Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.     

A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems

In this paper, we propose the use of a multiobjective evolutionary approach to generate a set of linguistic fuzzy-rule-based systems with different tradeoffs between accuracy and interpretability in regression problems. Accuracy and interpretability are measured in terms of approximation error and rule base (RB) complexity, respectively. The proposed approach is based on concurrently learning RBs and parameters of the membership functions of the associated linguistic labels. To manage the size of the search space, we have integrated the linguistic two-tuple representation model, which allows the symbolic translation of a label by only considering one parameter, with an efficient modification of the well known (2 + 2) Pareto archived evolution strategy (PAES). We tested our approach on nine real world datasets of different sizes and with different numbers of variables. Besides the (2 + 2)PAES, we have also used the well known nondominated sorting genetic algorithm (NSGA-II) and an accuracy-driven single-objective evolutionary algorithm (EA). We employed these optimization techniques both to concurrently learn rules and parameters and to learn only rules. We compared the different approaches by applying a nonparametric statistical test for pairwise comparisons, thus taking into consideration three representative points from the obtained Pareto fronts in the case of the multiobjective EAs. Finally, a data complexity measure, which is typically used in pattern recognition to evaluate the data density in terms of average number of patterns per variable, has been introduced to characterize regression problems. Results confirm the effectiveness of our approach, particularly for (possibly high dimensional) datasets with high values of the complexity metric.     

Matching-Pursuits Dissimilarity Measure for Shape-Based Comparison and Classification of High-Dimensional Data

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.     

MobSens: Making Smart Phones Smarter

In this article, we discuss experiences and lessons learned from deploying four mobile sensing applications on off-the-shelf mobile phones within a recreational framework called MobSens that contains elements of health, social, and environmental sensing at both individual and community levels. We describe the main components of our applications, which facilitate logging and external communications. We also outline the challenges faced when building and testing these applications and describe our strategies for overcoming them.     

Practical and scalable evolution of digital circuits

This paper addresses the scalability problem prevalent in the evolutionary design of digital circuits and shows that Evolvable Hardware (EHW) can indeed be considered as a viable alternative design methodology for large and complex circuits. Despite the effort by the EHW community to overcome the scalability problems using both direct mapped techniques and developmental approaches, so far only small circuits have been evolved. This paper shows that, by partitioning a digital circuit and making use of a modular developmental approach, namely, the Modular Developmental Cartesian Genetic Programming (MDCGP) technique, it is indeed possible to evolve large circuits. As a proof of concept, a 5 × 5 multiplier is evolved for partition sizes of 32 and 64. It is shown that compared to the direct evolution technique, the MDCGP technique provides five times reduction in terms of evolution times, 6–56% reduction in area and improved fault tolerance. The technique is readily scalable and can be applied to even larger partition sizes, and also to sequential circuits, thus providing a promising path to evolve large and complex circuits.     

Quasioptimal antenna array control in the problem of increasing noise immunity of information systems

Aquasioptimal method for control of antenna arrays in information-measurement systems is considered. The method is based on optimizing the form of directivity pattern of an array under complex amplitude current control in some of its elements. The advantage of the method is high-speed operation as a result of reducing expenses on calculating optimal parameters of the antenna system.     

Effect of a peripheral gas supply on the hydrodynamics of a spouting bed

The results of experimental investigations of the resistance of a pyramidal spouting bed with a peripheral gas supply are discussed, including the rates at the beginning and end of the process of spouting and material entrainment from an apparatus. Data is generalized in the form of dimensionless relations.Academic Scientific Complex A. V. Luikov Heat and Mass Transfer Institute of the Academy of Sciences of Belarus, Minsk. Translated from Inzhenerno-Fizicheskii Zhurnal, Vol. 67, Nos. 1–2, pp. 39–42, July–August, 1994.     

Stress relaxation in zirconium carbide. Report 2. Mechanisms of stress relaxation. The relationship of the processes of creep and relaxation

Mechanisms of stress relaxation in ZrC_1.00 (d=6–35 .m) in the area of the ductile-to-brittle transition temperature (1600–2200C) are considered. It is established that in the 1800–2000C range for ZrC_1.00 with grain sizes of 6 and 14–35 m, respectively, stress relaxation occurs as the result of pure grain boundary slip while at higher temperatures by intragranular cross slip. It is shown that creep in the steady stage and stress relaxation in ZrC_1.00 with grain sizes of 6–35 m are controlled by different physical processes, which makes impossible obtaining for these materials of data on stress relaxation by conversion with use of information on steady creep.Translated from Problemy Prochnosti, No. 2, pp. 55–60, February, 1994.     

Comparison of the ageing behaviour of PM 2124 Al alloy and Al-SiCp metal-matrix composite

The ageing response of 2124 Al-SiC particulate metal-matrix composite (MMC) and unreinforced alloy has been examined using hardness measurements and Arrhenius analysis. The formation of phases during precipitation has been studied using differential scanning calorimetry (DSC). The MMC exhibits accelerated ageing compared to unreinforced alloy, due to enhanced S formation. The activation energy for diffusion is lower in the MMC than in the unreinforced alloy. DSC scans show Guinier-Preston B (GPB) zone nucleation to occur at a lower temperature in the MMC, whilst the total volume of GPB zones formed is smaller than in the unreinforced alloy. A model has been proposed to explain the GPB zone formation behaviour, in which ease of GPB zone nucleation varies within the MMC, as a function of ageing time and of position within the matrix. S formation is enhanced in the MMC because of improved diffusion and a large increase in density of heterogeneous nucleation sites compared to the unreinforced alloy.