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Probability theory is well adapted to handle aleatory uncertainties resulting from the variability of failure phenomena. Recently, several uncertainty theories such as belief function theory were introduced in reliability assessments to handle epistemic uncertainties resulting from the lack of knowledge or insufficient data. In this paper, we propose some methods to construct belief functions of reliability parameters of components from statistical data about reliability. The proposed methods consider the parametric estimation of reliability parameters. [ABSTRACT FROM PUBLISHER]

In this paper, the plasma generated by the electrical explosion of a single aluminum wire in high vacuum is investigated, through self-emission spectrum in the range of near UV to visible wavelength (347.5–477.6 nm) captured by imaging spectrometer with the duration of the spectrometer time frame of 3 ns. Electron temperature and density of the plasma are determined through the spectrum using Boltzmann Plot and Stark Broadening Effect of isolated lines of Al III. The results show that the electron temperature in the plasma is in the range of 3.7–8.6 eV and the electron density has the order of magnitude about 10^18~cm^-3. Population inversion of Al II is found according to the intensity of the isolated lines in the spectra. [ABSTRACT FROM AUTHOR]

Recommending TV content to groups of viewers is best carried out when relevant information such as the demographics of the group is available. However, it can be difficult and time consuming to extract information for every user in the group. This paper shows how an audio analysis of the age and gender of a group of users watching the TV can be used for recommending a sequence of N short TV content items for the group. First, a state of the art audio-based classifier determines the age and gender of each user in an M-user group and creates a group profile. A genetic recommender algorithm then selects for each user in the profile, a single personalized multimedia item for viewing. When the number of items to be presented is different to the number of viewers in the group, i.e. M = N, a novel adaptation algorithm is proposed that first converts the M-user group profile to an N-slot content profile, thus ensuring that items are proportionally allocated to users with respect to their demographic categorization. The proposed system is compared to an ideal system where the group demographics are provided explicitly. Results using real speaker utterances show that, in spite of the inaccuracies of state-of-the-art age-and-gender detection systems, the proposed system has a significant ability to predict an item with a matching age and gender category. User studies were conducted where subjects were asked to rate a sequence of advertisements, where half of the advertisements were randomly selected, and the other half were selected using the audio-derived demographics. The recommended advertisements received a significant higher median rating of 7.75, as opposed to 4.25 for the randomly selected advertisements. [ABSTRACT FROM PUBLISHER]

The design of klystrons has long been a manual process guided by experience. However, with well-defined specifications and sufficiently rapid simulation methods, it is a good candidate process for automatic optimization techniques. In this paper, such a technique is evaluated and refined using klystron specific techniques, leading to several designs (with different tradeoffs between efficiency and size) each of a structure comparable with the SLAC B-factory klystrons. The most efficient of which, while only 1% more efficient, is 17.1% shorter. [ABSTRACT FROM AUTHOR]

In this paper, a Pareto-archived estimation-of-distribution algorithm (PAEDA) is presented for the multiobjective resource-constrained project scheduling problem with makespan and resource investment criteria. First, by combining the activity list and the resource list, an encoding scheme named activity-resource list is presented. Second, a novel hybrid probability model is designed to predict the most promising activity permutation and resource capacities. Third, a new sampling and updating mechanism for the probability model is developed to track the area with promising solutions. In addition, a Pareto archive is used to store the nondominated solutions that have been explored, and another archive is used to store the solutions for updating the probability model. The evolution process of the PAEDA is visualized showing the most promising area of the search space is tracked. Extensive numerical testing results then demonstrate that the PAEDA outperforms the existing methods. [ABSTRACT FROM PUBLISHER]

This paper studies the sample complexity of searching over multiple populations. We consider a large number of populations, each corresponding to either distribution P0 or P1. The goal of the search problem studied here is to find one population corresponding to distribution P1 with as few samples as possible. The main contribution is to quantify the number of samples needed to correctly find one such population. We consider two general approaches: nonadaptive sampling methods, which sample each population a predetermined number of times until a population following P1 is found, and adaptive sampling methods, which employ sequential sampling schemes for each population. We first derive a lower bound on the number of samples required by any sampling scheme. We then consider an adaptive procedure consisting of a series of sequential probability ratio tests, and show it comes within a constant factor of the lower bound. We give explicit expressions for this constant when samples of the populations follow Gaussian and Bernoulli distributions. An alternative adaptive scheme is discussed which does not require full knowledge of P1, and comes within a constant factor of the optimal scheme. For comparison, a lower bound on the sampling requirements of any nonadaptive scheme is presented. [ABSTRACT FROM AUTHOR]

The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. [ABSTRACT FROM AUTHOR]

This paper considers minimizing makespan for a blocking flowshop scheduling problem, which has important application in a variety of modern industries. A constructive heuristic is first presented to generate a good initial solution by combining the existing profile fitting (PF) approach and Nawaz–Enscore–Ham (NEH) heuristic in an effective way. Then, a memetic algorithm (MA) is proposed including effective techniques like a heuristic-based initialization, a path-relinking-based crossover operator, a referenced local search, and a procedure to control the diversity of the population. Afterwards, the parameters and operators of the proposed MA are calibrated by means of a design of experiments approach. Finally, a comparative evaluation is carried out with the best performing algorithms presented for the blocking flowshop with makespan criterion, and with the adaptations of other state-of-the-art MAs originally designed for the regular flowshop problem. The results show that the proposed MA performs much better than the other algorithms. Ultimately, 75 out of 120 upper bounds provided by Ribas [“An iterated greedy algorithm for the flowshop scheduling with blocking”, OMEGA, vol. 39, pp. 293–301, 2011.] for Taillard flowshop benchmarks that are considered as blocking flowshop instances are further improved by the presented MA. [ABSTRACT FROM AUTHOR]

Morphological and attribute profiles have been proven to be effective tools to fuse spectral and spatial information for classification of remote sensing data. A wide range of filters (i.e., number of levels in the profiles) is usually necessary in order to properly model the spatial information in a remote sensing scene. A dense sampling of the values of the parameters of the filters generates profiles that have both a very large dimensionality (leading to the Hughes phenomenon in classification) and a high redundancy. In this paper, a novel iterative technique based on genetic algorithms (GAs) is proposed to automatically optimize the selection of the optimal features from the profiles. The selection of the filtered images that compose the profile is performed by dividing them into three classes corresponding to high, medium, and low importance. We propose to measure the importance (modeled in terms of discriminative power in the classification task) using a random forest classifier, which provides a rank for each feature with its model. Only the set of images associated with the highest importance is selected, i.e., preserved for classification. The proposed technique is applied to the features labeled with medium importance, whereas the images with the lowest importance are removed from the profile. This method is employed to classify three hyperspectral data sets achieving significantly high classification accuracy values. A parallel computing implementation has been developed in order to significantly reduce the time required for the run of the GAs. [ABSTRACT FROM AUTHOR]

Hypergraph clustering refers to the process of extracting maximally coherent groups from a set of objects using high-order (rather than pairwise) similarities. Traditional approaches to this problem are based on the idea of partitioning the input data into a predetermined number of classes, thereby obtaining the clusters as a by-product of the partitioning process. In this paper, we offer a radically different view of the problem. In contrast to the classical approach, we attempt to provide a meaningful formalization of the very notion of a cluster and we show that game theory offers an attractive and unexplored perspective that serves our purpose well. To this end, we formulate the hypergraph clustering problem in terms of a noncooperative multiplayer “clustering game,” and show that a natural notion of a cluster turns out to be equivalent to a classical (evolutionary) game-theoretic equilibrium concept. We prove that the problem of finding the equilibria of our clustering game is equivalent to locally optimizing a polynomial function over the standard simplex, and we provide a discrete-time high-order replicator dynamics to perform this optimization, based on the Baum-Eagon inequality. Experiments over synthetic as well as real-world data are presented which show the superiority of our approach over the state of the art. [ABSTRACT FROM AUTHOR]

In wireless multicast systems, cooperative multicast, in which successful users help to relay received packets to unsuccessful users, has been shown to be effective in combating channel fading and improving system performance. However, this mechanism requires the users' voluntary contributions, which cannot be guaranteed since users are selfish and only care about their own performance. Furthermore, users may have heterogeneous costs (which are their private information) to forward packets, and they may lie about their costs if cheating can improve their utilities. To address these problems, in this paper, we model the interaction among users in the wireless multicast system as a multiseller multibuyer payment-based game, where users pay to receive relay service and get paid if they forward packets to others. A simplified case with homogeneous users that have the same cost to forward packets is investigated first. Then, for the case with heterogeneous users, to encourage users to tell their true costs, we use the second-price sealed-bid auction, which is a truth-telling auction, since bidding the true cost is a weakly dominant strategy. To analyze the multiseller multibuyer payment-based game, we observe that under different selected prices, the game can converge to different equilibria, resulting in different user free-riding probabilities and system throughput. We also study the price selection problem and derive the optimal price that maximizes the system throughput. Simulation results show the effectiveness of our proposed mechanism. [ABSTRACT FROM PUBLISHER]

Layered queueing networks are a useful tool for the performance modeling and prediction of software systems that exhibit complex characteristics such as multiple tiers of service, fork/join interactions, and asynchronous communication. These features generally result in nonproduct form behavior for which particularly efficient approximations based on mean value analysis (MVA) have been devised. This paper reconsiders the accuracy of such techniques by providing an interpretation of layered queueing networks as fluid models. Mediated by an automatic translation into a stochastic process algebra, PEPA, a network is associated with a set of ordinary differential equations (ODEs) whose size is insensitive to the population levels in the system under consideration. A substantial numerical assessment demonstrates that this approach significantly improves the quality of the approximation for typical performance indices such as utilization, throughput, and response time. Furthermore, backed by established theoretical results of asymptotic convergence, the error trend shows monotonic decrease with larger population sizes—a behavior which is found to be in sharp contrast with that of approximate mean value analysis, which instead tends to increase. [ABSTRACT FROM PUBLISHER]

Utilizing battery storage systems (BSSs) can reduce the intermittent output of PV generation systems (PVGSs) and make them dispatchable. The aim of this paper is to design an optimal charging/discharging scheduling for BSSs such that the line loss of distribution systems interconnected with sizeable PVGSs can be minimized. A mathematical model for BSSs which can be used to simulate the charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging procedures and the state of charge (SOC) is proposed first. The minimum line loss problem considering the intermittent output of PVGSs and the scheduling of BSSs is then formulated based on the BSS mathematical model. The optimal charging/discharging scheduling of BSSs can then be obtained by a genetic algorithm (GA) based method. Test results demonstrate the validity of the proposed mathematical model and optimal charging/discharging scheduling for BSSs. [ABSTRACT FROM PUBLISHER]

In this paper, impedance spectroscopy, gene expression programming (GEP), and genetic algorithms are combined to perform sensor characterization. The process presented is useful when there is no knowledge of the sensor equivalent circuit, and a set of impedance responses can be obtained for different measurand values. These responses are used by the algorithm to determine a suitable equivalent circuit and choose a circuit component that describes the measurand values. From this component, interpolation is used to infer the measurand value from the measured frequency responses. Improvements on the application of GEP to impedance characterization are presented. The method is validated through its application to numerical results of a humidity sensor and measurement results of a viscosity sensor. [ABSTRACT FROM AUTHOR]

This paper considers a system consisting of n linearly ordered multistate components. Each component can have different states: from complete failure, up to perfect functioning. A performance rate is associated with each state. The system fails if in each of at least m consecutive overlapping groups of r consecutive components (windows) the sum of the performance rates of components belonging to the group is lower than a minimum allowable level. It is shown that, in the case of different components, the system reliability depends on their arrangement. The optimal arrangement problem is formulated, and a numerical tool for solving this problem is suggested. The tool uses an extended universal moment generating function technique for system reliability evaluation, and a genetic algorithm for optimization. Examples of system reliability optimization are presented. [ABSTRACT FROM PUBLISHER]

Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain tradeoff clustering solutions. A number of cluster validity index measures are available in the literature. However, none of the measures can perform equally well in all kinds of datasets. Depending on the dataset properties and its inherent clustering structure, different cluster validity measures perform differently. Therefore, it is important to find the best set of validity indices that should be optimized simultaneously to obtain good clustering results. In this paper, a novel interactive genetic algorithm-based multiobjective approach is proposed that simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The proposed method interactively takes the input from the human decision maker (DM) during execution and adaptively learns from that input to obtain the final set of validity measures along with the final clustering result. The algorithm is applied for clustering real-life benchmark gene expression datasets and its performance is compared with that of several other existing clustering algorithms to demonstrate its effectiveness. The results indicate that the proposed method outperforms the other existing algorithms for all the datasets considered here. [ABSTRACT FROM PUBLISHER]

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously. [ABSTRACT FROM PUBLISHER]