Unification of independent and sequential procedures for decentralized controller design

This paper is concerned with decentralized control problems. There are two typical procedures to design decentralized controllers, ‘independent’ and ‘sequential’ design procedures. As the concepts and the techniques in these two approaches are too different from each other, there have been no attempts to unify these approaches. This paper proposes an iterative independent design procedure for decentralized control systems, which is a unified approach to these conventional approaches.     

Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes

We propose two approximate dynamic programming (ADP)-based strategies for control of nonlinear processes using input-output data. In the first strategy, which we term ‘J-learning,’ one builds an empirical nonlinear model using closed-loop test data and performs dynamic programming with it to derive an improved control policy. In the second strategy, called ‘Q-learning,’ one tries to learn an improved control policy in a model-less manner. Compared to the conventional model predictive control approach, the new approach offers some practical advantages in using nonlinear empirical models for process control. Besides the potential reduction in the on-line computational burden, it offers a convenient way to control the degree of model extrapolation in the calculation of optimal control moves. One major difficulty associated with using an empirical model within the multi-step predictive control setting is that the model can be excessively extrapolated into regions of the state space where identification data were scarce or nonexistent, leading to performances far worse than predicted by the model. Within the proposed ADP-based strategies, this problem is handled by imposing a penalty term designed on the basis of local data distribution. A CSTR example is provided to illustrate the proposed approaches.     

A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models

This article is about testing the equality of several normal means when the variances are unknown and arbitrary, i.e., the set up of the one-way ANOVA. Even though several tests are available in the literature, none of them perform well in terms of Type I error probability under various sample size and parameter combinations. In fact, Type I errors can be highly inflated for some of the commonly used tests; a serious issue that appears to have been overlooked. We propose a parametric bootstrap (PB) approach and compare it with three existing location-scale invariant tests—the Welch test, the James test and the generalized F (GF) test. The Type I error rates and powers of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test is the best among the four tests with respect to Type I error rates. The PB test performs very satisfactorily even for small samples while the Welch test and the GF test exhibit poor Type I error properties when the sample sizes are small and/or the number of means to be compared is moderate to large. The James test performs better than the Welch test and the GF test. It is also noted that the same tests can be used to test the significance of the random effect variance component in a one-way random model under unequal error variances. Such models are widely used to analyze data from inter-laboratory studies. The methods are illustrated using some examples.     

Application of a new multi-agent Hybrid Co-evolution based Particle Swarm Optimisation methodology in ship design

In this paper, a multiple objective ‘Hybrid Co-evolution based Particle Swarm Optimisation’ methodology (HCPSO) is proposed. This methodology is able to handle multiple objective optimisation problems in the area of ship design, where the simultaneous optimisation of several conflicting objectives is considered. The proposed method is a hybrid technique that merges the features of co-evolution and Nash equilibrium with a ε-disturbance technique to eliminate the stagnation. The method also offers a way to identify an efficient set of Pareto (conflicting) designs and to select a preferred solution amongst these designs. The combination of co-evolution approach and Nash-optima contributes to HCPSO by utilising faster search and evolution characteristics. The design search is performed within a multi-agent design framework to facilitate distributed synchronous cooperation. The most widely used test functions from the formal literature of multiple objectives optimisation are utilised to test the HCPSO. In addition, a real case study, the internal subdivision problem of a ROPAX vessel, is provided to exemplify the applicability of the developed method.     

A class of aggregation functions encompassing two-dimensional OWA operators

In this paper we prove that, under suitable conditions, Atanassov’s K_α operators, which act on intervals, provide the same numerical results as OWA operators of dimension two. On one hand, this allows us to recover OWA operators from K_α operators. On the other hand, by analyzing the properties of Atanassov’s operators, we can generalize them. In this way, we introduce a class of aggregation functions - the generalized Atanassov operators - that, in particular, include two-dimensional OWA operators. We investigate under which conditions these generalized Atanassov operators satisfy some properties usually required for aggregation functions, such as bisymmetry, strictness, monotonicity, etc. We also show that if we apply these aggregation functions to interval-valued fuzzy sets, we obtain an ordered family of fuzzy sets.     

Obtaining more information from conjoint experiments by best-worst choices

Conjoint choice experiments elicit individuals’ preferences for the attributes of a good by asking respondents to indicate repeatedly their most preferred alternative in a number of choice sets. However, conjoint choice experiments can be used to obtain more information than that revealed by the individuals’ single best choices. A way to obtain extra information is by means of best-worst choice experiments in which respondents are asked to indicate not only their most preferred alternative but also their least preferred one in each choice set. To create D-optimal designs for these experiments, an expression for the Fisher information matrix for the maximum-difference model is developed. Semi-Bayesian D-optimal best-worst choice designs are derived and compared with commonly used design strategies in marketing in terms of the D-optimality criterion and prediction accuracy. Finally, it is shown that best-worst choice experiments yield considerably more information than choice experiments.     

Incremental learning of bidirectional principal components for face recognition

Recently, bidirectional principal component analysis (BDPCA) has been proven to be an efficient tool for pattern recognition and image analysis. Encouraging experimental results have been reported and discussed in the literature. However, BDPCA has to be performed in batch mode, it means that all the training data has to be ready before we calculate the projection matrices. If there are additional samples need to be incorporated into an existing system, it has to be retrained with the whole updated training set. Moreover, the scatter matrices of BDPCA are formulated as the sum of K (samples size) image covariance matrices, this leads to the incremental learning directly on the scatters impossible, thus it presents new challenge for on-line training.In fact, there are two major reasons for building incremental algorithms. The first reason is that in some cases, when the number of training images is very large, the batch algorithm cannot process the entire training set due to large computational or space requirements of the batch approach. The second reason is when the learning algorithm is supposed to operate in a dynamical settings, that all the training data is not given in advance, and new training samples may arrive at any time, and they have to be processed in an on-line manner. Through matricizations of third-order tensor, we successfully transfer the eigenvalue decomposition problem of scatters to the singular value decomposition (SVD) of corresponding unfolded matrices, followed by complexity and memory analysis on the novel algorithm. A theoretical clue for selecting suitable dimensionality parameters without losing classification information is also presented in this paper. Experimental results on FERET and CMU PIE (pose, illumination, and expression) databases show that the IBDPCA algorithm gives a close approximation to the BDPCA method, but using less time.     

Sharing secrets in stego images with authentication

Recently, Lin and Tsai and Yang et al. proposed secret image sharing schemes with steganography and authentication, which divide a secret image into the shadows and embed the produced shadows in the cover images to form the stego images so as to be transmitted to authorized recipients securely. In addition, these schemes also involve their authentication mechanisms to verify the integrity of the stego images such that the secret image can be restored correctly. Unfortunately, these schemes still have two shortcomings. One is that the weak authentication cannot well protect the integrity of the stego images, so the secret image cannot be recovered completely. The other shortcoming is that the visual quality of the stego images is not good enough. To overcome such drawbacks, in this paper, we propose a novel secret image sharing scheme combining steganography and authentication based on Chinese remainder theorem (CRT). The proposed scheme not only improves the authentication ability but also enhances the visual quality of the stego images. The experimental results show that the proposed scheme is superior to the previously existing methods.     

Unified approach to coefficient-based regularized regression

In this paper, we consider the coefficient-based regularized least-squares regression problem with the l^q-regularizer (1≤q≤2) and data dependent hypothesis spaces. Algorithms in data dependent hypothesis spaces perform well with the property of flexibility. We conduct a unified error analysis by a stepping stone technique. An empirical covering number technique is also employed in our study to improve sample error. Comparing with existing results, we make a few improvements: First, we obtain a significantly sharper learning rate that can be arbitrarily close to O(m⁻¹) under reasonable conditions, which is regarded as the best learning rate in learning theory. Second, our results cover the case q=1, which is novel. Finally, our results hold under very general conditions.     

Smooth semiparametric and nonparametric Bayesian estimation of bivariate densities from bivariate histogram data

Penalized B-splines combined with the composite link model are used to estimate a bivariate density from a histogram with wide bins. The goals are multiple: they include the visualization of the dependence between the two variates, but also the estimation of derived quantities like Kendall’s tau, conditional moments and quantiles. Two strategies are proposed: the first one is semiparametric with flexible margins modeled using B-splines and a parametric copula for the dependence structure; the second one is nonparametric and is based on Kronecker products of the marginal B-spline bases. Frequentist and Bayesian estimations are described. A large simulation study quantifies the performances of the two methods under different dependence structures and for varying strengths of dependence, sample sizes and amounts of grouping. It suggests that Schwarz’s BIC is a good tool for classifying the competing models. The density estimates are used to evaluate conditional quantiles in two applications in social and in medical sciences.     

Extended rate, more GFUN

We present a software package that guesses formulae for sequences of, for example, rational numbers or rational functions, given the first few terms. We implement an algorithm due to Bernhard Beckermann and George Labahn, together with some enhancements to render our package efficient. Thus we extend and complement Christian Krattenthaler’s program Rate.m, the parts concerned with guessing of Bruno Salvy and Paul Zimmermann’s GFUN, the univariate case of Manuel Kauers’ Guess.m and Manuel Kauers’ and Christoph Koutschan’s qGeneratingFunctions.m.     

Online computation with advice

We consider a model for online computation in which the online algorithm receives, together with each request, some information regarding the future, referred to as advice. The advice is a function, defined by the online algorithm, of the whole request sequence. The advice provided to the online algorithm may allow an improvement in its performance, compared to the classical model of complete lack of information regarding the future. We are interested in the impact of such advice on the competitive ratio, and in particular, in the relation between the size b of the advice, measured in terms of bits of information per request, and the (improved) competitive ratio. Since b=0 corresponds to the classical online model, and b=⌈log∣A∣⌉, where A is the algorithm’s action space, corresponds to the optimal (offline) one, our model spans a spectrum of settings ranging from classical online algorithms to offline ones.In this paper we propose the above model and illustrate its applicability by considering two of the most extensively studied online problems, namely, metrical task systems (MTS) and the k-server problem. For MTS we establish tight (up to constant factors) upper and lower bounds on the competitive ratio of deterministic and randomized online algorithms with advice for any choice of 1≤b≤Θ(logn), where n is the number of states in the system: we prove that any randomized online algorithm for MTS has competitive ratio Ω(log(n)/b) and we present a deterministic online algorithm for MTS with competitive ratio O(log(n)/b). For the k-server problem we construct a deterministic online algorithm for general metric spaces with competitive ratio k^O(1/b) for any choice of Θ(1)≤b≤logk.     

Choquet integral with respect to ?ukasiewicz filters, and its modifications

In many decision problems a set of actions is evaluated with respect to a set of points of view, called criteria. This paper follows two aims - first to compare the so-called level-dependent Choquet integral introduced recently by Greco et al. with another transformation of Choquet integral, proposed by Havranová and Kalina. The other aim of this paper is to look for an appropriate utility function in a given setting. We illustrate our approach on a practical example, utilizing the level-dependent Choquet integral.     

Equitable and equitable list colorings of graphs

A proper k-vertex coloring of a graph is an equitable k-coloring if the sizes of the color classes differ by at most 1. A graph G is equitably k-choosable if, for any k-uniform list assignment L, G is L-colorable and each color appears on at most vertices. We prove in this paper that outerplane graphs are equitably k-choosable whenever k≥Δ, where Δ is the maximum degree. Moreover, we discuss equitable colorings of some d-degenerate graphs.     

Arithmetic operators in interval-valued fuzzy set theory

We introduce the addition, subtraction, multiplication and division on L^I, where L^I is the underlying lattice of both interval-valued fuzzy set theory [R. Sambuc, Fonctions Φ-floues. Application à l’aide au diagnostic en pathologie thyroidienne, Ph.D. Thesis, Université de Marseille, France, 1975] and intuitionistic fuzzy set theory [K.T. Atanassov, Intuitionistic fuzzy sets, 1983, VII ITKR’s Session, Sofia (deposed in Central Sci. Technical Library of Bulg. Acad. of Sci., 1697/84) (in Bulgarian)]. We investigate some algebraic properties of these operators. We show that using these operators the pseudo-t-representable extensions of the ?ukasiewicz t-norm and the product t-norm on the unit interval to L^I and some related operators can be written in a similar way as their counterparts on ([0,1],?).     

Negative selection algorithms on strings with efficient training and linear-time classification

A string-based negative selection algorithm is an immune-inspired classifier that infers a partitioning of a string space Σ^? into “normal” and “anomalous” partitions from a training set S containing only samples from the “normal” partition. The algorithm generates a set of patterns, called “detectors”, to cover regions of the string space containing none of the training samples. Strings that match at least one of these detectors are then classified as “anomalous”. A major problem with existing implementations of this approach is that the detector generating step needs exponential time in the worst case. Here we show that for the two most widely used kinds of detectors, the r-chunk and r-contiguous detectors based on partial matching to substrings of length r, negative selection can be implemented more efficiently by avoiding generating detectors altogether: for each detector type, training set S⊆Σ^? and parameter r≤? one can construct an automaton whose acceptance behaviour is equivalent to the algorithm’s classification outcome. The resulting runtime is O(|S|?r|Σ|) for constructing the automaton in the training phase and O(?) for classifying a string.     

A model reference approach to safe controller changes in iterative identification and control

A controller change from a current controller which stabilises the plant to a new controller, designed on the basis of an approximate model of the plant and with guaranteed bounds on the stability properties of the true closed loop, is called a safe controller change. In this paper, we present a model reference approach to the determination of safe controller changes on the basis of approximate closed loop models of the plant and robust stability results in the ν-gap.     

Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.