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Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Online social networks have become increasingly popular. One of the interesting applications is the friend locator, in which the application server informs a user through a mobile device if some of his/her listed friends are close enough in terms of geographical locations. However, in such services, it is challenging to protect the privacy of the individual users. Previous solutions for the friend locator do not guarantee a high level of privacy and do not maintain efficiency. In this paper, we propose a dual-resolution system structure to guarantee both strong privacy and efficiency. Additionally, we use the polygon decomposition method to achieve both accuracy and flexibility. To be more specific, in the coarse resolution level, each regular mobile user uploads his/her encrypted coarse location information to a central server periodically for comparisons. If a regular mobile user is found to be in the same grid block as an active mobile user, then the friend locator procedure with a higher resolution level will be conducted. Finally, through numerical analysis and simulations, we show that the proposed system design and algorithm can achieve high privacy, efficiency, accuracy, and flexibility. [ABSTRACT FROM PUBLISHER]

Accurate attenuation correction in PET/CT relies upon anatomically matched PET and CT scans, however, attenuation mismatch artifacts are often present due to continuous respiration during both the PET and CT scans. Acquiring respiratory gated CT and PET scans in which each frame of the 4DCT is matched with a frame in a respiratory gated PET scan is one strategy for obtaining temporally correlated PET and CT images. In this paper we propose a cross population model as an alternative to 4DCT for estimating the deformation of attenuating objects within the thorax. Principal components analysis was used to extract components of motion from the 4DCT scans of a cohort of 10 patients and the components were then fitted to the frames from a respiratory gated PET scan. The cross population model produced an artificial, yet realistic patient specific deformation field for each frame even though no 4DCT was acquired in conjunction with the gated PET scan. The deformation field was then applied to the static spiral CT image to produce CT frames matching each PET frame. The first 5 components of motion accounted for 95% of the movement in regions where there was sufficient PET camera sensitivity to detect motion. We propose this approach as the basis for a robust method of transforming a static spiral CT into spatially matched attenuation maps. We investigate improvements in image contrast as a result of using our method for attenuation correction. [ABSTRACT FROM PUBLISHER]

Existing population-based Stochastic Search Algorithms (SSAs) are too time-consuming to solve dynamic optimal power flow (OPF). The solution proposed in this paper is to accelerate SSAs with memory. Two memory schemes, the similarity retrieval scheme and the mean-based immigrants scheme, are proposed and applied together to the Differential Evolution and Particle Swarm Optimizer, which are two representatives of SSAs. Experiments are conducted on modified IEEE 30-bus and IEEE 118-bus systems with changing load buses and the objective of minimizing real power transmission loss. The results show that the proposed schemes significantly improve the performance of the two existing algorithms, and that SSAs could be practical for tracking optima of dynamic OPF. [ABSTRACT FROM AUTHOR]

Total Bregman divergences are a recent tweak of ordinary Bregman divergences originally motivated by applications that required invariance by rotations. They have displayed superior results compared with ordinary Bregman divergences on several clustering, computer vision, medical imaging, and machine learning tasks. These preliminary results raise two important problems. First, report a complete characterization of the left and right population minimizers for this class of total Bregman divergences. Second, characterize a principled superset of total and ordinary Bregman divergences with good clustering properties, from which one could tailor the choice of a divergence to a particular application. In this paper, we provide and study one such superset with interesting geometric features, that we call conformal divergences, and focus on their left and right population minimizers. Our results are obtained in a recently coined $(u, v)$ -geometric structure that is a generalization of the dually flat affine connections in information geometry. We characterize both analytically and geometrically the population minimizers. We prove that conformal divergences (resp. total Bregman divergences) are essentially exhaustive for their left (resp. right) population minimizers. We further report new results and extend previous results on the robustness to outliers of the left and right population minimizers, and discuss the role of the $(u, v)$ -geometric structure in clustering. Additional results are also given. [ABSTRACT FROM PUBLISHER]

In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets. [ABSTRACT FROM PUBLISHER]

Thulium laser operation on the 3H4–3H6 transition at 823 nm has been demonstrated in bulk Tm:YAG by quasi-continuous-wave pumping with a fiber-coupled diode array at 782 nm. The prior demonstrations of this laser transition have been limited primarily to fluorozirconate glass host materials in waveguide configurations. Population trapping has been identified as an important consideration in the design and operation of lasers on this transition. The use of a crystalline host should enable both high peak and average power operation, and the basic principles discussed in this paper should be extensible to other crystalline hosts and room-temperature operation. [ABSTRACT FROM PUBLISHER]

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods. [ABSTRACT FROM AUTHOR]

In this paper, a general, robust, and automatic parameter extraction of nonlinear compact models is presented. The parameter extraction is based on multiobjective optimization using evolutionary algorithms, which allow fitting of several highly nonlinear and highly conflicting characteristics simultaneously. Two multiobjective evolutionary algorithms which have been proved to be robust for a wide range of multiobjective problems [1]–[3], the nondominated sorting genetic algorithm II and the multiobjective covariance matrix adaptation evolution strategy, are used in the parameter extraction of a novel power diode compact model based on the lumped charge technique. The performance of the algorithms is assessed using a systematic statistical approach. Good agreement between the simulated and measured characteristics of the power diode shows the accuracy of the used compact model and the efficiency and effectiveness of the proposed multiobjective optimization scheme. [ABSTRACT FROM AUTHOR]

In 2009 Tausch developed a simple equation to predict the cumulative probability of a single uncorrectable (double bit) error occurring within Hamming protected memory as the number of random upsets increase. This paper furthers that work by: 1) developing a simplified equation that predicts the number of failing addresses vs. random upsets in Hamming protected memories, 2) showing the range over which this simplified equation matches more complicated, 3) proving the validity of this equation through radiation testing, 4) showing how this equation can be used to improve test techniques in various simulator environments and 5) showing how this equation can be used to better analyze test results and to understand and predict the behavior of Hamming memories in radiation environments. An approach will be described to better calculate upset cross-sections of Hamming protected SRAMs in heavy ion environments. [ABSTRACT FROM AUTHOR]

Z-pinches, formed by passing large currents through wires or gas puffs, are the most powerful and energetic laboratory sources of X-rays. As higher current generators become available, Z-pinches incorporate increasingly larger masses of plasma and are nearly always optically thick to some or most of the photons that they produce. Understanding and modeling the transport of this radiation is an important element in attaining an overall picture of the physics of Z-pinches and in optimizing their properties for applications. This paper focuses on the aspects of radiation transport that are most relevant to the Z-pinch environment. The topics covered include: theory of radiation transport and its strengths and limitations, sources of opacity in Z-pinches, requirements for local thermodynamic equilibrium in the presence of opacity, transition to the blackbody limit, and Z-pinch experiments that demonstrate the effects of radiation transport. [ABSTRACT FROM PUBLISHER]

Electrical Engineering (EE) and Computer Engineering (CpE) programs have similar curricula, but different demographics and student outcomes. This paper extends earlier longitudinal studies to a larger and more diverse dataset with 90\,000 first-time-in-college and 26\,000 transfer students who majored in engineering at USA institutions, including students who started in first-year engineering programs, those switching majors, and those transferring from other institutions. Black men and women and Asian men in engineering are strongly attracted to EE when they start in college. Black students and Asian and Hispanic men are attracted to CpE more than other engineering disciplines, but at lower rates than EE. Asian students have the highest graduation rates in EE. EE students are much more likely to graduate than CpE students. Compared to other engineering disciplines, CpE graduation rates are low for women of all races/ethnicities and Black men. Both EE and CpE lose many of those starting the programs, but switchers and transfers compensate for some of the loss. Considering Asian students and White men, switching to EE accounts for the high attrition rate from CpE, but attrition in other populations cannot be explained so easily. Trajectories of student enrollment differ by race/ethnicity. The approach used here could serve as a model for other fields studying their own demographic distributions. [ABSTRACT FROM AUTHOR]

A novel optimization-enabled electromagnetic transient simulation approach is proposed for calculating the resistor setting of a multistage impulse generator for insulation impulse testing of high-voltage transformers. This approach enables test engineers to overcome most of the major challenges attributed to the use of conventional trial-and-error methods for determining resistor settings, including the excessive time consumption and the potential damage to the test transformer due to a greater number of trial tests. The proposed method uses the frequency response of the test transformer to synthesize a circuit model for it. The test setup, including the test transformer and the impulse generator, is simulated using an electromagnetic-transients-type simulator. A genetic-algorithm-based approach is used to optimize the setting of the impulse generator. Optimized resistor values are then used in impulse testing of a three-phase power transformer. Different cases of impulse generator resistor arrangements are studied in this paper and simulated waveforms are compared with those obtained from measurements. [ABSTRACT FROM AUTHOR]

In this paper, a fast power/ground (P/G) pin assignment method for large-scale high-pin-count ball grid array (BGA) packages using static template is proposed. The static template is generated using genetic algorithm. An algorithm is developed to remove the boundary problem of the static template so that its copies can be directly merged into a large-scale BGA package. The selection of the size of static template is demonstrated. A visual demonstration method is presented to better evaluate the quality of the assignment of signal and P/G pins by human eyes. Using static template method, the pin assignment of a $50\times 50$ BGA package with blocks of core, I/Os and differential pairs can be generated in a few tens of seconds automatically. [ABSTRACT FROM PUBLISHER]

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances. [ABSTRACT FROM PUBLISHER]

Given that heart disease can cause abnormal motion dynamics over the cardiac cycle, understanding and quantifying cardiac motion can provide insight for clinicians to aid with diagnosis, therapy planning, and determining prognosis. The goal of this paper is to extract population-specific motion patterns from 3D displacements in order to identify the mean motion in a population, and to describe pathology-specific motion patterns in terms of the spatial and temporal components. Since there are common motion patterns observed in patients with the same condition, extracting these can lead towards a better understanding of the disease. Quantifying cardiac motion at a population level is not a simple task since images can vary widely in terms of image quality, size, resolution, and pose. To overcome this, we analyze the parameters obtained from a cardiac-specific Polyaffine motion-tracking algorithm, which are aligned both spatially and temporally to a common reference space. Once all parameters are aligned, different subjects can be compared and analyzed in the space of Polyaffine transformations by projecting the transformations to a reduced order subspace in which dominant motion patterns in each population can be extracted. Using tensor decomposition, the spatial and temporal aspects can be decoupled in order to study the components individually. The proposed method was validated on healthy volunteers and Tetralogy of Fallot patients according to known spatial and temporal behavior for each population. A key advantage of this method is the ability to regenerate motion sequences from the models, which can be visualized in terms of the full motion. [ABSTRACT FROM AUTHOR]

The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting; this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values; ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization; and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry. [ABSTRACT FROM AUTHOR]

Modern wind turbines vary greatly in their drive train configurations. With the variety of options available, it can be difficult to determine which type is most suitable for on and offshore applications. A large percentage of modern drive trains consist of either doubly fed induction generators with partially rated converters or permanent magnet generators with fully rated converters. These configurations are the focus of this empirical reliability comparison. The turbine population for this analysis contains over 1800 doubly fed induction generators, partially rated converter wind turbines, and 400 permanent magnet generator fully rated converter wind turbines. The turbines analyzed are identical except for their drive train configurations and are modern MW scale turbines making this population the largest and most modern encountered in the literature review. Results of the analysis include overall failure rates, failure rates per operational year, failure rates per failure mode, and failure rates per failure cost category for the two drive train configurations. These results contribute toward deciding on the most suitable turbine type for a particular site, as well as toward cost of energy comparisons for different drive train types. A comparison between failure rates from this analysis and failure rates from similar analyses is also shown in this paper. [ABSTRACT FROM AUTHOR]

The development of efficient parallel algorithms based on mixed mode programming represents one of the most popular lines of research in current bioinformatics. By exploiting hardware resources at inter-node/intra-node level, we can address grand computational challenges which involve the optimization of multiple objective functions simultaneously. In this sense, the inference of evolutionary trees represents one of the most difficult NP-hard problems in the field. Tackling such a problem requires efficient parallel designs to take advantage of the characteristics of modern multicore clusters. In this paper, we aim to solve the phylogenetic inference problem by applying MPI/OpenMP schemes to two multiobjective metaheuristics: fast non-dominated sorting genetic algorithm and multiobjective firefly algorithm. In order to assess the performance achieved by these proposals under different system and problem sizes, we have conducted experiments on six real nucleotide data sets according to a statistical methodology. Our parallel and multiobjective metrics point out the relevance of combining hybrid programming and novel bioinspired designs with regard to other parallel and biological approaches from the literature. [ABSTRACT FROM PUBLISHER]

The discussion around different network architectures, namely high power/high tower (HPHT) versus low power/low tower (LTLP) is currently a hot topic in different international fora. This paper presents the results of a study to compare coverage, number of sites, and network deployment/maintenance costs associated to three different network configurations (HPHT only, LTLP only, and mixed HPHT + LTLP) for rooftop, mobile, and portable indoor/outdoor digital terrestrial television service delivery. The analysis has been based on a practical network planning scenario. The results have been obtained with coverage simulations in the Basque Country, a region in northern Spain currently served with 64 transmitters of a traditional broadcast network. The results conclude that a LPLT network would require 3.3 more centers for providing the same coverage as the current HPHT network at a cost three times higher. Also, it has been observed that the current HPHT network fails to give portable and mobile coverage numbers close to real-service targets in urban areas. In those cases, the approach with lower number of sites and cost is a mixed HPHT + LTLP network, where a set of LPLT transmitters complement the coverage provided by the HPHT network. [ABSTRACT FROM AUTHOR]

The populations of industrialized countries will decline over the coming decades. To ensure that we have efficient access networks in the coming age of depopulation, it is important to consider the reorganization of central offices over time. The cost for operating and maintaining central offices can be reduced by efficiently closing central offices. However, dynamic facility location models that have been developed largely focused on the problem of optimally expanding a network over time to meet increasing demand. The practical relevance of the dynamic models is limited because the complexity and the required amount of data are much greater than with static models. This paper proposes a dynamic access network reorganization model for the depopulation age. Although the proposed model is a dynamic model, it is simple because it assumes that the only option will be to close central offices. The model first searches for a set of optimal solutions for each planning period as a capacitated facility location problem. Then, it selects the optimal combination of optimal solutions that can minimize the total number of open offices with the limitation of network reorganization. The validity of the proposed model was evaluated with computer simulations. It was confirmed that although the proposed model is a dynamic model it is simple and practical. [ABSTRACT FROM PUBLISHER]

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001. [ABSTRACT FROM PUBLISHER]

This paper discovers fundamental principles of the backoff process that governs the performance of IEEE 802.11. A simplistic principle founded upon regular variation theory is that the backoff time has a truncated Pareto-type tail distribution with an exponent of (\log \gamma \!)/\log m ( \boldsymbol m is the multiplicative factor and \gamma is the collision probability). This reveals that the per-node backoff process is heavy-tailed in the strict sense for \gamma > 1/m^2 , and paves the way for the following unifying result. The state-of-the-art theory on the superposition of the heavy-tailed processes is applied to establish a dichotomy exhibited by the aggregate backoff process, putting emphasis on the importance of time-scales on which we view the backoff processes. While the aggregation on normal time-scales leads to a Poisson process, it is approximated by a new limiting process possessing long-range dependence (LRD) on coarse time-scales. This dichotomy turns out to be instrumental in formulating short-term fairness, extending existing formulas to arbitrary population, and to elucidate the absence of LRD in practical situations. A refined wavelet analysis is conducted to strengthen this argument. [ABSTRACT FROM AUTHOR]

An improved nondominated sorting genetic algorithm–II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved. [ABSTRACT FROM AUTHOR]

Lightning is considered one of the main causes of faults in overhead distribution networks. Direct strokes usually lead to flashovers due to the insulation levels that are used. Induced overvoltages caused by indirect lightning are usually lower and can be efficiently reduced by metal–oxide surge arresters. Hence, its associated flashover rate can be reduced. In this paper, a heuristic method is proposed to optimize the number of surge arresters as well as their locations. The method presented is based on genetic algorithms and an economic approach is taken into account by means of evaluating the cost of insulation flashover. [ABSTRACT FROM AUTHOR]

Stability is an important constraint in power system operation and the transient stability constrained optimal power flow (OPF) has always received considerable attention in recent years. In this paper, the defects of the existing models and algorithms around this topic are firstly analyzed, on the basis of which, a multi-objective optimization method is proposed. The basic idea of the proposed method is to model transient stability as an objective function rather than an inequality constraint and consider classic transient stability constrained OPF (TSCOPF) as a tradeoff procedure using Pareto ideology. Second, a master-slave parallel elitist non-dominated sorting genetic algorithm II is used to solve the proposed multi-objective optimization problem, the parallel algorithm shows an excellent acceleration effect and provides a set of Pareto optimal solutions for decision makers to select. An innovative weight assigning technique based on fuzzy membership variance is also introduced for a more scientific and objective optimal solution decision. Case study results demonstrate the proposed multi-objective method has many advantages, compared with traditional TSCOPF methods. [ABSTRACT FROM PUBLISHER]

In order to optimize their assets, electrical power distribution companies seek out various techniques to improve system operation and its different variables, like voltage levels, active power losses and so on. A few of the tools applied to meet these objectives include reactive power compensation, use of voltage regulators, and network reconfiguration. One target most companies aim at is power loss minimization; one available tool to do this is distribution system reconfiguration. To reconfigure a network in radial power distribution systems means to alter the topology changing the state of a set of switches normally closed (NC) and normally opened (NO). In restructured electrical power business, a company must also consider obtaining a topology as reliable as possible. In most cases, reducing the power losses is no guarantee of improved reliability. This paper presents a multiobjective algorithm to reduce power losses while improving the reliability index using the artificial immune systems technique applying graph theory considerations to improve computational performance and Pareto dominance rules. The proposed algorithm is tested on a sample system, 14-bus test system, and on Administración Nacional de Electricidad (ANDE) real feeder (CBO-01 23-kV feeder). [ABSTRACT FROM PUBLISHER]

Harmony search (HS) algorithm is an evolutionary optimization algorithm developed in an analogy with an improvisation process where musicians try to polish their pitches to obtain a better harmony. In this paper, a modified HS (MHS) algorithm is adapted to multi-objective optimization using external archiving, ranking with crowding distance, and control parameters tuning based on Ricker map to solve a transformer design optimization (TDO) problem with two competing objectives. Simulations applied to a TDO problem demonstrate the effectiveness of the proposed multi-objective MHS algorithm. Results indicate that, compared with other multi-objective HS algorithm, in terms of output quality, the proposed MHS is able to find competitive solutions with a good tradeoff between the design objectives. [ABSTRACT FROM PUBLISHER]

Degradation data may be collected from a population with heterogeneous subpopulations. This paper contributes to the development of a new statistical modeling and computation method for analyzing heterogeneous degradation data. We adopt the random-coefficient degradation path approach, and propose a hierarchical Bayesian degradation model. To account for the heterogeneity, we model the unit-to-unit variability via random parameters in a Gaussian mixture model. We developed a computationally convenient algorithm that combines Gibbs sampling for parameter estimation as well as failure-time distribution prediction and Akaike information criterion for determining the number of subpopulations. A numerical example is used to illustrate the advantages of the proposed methodology over existing methods that do not explicitly consider heterogeneity in the degradation data. [ABSTRACT FROM PUBLISHER]

Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks. [ABSTRACT FROM AUTHOR]

The next-generation wireless communication networks are envisioned to offer many multimedia services such as audio/video clips, mobile TV, web browsing, video conference, etc., with diverse quality of service (QoS) requirements. Multicasting/broadcasting has been recognized as an emerging and enabling technique for such multimedia transmissions over wireless networks. By employing multicast transmission, a base station can transmit the same data content to several groups of users requiring flexible QoS. In this paper, we investigate subchannel and power allocation problems in an OFDM-based wireless multicast system. With the goal of maximizing the total system capacity, a low complexity, novel genetic algorithm aided efficient subchannel allocation scheme taking into account the constraints of total available bandwidth, proportional data rate fairness and total transmit power budget at the base station is proposed. The subchannel allocation is then followed by optimal power allocation. Unlike previous work where either no fairness or fairness based on number of subchannels allocated to the different groups was considered, we incorporate a set of system parameters in the problem formulation such that the ratio of the group data rates strictly follow the set of system parameters after resource allocation. Simulation results show that the proposed method obtains higher sum capacities while maintaining proportional data rate fairness among different multicast groups, without being awfully complex. [ABSTRACT FROM PUBLISHER]

Several studies have shown the importance of realistic micromobility and macromobility modeling in vehicular ad hoc networks (VANETs). At the macroscopic level, most researchers focus on a detailed and accurate description of road topology. However, a key factor often overlooked is a spatiotemporal configuration of vehicular traffic. This factor greatly influences network topology and topology variations. Indeed, vehicle distribution has high spatial and temporal diversity that depends on the time of the day and place attraction. This diversity impacts the quality of radio links and, thus, network topology. In this paper, we propose a new mobility model for vehicular networks in urban and suburban environments. To reproduce realistic network topology and topological changes, the model uses real static and dynamic data on the environment. The data concern particularly the topographic and socioeconomic characteristics of infrastructures and the spatiotemporal population distribution. We validate our model by comparing the simulation results with real data derived from individual displacement survey. We also present statistics on network topology, which show the interest of taking into account the spatiotemporal mobility variation. [ABSTRACT FROM PUBLISHER]

We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the \ell 2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable selection performance of these procedures have not been established, even though model interpretation is of fundamental importance in scientific data analysis. This paper bridges the gap by providing sharp sufficient conditions for consistent variable selection using the sparse discriminant analysis. Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size $n$ , dimensionality $p$ , and sparsity level $s$ in the high-dimensional setting. Sufficient conditions are complemented by the necessary information theoretic limits on the variable selection problem in the context of high-dimensional discriminant analysis. Exploiting a numerical equivalence result, our method also establish the optimal results for the ROAD estimator and the sparse optimal scoring estimator. Furthermore, we analyze an exhaustive search procedure, whose performance serves as a benchmark, and show that it is variable selection consistent under weaker conditions. Extensive simulations demonstrating the sharpness of the bounds are also provided. [ABSTRACT FROM AUTHOR]

Brain storm optimization (BSO) is a novel population-based swarm intelligence algorithm based on the human brainstorming process. BSO has been proven feasible and has been successfully applied to benchmark problems in the electromagnetic field. In this paper, inspired by the mechanism of quantum theories, a novel variant of BSO algorithm, called quantum-behaved BSO (QBSO), is proposed to solve an optimization problem modeled for Loney’s solenoid problem. The new mechanism improves the diversity of population and also utilizes the global information to generate the new individual. Simulation results show that QBSO has better ability to jump out of local optima and perform better compared with the basic BSO. [ABSTRACT FROM AUTHOR]

Reliability analysis of power systems has been attracting increasing attention. Regulatory agencies establish reliability standards that, if infringed, result in costly fines for the utility suppliers. A special concern pertains to the distribution networks on which most failures occur. The allocation of switches is a possible strategy to improve reliability, by allowing network reconfiguration to isolate contingencies and restore power to dark areas. This paper proposes an optimization methodology to allocate switches on radially operated distribution networks. The solution framework considers sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The approach minimizes the costs of allocation and energy not supplied, under reliability and flow capacity constraints. The solution framework is based on memetic algorithm concepts with a structured population. Case studies with a large network and real-world scenarios were used to evaluate the methodology. The results indicate that significant cost reductions can be achieved using the proposed solutions. [ABSTRACT FROM PUBLISHER]

Cluster identification of ultra-wideband (UWB) propagations is of great significance to the parameter extraction and measurement analysis of channel modeling. In this paper, we address this challenging problem within a promising biological processing framework. Both the two large-scale characteristics of each multipath component, i.e., the decaying amplitude and the time of arrivals, are organically combined and fully explored in the suggested cluster identification algorithm. Each resolvable trajectory component is first projected onto a 2-D amplitude–time plane and further modeled as a virtual ant-agent, which can move around in this 2-D workspace with a preference to the high local-environment similarity. By establishing a subtle population similarity and specifying an efficient position adaptation strategy, cluster identifications can be realized by the biological ant colony clustering procedure. Owing to the population-based intelligence and the involved positive-feedback collaboration during the agents evolution, the suggested algorithm can efficiently identify the involved multiple clusters in a completely automatic manner. Experiments on UWB channels validate the proposed method. The practical parameter configuration is analyzed, and a group of numerical performance metrics is derived. As demonstrated by numerical investigations, multiple clusters involved in UWB channel impulse responses can be accurately extracted. [ABSTRACT FROM AUTHOR]

The past decade has seen a dramatic increase in the amount of data captured and made available to scientists for research. This increase amplifies the difficulty scientists face in finding the data most relevant to their information needs. In prior work, we hypothesized that Information Retrieval-style ranked search can be applied to data sets to help a scientist discover the most relevant data amongst the thousands of data sets in many formats, much like text-based ranked search helps users make sense of the vast number of Internet documents. To test this hypothesis, we explored the use of ranked search for scientific data using an existing multi-terabyte observational archive as our test-bed. In this paper, we investigate whether the concept of varying relevance, and therefore ranked search, applies to numeric data—that is, are data sets are enough like documents for Information Retrieval techniques and evaluation measures to apply? We present a user study that demonstrates that data set similarity resonates with users as a basis for relevance and, therefore, for ranked search. We evaluate a prototype implementation of ranked search over data sets with a second user study and demonstrate that ranked search improves a scientist's ability to find needed data. [ABSTRACT FROM PUBLISHER]

In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms. [ABSTRACT FROM PUBLISHER]

Backtracking search algorithm (BSA) is a novel evolutionary algorithm (EA) for solving real-valued numerical optimization problems. In this paper, an adaptive BSA (ABSA) is proposed to solve the optimization problem of an induction magnetometer (IM). In the adaptive algorithm, the probabilities of crossover and mutation are varied depending on the fitness values of the solutions to refine the convergence performance. The proposed ABSA will also be compared with basic BSA and other widely used EA algorithms. Simulation results show that ABSA is better able to solving the IM optimization problems. [ABSTRACT FROM AUTHOR]

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated. [ABSTRACT FROM AUTHOR]

The automatic design of synchronous reluctance (SyR) machines is considered in this paper by means of finite-element analysis and multiobjective optimization algorithms (MOOAs). The research focuses on the design of the rotor geometry, which is the key aspect of the SyR machine design. In particular, the performance of three popular MOOAs is analyzed and compared in terms of quality of the final design and computational time. A procedure to minimize the computational burden of the optimized design process is introduced and applied to a three-layer and a five layer rotor. Two prototypes experimentally demonstrate the feasibility of the design procedure. [ABSTRACT FROM AUTHOR]