to 1018% (for containing 2% AMPS and crosslinked by EGDMA) 15246% (for containing 8% AMPS and crosslinked
by TMPTA), while AAm hydrogels swelled up to 804% (crosslinked by TMPTA)–770% (crosslinked by EGDMA). The values of equilibrium
water content of the hydrogels are 0.8851–0.9935. Diffusion behavior was investigated. Water diffusion into hydrogels was
found to be non-Fickian in character. 相似文献
The wettability and infiltration of molten ZrSi2 and ZrSi2-Lu2O3 alloys into Cf/SiC and B4C-infiltrated Cf/SiC composites were investigated to understand the interfacial interactions that occur during the development of Cf/SiC-ZrC and Cf/SiC-ZrB2-ZrC-Lu2O3 materials. A significant evaporation of Si from the liquid affected the wetting behaviour of the alloy when tested in a vacuum at 1670 °C. The better wetting and spreading of the alloy over the surface was observed for the composites with lower overall porosity (12 %). On the other hand, the formation of an outer dense layer, followed up by the uniform infiltrated region up to ~ 1 mm was observed for the Cf/SiC with higher porosity (21 %). The infiltrated alloy reacted with SiC matrix to form ZrC or with B4C-infiltrated SiC matrix to form ZrB2-ZrC-SiC. The Lu2O3 particles were not wetted by the melt, and were pushed away of the reaction zone by the solidification front. 相似文献
Abstract: Features are used to represent patterns with minimal loss of important information. The feature vector, which is composed of the set of all features used to describe a pattern, is a reduced‐dimensional representation of that pattern. Medical diagnostic accuracies can be improved when the pattern is simplified through representation by important features. By identifying a set of salient features, the noise in a classification model can be reduced, resulting in more accurate classification. In this study, a signal‐to‐noise ratio saliency measure was employed to determine the saliency of input features of recurrent neural networks (RNNs) used in classification of ophthalmic arterial Doppler signals. Eigenvector methods were used to extract features representing the ophthalmic arterial Doppler signals. The RNNs used in the ophthalmic arterial Doppler signal classification were trained for the signal‐to‐noise ratio screening method. The application results of the signal‐to‐noise ratio screening method to the ophthalmic arterial Doppler signals demonstrated that classification accuracies of RNNs with salient input features are higher than those of RNNs with salient and non‐salient input features. 相似文献
In this paper, a new matching pursuits dissimilarity measure (MPDM) is presented that compares two signals using the information provided by their matching pursuits (MP) approximations, without requiring any prior domain knowledge. MPDM is a flexible and differentiable measure that can be used to perform shape-based comparisons and fuzzy clustering of very high-dimensional, possibly compressed, data. A novel prototype based classification algorithm, which is termed the computer aided minimization procedure (CAMP), is also proposed. The CAMP algorithm uses the MPDM with the competitive agglomeration (CA) fuzzy clustering algorithm to build reliable shape based prototypes for classification. MP is a well known sparse signal approximation technique, which is commonly used for video and image coding. The dictionary and coefficient information produced by MP has previously been used to define features to build discrimination and prototype based classifiers. However, existing MP based classification applications are quite problem domain specific, thus making their generalization to other problems quite difficult. The proposed CAMP algorithm is the first MP based classification system that requires no assumptions about the problem domain and builds a bridge between the MP and fuzzy clustering algorithms. Experimental results also show that the CAMP algorithm is more resilient to outliers in test data than the multilayer perceptron (MLP) and support-vector-machine (SVM) classifiers, as well as prototype-based classifiers using the Euclidean distance as their dissimilarity measure. 相似文献
We present a comprehensive review of the evolutionary design of neural network architectures. This work is motivated by the fact that the success of an Artificial Neural Network (ANN) highly depends on its architecture and among many approaches Evolutionary Computation, which is a set of global-search methods inspired by biological evolution has been proved to be an efficient approach for optimizing neural network structures. Initial attempts for automating architecture design by applying evolutionary approaches start in the late 1980s and have attracted significant interest until today. In this context, we examined the historical progress and analyzed all relevant scientific papers with a special emphasis on how evolutionary computation techniques were adopted and various encoding strategies proposed. We summarized key aspects of methodology, discussed common challenges, and investigated the works in chronological order by dividing the entire timeframe into three periods. The first period covers early works focusing on the optimization of simple ANN architectures with a variety of solutions proposed on chromosome representation. In the second period, the rise of more powerful methods and hybrid approaches were surveyed. In parallel with the recent advances, the last period covers the Deep Learning Era, in which research direction is shifted towards configuring advanced models of deep neural networks. Finally, we propose open problems for future research in the field of neural architecture search and provide insights for fully automated machine learning. Our aim is to provide a complete reference of works in this subject and guide researchers towards promising directions.