Development of a sequential robust–tolerance design model for a destructive quality characteristic

Robust design (RD) and tolerance design (TD) have received much attention from researchers and practitioners for more than two decades, and a number of methodologies for modeling and optimizing the RD and TD processes have been studied. However, there is ample room for improvement. Because most existing research considers RD and TD as separate research fields, the primary objective of this paper is to develop a sequential robust–tolerance design method to jointly determine the best factor settings and the closed-form solutions for the optimal specification limits. We then apply the proposed method to a destructive quality characteristic. Finally, a case study and sensitivity analyses are performed for verification purposes, and further studies are discussed.     

A dispatching method for automated lifting vehicles in automated port container terminals

In automated container terminals, containers are transported from the marshalling yard to a ship and vice versa by automated vehicles. The automated vehicle type studied in this paper is an automated lifting vehicle (ALV) that is capable of lifting a container from the ground by itself. This study discusses how to dispatch ALVs by utilizing information about pickup and delivery locations and time in future delivery tasks. A mixed-integer programming model is provided for assigning optimal delivery tasks to ALVs. A procedure for converting buffer constraints into time window constraints and a heuristic algorithm for overcoming the excessive computational time required for solving the mathematical model are suggested. Numerical experiments are reported to compare the objective values and computational times by a heuristic algorithm with those by an optimizing method and to analyze the effects of dual cycle operation, number of ALVs, and buffer capacity on the performance of ALVs.     

A combined work optimization technology under resource constraints with an application to road repair

We propose an approach for solving the task prioritization problem in road surface repair under bounded resources; the idea is to use a combination of defect recognition and classification methods based on statistical analysis and machine learning (random forests) with original methods for solving infinite-dimensional optimization problems (optical-geometric analogy). We show the results of a computational experiment that indicate high performance of the developed algorithms, and the resulting solutions were evaluated highly by experts in road facilities management. Our results may encourage more efficient use of resources to improve the quality of motorways.     

A decision tree using ID3 algorithm for English semantic analysis

Natural language processing has been studied for many years, and it has been applied to many researches and commercial applications. A new model is proposed in this paper, and is used in the English document-level emotional classification. In this survey, we proposed a new model by using an ID3 algorithm of a decision tree to classify semantics (positive, negative, and neutral) for the English documents. The semantic classification of our model is based on many rules which are generated by applying the ID3 algorithm to 115,000 English sentences of our English training data set. We test our new model on the English testing data set including 25,000 English documents, and achieve 63.6% accuracy of sentiment classification results.     

Dynamic behaviour of a magnetically actuated floating liquid marble

This paper reports the dynamic behaviour of a magnetically actuated floating liquid marble by analysing the oscillation of the marble. A liquid marble is a liquid droplet coated with hydrophobic powder. Magnetite particles inside the marble make it magnetic. The marble floats on a carrier liquid that contains aqueous glycerol of various concentrations. A permanent magnet located under the carrier liquid drives the floating marble with the initial velocity. Stopping the magnet abruptly causes the marble to oscillate around its final position for a few seconds. The oscillation was recorded and analysed using customised image processing and evaluation software. The damped harmonic motion model was then applied to the data and tested. Subsequently, critical parameters of the system such as the initial displacement, friction correction factor, the apparent frequency and the spring constant were determined and discussed. The simple experimental set-up and convenient theoretical approach allow us to characterise the marble motion under the influence of a magnet with good accuracy.     

Homogenization-based multiscale crack modelling: From micro-diffusive damage to macro-cracks

The existence of a representative volume element (RVE) for a class of quasi-brittle materials having a random heterogeneous microstructure in tensile, shear and mixed mode loading is demonstrated by deriving traction–separation relations, which are objective with respect to RVE size. A computational homogenization based multiscale crack modelling framework, implemented in an FE² setting, for quasi-brittle solids with complex random microstructure is presented. The objectivity of the macroscopic response to the micro-sample size is shown by numerical simulations. Therefore, a homogenization scheme, which is objective with respect to macroscopic discretization and microscopic sample size, is devised. Numerical examples including a comparison with direct numerical simulation are given to demonstrate the performance of the proposed method.     

Stability radius of implicit dynamic equations with constant coefficients on time scales

This paper deals with the stability radii of implicit dynamic equations on time scales when the structured perturbations act on both the coefficient of derivative and the right-hand side. Formulas of the stability radii are derived as a unification and generalization of some previous results. A special case where the real stability radius and the complex stability radius are equal is studied. Examples are derived to illustrate results.     

BSPNN: boosted subspace probabilistic neural network for email security

In the modern age of Internet connectivity, advanced information systems have accumulated huge volumes of data. Such fast growing, tremendous amount of data, collected and stored in large databases has far exceeded our human ability to comprehend without proper tools. There has been a great deal of research conducted to explore the potential applications of Machine Learning technologies in Security Informatics. This article studies the Network Security Detection problems in which predictive models are constructed to detect network security breaches such as spamming. Due to overwhelming volume of data, complexity and dynamics of computer networks and evolving cyber threats, current security systems suffer limited performance with low detection accuracy and high number of false alarms. To address such performance issues, a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), has been proposed which combines a Radial Basis Function Neural Network with an innovative diversity-based ensemble learning framework. Extensive empirical analyses suggested that BSPNN achieved high detection accuracy with relatively small computational complexity compared with other conventional detection methods.     

Word confidence measure based on frame likelihood score

The automatic recognition of natural or close to natural speech is linked to the problem of detection of “new” or “unknown” words. These are the words or the nonverbal acoustical events that do not belong to the speech recognition system’s vocabulary. In this paper we consider a new method for the estimation of confidence score for words at the output of the recognition system based on a likelihood score of the signal frame. The method and confidence measure could be used, for example, for out-of-vocabulary (OOV) word detection and rejection. The text was submitted by the authors in English. Minh T. Nguyen received the MS degree in computer sciences from Moscow State University in 2004. In 2004 he began postgraduate studies at the Computing Centre, Russian Academy of Sciences. He is working on development and evaluation of confidence measures for speech recognition. Vladimir J. Chuchupal received the MS degree in mathematics from Moscow State Pedagogical Institute in 1976. He received his Candidate of Sciences degree from the Computing Centre, USSR Academy of Sciences, in 1984 completing his dissertation work in noisy speech enhancement methods. Since 1984 he has been with the Speech Recognition subdivision of the Computing Centre, Russian Academy of Sciences, where he works on speech recognition problems. Currently he is the head of the subdivision.     

Equilibrium analysis through separation of user and network behavior

Internet complexity makes reasoning about traffic equilibrium difficult, partly because users react to congestion. This difficulty calls for an analytic technique that is simple, yet have enough details to capture user behavior and flexibly address a broad range of issues.This paper presents such a technique. It treats traffic equilibrium as a balance between an inflow controlled by users, and an outflow controlled by the network (link capacity, congestion avoidance, etc.). This decomposition is demonstrated with a surfing session model, and validated with a traffic trace and NS2 simulations.The technique’s accessibility and breadth are illustrated through an analysis of several issues concerning the location, stability, robustness and dynamics of traffic equilibrium.