Mutual information-based selection of optimal spatial–temporal patterns for single-trial EEG-based BCIs

The common spatial pattern (CSP) algorithm is effective in decoding the spatial patterns of the corresponding neuronal activities from electroencephalogram (EEG) signal patterns in brain–computer interfaces (BCIs). However, its effectiveness depends on the subject-specific time segment relative to the visual cue and on the temporal frequency band that is often selected manually or heuristically. This paper presents a novel statistical method to automatically select the optimal subject-specific time segment and temporal frequency band based on the mutual information between the spatial–temporal patterns from the EEG signals and the corresponding neuronal activities. The proposed method comprises four progressive stages: multi-time segment and temporal frequency band-pass filtering, CSP spatial filtering, mutual information-based feature selection and naïve Bayesian classification. The proposed mutual information-based selection of optimal spatial–temporal patterns and its one-versus-rest multi-class extension were evaluated on single-trial EEG from the BCI Competition IV Datasets IIb and IIa respectively. The results showed that the proposed method yielded relatively better session-to-session classification results compared against the best submission.     

Simultaneous determination of the optimal production–inventory and product inspection policies for a deteriorating production system

In this paper, we attempt to find a method for the optimization of production–inventory and product inspection policies for deteriorating production systems. Taking advantage of the nature of a deteriorating production system, a strategy would be not to inspect the first s items of the batch. Therefore, an inspection policy which disregards the first s (DTF-s) items of the batch is proposed. Under the DTF-s policy, we do not inspect the first s produced items but inspect only those items from the (s+1)th till the end of the production run. The objective of this study was the joint determination of the production lot size and the inspection policy s, resulting in a minimization of the expected average cost per unit time. Based on this model, the underlying conditions necessary for the existence of an optimal policy are given. Two commonly used inspection strategies, no inspection and full inspection are discussed. Under both inspection strategies, an optimal production–inventory lot is bounded by the traditional economic quantity. The case of full inspection is shown to be an extension of previously reported results. The option of investing in the process of quality improvement is also discussed. Finally, numerical examples are given to illustrate the method and its advantages in the conclusion.Scope and purposeThis paper considers the relationship between production, inventory and inspection in a deteriorating production system which may transit from the “in-control” state to the “out-of-control” state after a period of operation. Once the transition to the “out-of-control” state has occurred, it is assumed that some percentage of the items produced are defective or of substandard quality. However, in many cases, defects in a defective item can only be identified by an inspection process which carries an inspection cost. Those inspected items which are found to be defective are reworked at some cost before being shipped. On the other hand, defective items which are not inspected will be passed to the customer, incurring a much larger warranty cost. In order to operate such a system economically, tradeoffs among production setup, inventory, inspection and defective cost must be analyzed. Deterioration of the production system is an inherent process in all manufacturing industries. An understanding of the relationship among production, inventory and inspection for such systems will help managers to maintain efficient and economic control of operations.     

Time optimal control of a linear hyperbolic system†

The time optimal control of transmission lines with amplitude constraints on the control is considered as a typical problem involving systems governed by hyperbolic partial differential equations. Using a Laplace transformation formulation to yield a time ‘optimal’ solution, it is shown how this sub-optimal control which is bang-bang develops into an optimal control which is not always at its limiting values—demonstrating the effect which the nature of the differential equation has on the form of the optimal control. A simple physical interpretation of the results is given.     

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algo- rithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assign- ment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate as- sociation cost; hence, much of the procedure time is saved. In the 2-stage asso- ciation algorithm, a large number of false location points are eliminated from can- didate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.     

On the value of the Kullback–Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands

The practical value of a criterion based on statistical information theory is demonstrated for the selection of optimal wavelength and bandwidth of low-cost lighting systems in plant imaging applications. Kullback–Leibler divergence is applied to the problem of spectral band reduction from hyperspectral imaging. The results are illustrated on various plant imaging problems and show similar results to the one obtained with state-of-the-art criteria. A specific interest of the proposed approach is to offer the possibility to integrate technological constraints in the optimization of the spectral bands selected.     

Fuzzy mean–variance–skewness portfolio selection models by interval analysis

In portfolio selection problem, the expected return, risk, liquidity etc. cannot be predicted precisely. The investor generally makes his portfolio decision according to his experience and his economic wisdom. So, deterministic portfolio selection is not a good choice for the investor. In most of the recent works on this problem, fuzzy set theory is widely used to model the problem in uncertain environments. This paper utilizes the concept of interval numbers in fuzzy set theory to extend the classical mean–variance (MV) portfolio selection model into mean–variance–skewness (MVS) model with consideration of transaction cost. In addition, some other criteria like short and long term returns, liquidity, dividends, number of assets in the portfolio and the maximum and minimum allowable capital invested in stocks of any selected company are considered. Three different models have been proposed by defining the future financial market optimistically, pessimistically and in the combined form to model the fuzzy MVS portfolio selection problem. In order to solve the models, fuzzy simulation (FS) and elitist genetic algorithm (EGA) are integrated to produce a more powerful and effective hybrid intelligence algorithm (HIA). Finally, our approaches are tested on a set of stock data from Bombay Stock Exchange (BSE).     

The numerical simulation of periodic solutions for a predator–prey system

In this article, the existence and global stability of periodic solutions for a semi-ratio-dependent predator–prey system with Holling IV functional response and time delays are investigated. Using coincidence degree theory and Lyapunov method, sufficient conditions for the existence and global stability of periodic solutions are obtained. A numerical simulation is given to illustrate the results.     

Local null controllability for a chemotaxis system of parabolic–elliptic type

We consider the controllability of a chemotaxis system of parabolic–elliptic type. By linearizing the nonlinear system into two separate linear equations, we can bypass the obstacle caused by the nonlinear drift term and establish local null controllability of the original nonlinear system. This approach is different from the usual method for dealing with coupled parabolic systems.     

Hybrid position–admittance realization of an adaptive output super-twisting controller for a robotic scalpel

This study proposes the design and implementation of a hybrid robust automatic controller based on the application of a high order sliding mode algorithm for a robotic scalpel prototype (RS). Two fully actuated arms with three degrees of freedom constitute the RS, one arm holds the sample and the second one has the scalpel to exert the cutting task. Each arm is attached to its corresponding cartesian robotic platform. The available measurements are the angular displacements, the linear displacement and the force vector describing the interaction between the scalpel and the biological sample. A hybrid position–admittance controller implements an output-based adaptive distributed super-twisting algorithm to mobilize the RS. A high order sliding mode observer estimates the unknown angular and linear velocities that were used in the hybrid controller. Once the end-effector of each arm reaches the desired cutting position, the designed controller switches to the admittance controller to avoid damaging the surrounding tissue. Numerical simulations show the advantages of the suggested controller in comparison with classical algorithms. The hybrid sliding mode admittance controller has been successfully evaluated on an self-constructed platform. The experimental results show a precise cut and efficient mobilization of the RS compared to other classical controllers such as proportional-differentiator, proportional-integral and first order sliding mode controllers.     

TeMAS–a multi-agent system for temporally rich domains

In this paper, we present the model and simulator of a multi-agent system (MAS) for temporally rich domains. The theoretical foundations of the model include a knowledge representation scheme based on an original modification of Petri nets, called Petri nets with time tokens (PNTTs), as well as temporal reasoning based on the extension of Allen's temporal logic. The proposed MAS, called TeMAS, has a hierarchical structure, consisting of different levels, where each level contains clusters of agents. A paradigm of hierarchically organized blackboards is used for the communication among agents, clusters, as well as levels. We describe an object-oriented implementation of a program simulator of TeMAS and give an example of the use of the simulator for interpretation of events in a dynamic scene. Slobodan Ribarić received the B.S. degree in electronics, the M.S. degree in automatics, and the Ph.D. degree in electrical engineering from the Faculty of Electrical Engineering, Ljubljana, Slovenia, in 1974, 1976, and 1982, respectively. He is currently a Full Professor at the Department of Electronics, Microelectronics, Computer and Intelligent Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia. His research interests include pattern recognition, artificial intelligence, biometrics, computer architecture and robot vision. He has published more than 150 papers on these topics and authored four books (Microprocessor Architecture, The Fifth Computer Generation Architecture, Advanced Microprocessor Architectures, CISC and RISC Computer Architecture) and co-authored one book (An Introduction to Pattern Recognition). Dr. Ribarić is a Member of the IEEE, ISAI and IAPR. Tomislav Hrkać received the B.S. degree in computer science from the Faculty of Electrical Engineering and Computing at the University of Zagreb, Croatia, in 1999. Since October 2000, he has been a Researcher with the Department of Electronics, Microelectronics, Computer and Intelligent Systems at the same faculty. He received the M.S. degree in 2004. As a co-author, he published several papers in international conference proceedings and a paper in a reviewed scientific journal. He is a Student Member of IEEE.     

COOPT — a software package for optimal control of large-scale differential–algebraic equation systems

This paper describes the functionality and implementation of COOPT. This software package implements a direct method with modified multiple shooting type techniques for solving optimal control problems of large-scale differential–algebraic equation (DAE) systems. The basic approach in COOPT is to divide the original time interval into multiple shooting intervals, with the DAEs solved numerically on the subintervals at each optimization iteration. Continuity constraints are imposed across the subintervals. The resulting optimization problem is solved by sparse sequential quadratic programming (SQP) methods. Partial derivative matrices needed for the optimization are generated by DAE sensitivity software. The sensitivity equations to be solved are generated via automatic differentiation.COOPT has been successfully used in solving optimal control problems arising from a wide variety of applications, such as chemical vapor deposition of superconducting thin films, spacecraft trajectory design and contingency/recovery problems, and computation of cell traction forces in tissue engineering.     

Bifurcation diagrams for the moments of a kinetic type model of keloid–immune system competition

The mathematical modelling of the keloid disease triggered by a virus has been recently investigated by one of the authors, Bianca (2011) [5], where it was shown that the model is able to depict the emerging behaviours which occur during the keloid formation.This paper deals with further numerical investigations of that model related to the bifurcation analysis of the measurable macroscopic variables associated to each functional subsystem. It is shown that there exists a critical value of a bifurcation parameter separating situations where the immune system controls the keloid formation from those where malignant effects are not contrasted.     

Dynamic selection of generative–discriminative ensembles for off-line signature verification

In practice, each writer provides only a limited number of signature samples to design a signature verification (SV) system. Hybrid generative–discriminative ensembles of classifiers (EoCs) are proposed in this paper to design an off-line SV system from few samples, where the classifier selection process is performed dynamically. To design the generative stage, multiple discrete left-to-right Hidden Markov Models (HMMs) are trained using a different number of states and codebook sizes, allowing the system to learn signatures at different levels of perception. To design the discriminative stage, HMM likelihoods are measured for each training signature, and assembled into feature vectors that are used to train a diversified pool of two-class classifiers through a specialized Random Subspace Method. During verification, a new dynamic selection strategy based on the K-nearest-oracles (KNORA) algorithm and on Output Profiles selects the most accurate EoCs to classify a given input signature. This SV system is suitable for incremental learning of new signature samples. Experiments performed with real-world signature data (composed of genuine samples, and random, simple and skilled forgeries) indicate that the proposed dynamic selection strategy can significantly reduce the overall error rates, with respect to other EoCs formed using well-known dynamic and static selection strategies. Moreover, the performance of the SV system proposed in this paper is significantly greater than or comparable to that of related systems found in the literature.     

An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy

Manufacturing enterprises are collaborating among each other in manufacturing service ecosystems (MSE) with the objective to compose and provision numerous product–services (P–S) on the market. However, many paramount processes outset much before the actual composition, like the strategy planning of those P–S. Such decisions are usually full of ambiguities with complex sets of decisional possibilities, which are extremely hard to encompass even within a decision support system. Thus, the aim of this article is to undergird the development of an effective decision support system (DSS) for solving the challenge of planning a P–S strategy within a MSE, as well to present and apply a relative novel fuzzy inference technique, in order to build the DSS in question. This is achieved by first designing the logical data model that conceptualizes the context of planning a P–S strategy within a MSE, secondly by designing the actual business intelligence (BI) sets of rules and thirdly to build a DSS and test its data. As the input data needed to plan a strategy are often intangible, without a clear delineation among classes (e.g. “Market_1 is more competitive than Market_2”), with more than just binary values that can also overlap among each other and can be expressed using human language, a fuzzy based inference system is used to build the BI rules set. The DSS provides answers to three central uncertainties in P–S strategy planning expressed in the article as performance questions.     

A note on the existence and optimal control for mixed Volterra–Fredholm-type integrodifferential dispersion system of third order

The optimal control problem consists of a performance index subject to a set of differential equations that describes the path of the control and state variables. The main aim of this article is to prove the existence and uniqueness of a mild solution, optimal control, and time-optimal control of a mixed Volterra–Fredholm-type third-order dispersion system. By applying the strongly continuous semigroup theory and the Banach fixed-point theorem, we prove the existence and uniqueness of the considered system. The optimal control results are proved by using Mazur's lemma, Gronwall's inequality, and the minimizing sequence technique. The discussion on the time-optimal control of the third-order dispersion system is also presented.     

New optimal controller tuning method for an AVR system using a simplified Ant Colony Optimization with a new constrained Nelder–Mead algorithm

In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.     

Computation of sub–optimal linear controls for non–linear stochastic systems†

A successive approximation technique based on statistical linearization is developed which permits the design of sub–optimal linear controls for non–linear time–invariant processes perturbed by Gaussian random disturbances. The application of the technique to a chemical process example is given, and the validity of the results is confirmed by simulation.