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Purpose This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime.Design/methodology/approach This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm.Findings Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime.Research limitations/implications In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only.Practical implications The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations.Social implications The research highly gains impacts toward mobile-based applications.Originality/value A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs. [ABSTRACT FROM AUTHOR]

Publication bias can significantly limit the validity of meta-analysis when trying to draw conclusion about a research question from independent studies. Most research on detection and correction for publication bias in meta-analysis focus mainly on funnel plot-based methodologies or selection models. In this paper, we formulate publication bias as a truncated distribution problem, and propose new parametric solutions. We develop methodologies of estimating the underlying overall effect size and the severity of publication bias. We distinguish the two major situations, in which publication bias may be induced by: (1) small effect size or (2) large p-value. We consider both fixed and random effects models, and derive estimators for the overall mean and the truncation proportion. These estimators will be obtained using maximum likelihood estimation and method of moments under fixed- and random-effects models, respectively. We carried out extensive simulation studies to evaluate the performance of our methodology, and to compare with the non-parametric Trim and Fill method based on funnel plot. We find that our methods based on truncated normal distribution perform consistently well, both in detecting and correcting publication bias under various situations. [ABSTRACT FROM AUTHOR]

A key question for road traffic noise management is whether prediction of human response to road traffic noise could be improved by accounting for noise events instead of, or in addition to, energy equivalent or percentile measures of noise exposure. However, there is a critical prior question: how should noise events in road traffic be measured? Even at moderate traffic flow rates, detecting and counting noise events caused by road traffic is not a trivial exercise, and as yet there is no generally accepted noise event detection algorithm. This paper investigates the performance of a generalized exceedance algorithm for detecting noise events, constructed on the basis of the literature on noise events caused by road traffic. For this purpose, a microscopic traffic simulation model, coupled to an emission model that accounts for distributions of sound power levels of individual vehicles, is used to simulate one-hour time histories of the noise level in the proximity of a roadway, for an exhaustive set of traffic flow/composition and propagation distance conditions in unshielded locations. The validity and reliability of the number of noise events detected by the generalized algorithm in these one-hour time histories is then evaluated for a range of algorithm parameter sets. By discarding parameter sets that do not result in an algorithm that returns valid or reliable counts, and by examining redundancy in the remaining ones, a small number of representative parameter sets is identified, which may prove useful in the construction of event-based indicators supplementary to energy-equivalent measures of road traffic noise. [ABSTRACT FROM AUTHOR]

Centroidal Voronoi tessellation (CVT)-based mesh generation is a very effective technique for creating high-quality Voronoi meshes and their dual Delaunay triangulations that often play a crucial role in applications, including ocean and atmospheric simulations using finite volume schemes. In the next generation climate models, the spacing scales change dramatically across the whole sphere and require ultra-high resolution and smooth transitions from coarse to fine grid regions. Thus fast and robust spherical CVT (SCVT) meshing algorithms become highly desirable. In this paper, we first propose a Lloyd-preconditioned limited-memory BFGS method for constructing SCVTs that is also applicable to the construction of CVTs of general domains. This method is then parallelized based on overlapping domain decomposition, enabling excellent scalability on distributed systems. Results of several computational experiments show that the new method could incur computational time costs one order of magnitude smaller compared with some existing methods for generating large-scale highly variable-resolution meshes, while also providing significant improvements in mesh quality. [ABSTRACT FROM AUTHOR]

The paper presents the theoretical analysis and simulation verification of robust fault diagnosis and adaptive parameter identification for single phase transformerless inverters. The fault diagnosis is composed of two parts, fault detection and fault identification. In the fault detection part, a Luenberger observer is designed to realize the detection of faults. Then, we apply a bank of observers to identify the location of faults. Meanwhile, the fault identification observers based estimation along with a gradient descent algorithm are also used in the parameter identification to estimate the actual values of components in a single phase transformerless inverter. Not only we develop the design methodology for the robust fault diagnosis and adaptive parameter identifier but also we present simulation results. The simulation results show the effectiveness of fault diagnosis and the accurate tracking of changes in component parameters for a wide range. [ABSTRACT FROM AUTHOR]

Time Petri nets (TPNs) have been widely used for modeling discrete event systems such as manufacturing, supply chain, and military systems. However, TPNs still have many drawbacks in some scenarios where an operation or process is associated with probability, and also lack appropriate simulation algorithms for analyzing different types of systems. In this paper, we address these two issues by proposing a class of extended time Petri nets (ETPNs) and presenting an appropriate simulation algorithm. We illustrate and validate our approach using a hypothetic command and control system, which shows that this approach could be a powerful tool for modeling and analyzing discrete event systems. [ABSTRACT FROM AUTHOR]

Oil reservoir history matching is a well-known inverse problem for predicting production by optimizing enormous unknown parameters with numerical simulation. Typically it can be formulated in a Bayesian framework with geological priors. Instead of gradient-based optimization with the possibility of converging to a local minimum, evolutionary algorithms have been introduced to globally find optimal parameters. Due to the high-dimensional parameters, the optimization could become inefficient; therefore, many dimensionality reduction algorithms have been applied in history matching. However, these methods suffer from the linear assumption or the pre-image problem, which could affect the model optimization. In this paper, based on the evolutionary algorithm termed Multi-objective Evolutionary Algorithm Based on Decomposition, which is capable of simultaneously optimizing the parameters with respect to the data of several oil wells, we propose history matching with dimensionality reduction by explicitly utilizing the nonlinear dimensionality reduction model Auto-Encoder to reduce the number of unknown parameters, which can naturally handle the pre-image problem and then improve model performance in terms of precision and complexity. Experimental results based on PUNQ-S3 data verify the efficiency of the newly proposed methods. [ABSTRACT FROM AUTHOR]

Manual and automated blind controls are typically not included in energy and daylight simulation in part because there is no consensus in the research or practice communities about the way users operate manual blinds or override automated blinds. In order for blind use patterns to become part of energy and daylight simulation best practices, the range of annual energy and daylighting impacts associated with blind use must be understood. This paper addresses these aspects by comparing four leading candidates of manually-controlled blinds plus two automated blind control algorithms using a high-rise office building located in Boise, ID. This study revealed that all four current “manual” blind use algorithm choices perform relatively similarly to automated systems, and surprisingly sometimes even more efficiently. LM-83 currently has the lowest average occlusion during regularly occupied hours, followed by Lightswitch-2002, while Blindswith-A and -B have the highest average occlusion. The IES-recommended manual blind algorithm resulted even in lower average blind occlusion and lighting energy consumption than automated systems. Finally, life-cycle cost analysis was calculated. The results show that the cost savings from interior automated shading system are substantial over a 30-year time horizon, when compared with common passive manual blinds ($25 versus $7.6 Net Present Value per SF glazing area). [ABSTRACT FROM AUTHOR]

Accurate simulation of photovoltaic characteristics is now a mandatory obligation before validating an experiment; hence, accurate model and parameters of solar cells are indispensable. This paper presents an improved explicit double-diode model based on the Lambert W function (EDDM-LW), and then compares the fitness and parameter extraction performance. By defining two new parameters ( κ and τ ) to separate the exponential function in double-diode model (DDM) and using the Lambert W function, the explicit expression for I-V characteristics is proposed. In contrast to exiting works, the new parameters can readily be computed by the electrical characteristics of the standard test condition without an implicit characteristic. To verify the accuracy of the proposed model, the fitness difference is first investigated with a solar cell and three different types of solar modules. The results indicate that under the same parameter values, EDDM-LW achieves the lowest root mean square error value and exhibits better fitness in representing the I-V characteristics. In addition, the optimal parameters are extracted by an improved teaching-learning-based optimization algorithm. The experimental results show that the optimal parameter values extracted from EDDM-LW are more accurate than those extracted from DDM. Based on these observations, EDDM-LW can be deemed a useful and practical model for the simulation, evaluation, and optimization of the photovoltaic system. [ABSTRACT FROM AUTHOR]

An important open problem in computational complexity theory is to prove the size of circuits, namely, Boolean circuit lower bounds, necessary to solve explicit problems. We survey learning-theoretic approaches to proofs of Boolean circuit lower bounds in this paper. In particular, we discuss how to prove circuit lower bounds in uniform classes by assuming (or constructing) circuit-learning algorithms in several settings, such as the exact, probably approximately correct (PAC), and statistical query learning models. [ABSTRACT FROM AUTHOR]

The spatial modulation coincidence imaging (SMCI), as a novel kind of microwave coincidence imaging method, is proposed in this paper. The SMCI system provides a new way to produce the time-space independent signal instead of multitransmitter architecture with wideband randomly modulated signal in radar coincidence imaging. Due to some special features, metamaterial plate is utilized as the reflective surface to modulate the incident signal to construct random radiation field. The resolution of SMCI system is analyzed under large viewing angle with two different transmitting signals. Reflective surface is nonuniformly divided to derive the expression of resolution. The analysis results show that the resolution of SMCI system is mainly determined by the size of reflective surface and center frequency, which is similar to the traditional aperture. The SMCI system is low cost and flexible in design. Simultaneously, it can avoid the synchronization problem between subsources. Moreover, the SMCI system can achieve the resolution of space target through single transmitter–single receiver radar system. High-resolution image can be reconstructed since the tests are nonlinear. Finally, a series of simulation experiments is presented based on the nondirect-viewing scene we proposed. Using the algorithm based on a compressed sensing theory, we reconstructed the target image with high resolution. [ABSTRACT FROM AUTHOR]

This paper focuses on the endmember extraction (EE) technique for analyzing hyperspectral images. We first prove that the reconstruction errors (REs) and abundance anomalies (AAs) (abundances that fail to satisfy the abundance constraints) are effective in extracting undetected endmembers. Then, according to the spatial continuity of the endmember objects and differing from noise or outliers with a sparse distribution, the endmembers are assumed to be located at some salient areas in the RE and AA maps. A novel EE algorithm termed saliency-based endmember detection (SED) is proposed, where the visual saliency model is introduced to explore and analyze the spatial information that is contained in the AA and RE maps. Specifically, the AA and RE maps are regarded as the visual inputs, whereas the endmembers are treated as the visual stimuli. In SED, we assume that the pure pixel assumption holds. Based on the characteristics of the human visual system, the proposed method can not only extract endmembers in homogenous areas, but it can also highlight the small targets whose abundances may be spatially varied. In addition, since the spatial information is exploited in the reconstruction, the capability of the endmembers to represent the hyperspectral scene is automatically considered in the process of EE, and the detected endmembers are both accurate and reliable. The experimental results obtained on both simulated and real hyperspectral data confirm the merits and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Modeling has become the most commonly used method in fisheries science, with numerous types of models and approaches available today. The large variety of models and the overwhelming amount of scientific literature published yearly can make it difficult to effectively access and use the output of fisheries modeling publications. In particular, the underlying topic of an article cannot always be detected using keyword searches. As a consequence, identifying the developments and trends within fisheries modeling research can be challenging and time-consuming. This paper utilizes a machine learning algorithm to uncover hidden topics and subtopics from peer-reviewed fisheries modeling publications and identifies temporal trends using 22,236 full-text articles extracted from 13 top-tier fisheries journals from 1990 to 2016. Two modeling topics were discovered: estimation models (a topic that contains the idea of catch, effort, and abundance estimation) and stock assessment models (a topic on the assessment of the current state of a fishery and future projections of fish stock responses and management effects). The underlying modeling subtopics show a change in the research focus of modeling publications over the last 26 years. [ABSTRACT FROM AUTHOR]

In many situations, uncertainty and randomness concurrently occur in a system. Thus this paper presents a new concept for uncertain random variable. Also, a simulation algorithm based on uncertain random variables is presented to approximate the chance distribution using pessimistic value and optimistic value. An example is also given to illustrate how to use the presented simulation algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes and simulates an integrated autonomous vehicle (AV) and public transportation (PT) system. After discussing the attributes of and the interaction among the prospective stakeholders in the system, we identify opportunities for synergy between AVs and the PT system based on Singapore’s organizational structure and demand characteristics. Envisioning an integrated system in the context of the first-mile problem during morning peak hours, we propose to preserve high demand bus routes while repurposing low-demand bus routes and using shared AVs as an alternative. An agent-based supply-side simulation is built to assess the performance of the proposed service in fifty-two scenarios with different fleet sizes and ridesharing preferences. Under a set of assumptions on AV operation costs and dispatching algorithms, the results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic model-based design of a control system and an approach toward a drive selection of a centrifuge motion simulator (CMS). The objective of the presented method is to achieve the desired performance while taking into account the complexity of the control system and the overall device cost. An estimation of a dynamic interaction of the interconnected CMS links motions is performed using the suitable inverse dynamics simulation. An algorithm based on the approximate inverse dynamics model is used within the drive selection method. The model of the actuator's mechanical subsystem includes the effective inertia (inertia reflected on the rotor shaft) calculated from the inverse dynamics model. A centralized control strategy based on a computed torque method is considered and compared to traditional decentralized motion controllers. To obtain an accurate comparison of the suggested control methods through a realistic simulation, structural natural frequencies of the manipulator links are considered, and the actuator capabilities are taken into account. The control system design and simulation methods and the drive selection strategies, presented here for the CMS, are applicable within the general robot manipulator's domain. [ABSTRACT FROM AUTHOR]

An efficient molecular dynamics (MD) algorithm is presented in this paper for biomolecular systems, which incorporates a novel variation on the fast multipole method (FMM) coupled to the reversible reference system propagator algorithm (r-RESPA). A top-down FMM is proposed which calculates multipoles recursively from the top of the box tree instead of from the bottom in Greengard’s original FMM, in an effort to be more efficient for noncubic or nonuniform systems. In addition, the use of noncubic box subdivisions of biomolecular systems is used and discussed. Reversible RESPA based on a Trotter factorization of the Liouville propagator in generating numerical integration schemes is coupled to the top-down FMM and applied to a MD study of proteins in vacuo, and is shown to be able to use a much larger time-step than the standard velocity Verlet method for a comparable level of accuracy. Furthermore, by using the FMM it becomes possible to perform MD simulations for very large biomolecules, since memory and CPU time requirements are now nearly of order of O(N) instead of O(N2). For a protein with 9513 atoms (the photosynthetic reaction center), the efficient MD algorithm leads to 20-fold reduction in CPU time for the Coulomb interaction and approximately 15-fold reduction in total CPU time over the standard velocity Verlet algorithm with a direct evaluation of Coulomb forces. © 1995 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient. [ABSTRACT FROM AUTHOR]

Particle-based methods based on material models using spheres can provide good approximations for many physical phenomena at both the micro and macroscales. The point of departure for the simulations, in general, is a dense arrangement of spherical particles (sphere pack) inside a given container. For generic domains, the generation of a sphere pack may be complex and time-consuming, especially if the pack must comply with a prescribed sphere size distribution and the stability requirements of the simulation. The primary goal of this paper is to present an efficient algorithm that is capable of producing packs with millions of spheres following a statistical sphere size distribution inside complex arbitrary domains. This algorithm uses a new strategy to ensure that the sphere size distribution is preserved even when large particles are rejected in the growing process. The paper also presents numerical results that enable an evaluation of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The self-updating process (SUP) is a clustering algorithm that stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering. It is an iterative process on the sample space and allows for both time-varying and time-invariant operators. By simulations and comparisons, this paper shows that SUP is particularly competitive in clustering (i) data with noise, (ii) data with a large number of clusters, and (iii) unbalanced data. When noise is present in the data, SUP is able to isolate the noise data points while performing clustering simultaneously. The property of the local updating enables SUP to handle data with a large number of clusters and data of various structures. In this paper, we showed that the blurring mean-shift is a static SUP. Therefore, our discussions on the strengths of SUP also apply to the blurring mean-shift. [ABSTRACT FROM AUTHOR]

In the theory of belief functions, the approximation of a basic belief assignment (BBA) is for reducing the high computational cost especially when large number of focal elements are available. In traditional BBA approximation approaches, a focal element’s own characteristics such as the mass assignment and the cardinality, are usually used separately or jointly as criteria for the removal of focal elements. Besides the computational cost, the distance between the original BBA and the approximated one is also concerned, which represents the loss of information in BBA approximation. In this paper, an iterative approximation approach is proposed based on maximizing the closeness, i.e., minimizing the distance between the approximated BBA in current iteration and the BBA obtained in the previous iteration, where one focal element is removed in each iteration. The iteration stops when the desired number of focal elements is reached. The performance evaluation approaches for BBA approximations are also discussed and used to compare and evaluate traditional BBA approximations and the newly proposed one in this paper, which include traditional time-based way, closeness-based way and new proposed ones. Experimental results and related analyses are provided to show the rationality and efficiency of our proposed new BBA approximation. [ABSTRACT FROM AUTHOR]

The implementation of digital image processing required detailed knowledge of both hardware design and hardware description languages. This paper presents an efficient approach for the implementation of real time hardware digital image processing algorithms without requiring detailed knowledge of hardware design and hardware description languages. The purpose of this work is to achieve a real time hardware implementation with higher performance in both size and speed. It focuses on the implementation of an efficient architecture for image processing algorithms like segmentation (threshold) and contrast stretching by using the fewest possible system generator blocks for DSP tool, which integrates itself with the MATLAB based Simulink graphics environment and relieves the user of the textual HDL programming. While Past research has shown that the Image enhancement techniques on FPGA based on the Xilinx System Generator. This study connect between image histogram and Image enhancement techniques depending on the type of enhancement required. This paper describes also the methodology for implementing real-time DSP applications on FPGA and concept of hardware software co-simulation for digital image processing by using the Mathworks model-based design tool Simulink / Xilinx System Generator (XSG). Performances of efficient architectures are implemented on FPGA Virtex5 (XUPV5-LX110T). [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to present and demonstrate the efficiency of utilization of a new approach for estimating parameters of micromechanical gyros (MMG) during their operation. It is shown that the main sources of errors are due to the following three factors: measurement errors of micron deviations of the sensitive element, unknown actual value of difference between the frequencies of primary and secondary oscillations and unknown value of damping constant for the axis of secondary oscillations. The estimation of the last two factors during the operation leads to a significant increase of accuracy of MMG. The solution of the problem is presented in the current paper. Design/methodology/approach – To solve this problem, an original two-level algorithm intended for use in the embedded MMG controller is considered. The identification of the internal MMG parameters is carried out at the first stage. At the second stage, an MMG model is created, based on the results of identification of parameters at the first stage. This model is used as a basis for synthesis of a Kalman filter for estimating the angular velocity. The ways of simplifying identification algorithms and filtering for the purpose of their practical implementation are shown. Findings – This paper presents the research results on the effectiveness of the proposed algorithms. Results of simulation are used to confirm the effectiveness of the proposed algorithm in reducing the effect of changes in the design parameters of MMG on the accuracy of the angular velocity measurements. Practical implications – Proposed algorithms can be used in embedded MMG controllers. Originality/value – This paper presents a new approach for improving the accuracy of MMG by using a built-in algorithm for estimating parameters during system operation. [ABSTRACT FROM AUTHOR]

Efficient data delivery accompanied with low end-to-end delay is important in vehicular ad-hoc networks and optimal route selection is vital to improve these performance metrics. Different routing algorithms are proposed in VANETs. This paper reviews and analyzes these algorithms briefly and based on the analysis, a new opportunistic-based routing algorithm (OSTD) is proposed for urban scenarios. The proposed algorithm severely considers the type of vehicular distribution in the calculation of utility function. This utility function is used to evaluate the routes in the network. The reason of such a severe consideration is expressed and evaluated in the paper. Vehicle's driving path predictability is also used in the algorithm to forward the packet to a more suitable next hop, as vehicular mobility is the reflection of human social activity. Simulation results show that OSTD achieves a higher delivery ratio and lower end-to-end delay and packet loss compared to the other well-known protocols. [ABSTRACT FROM AUTHOR]

The use of quaternions or quaternion error attitude control strategies for unmanned aerial vehicles (UAVs) is commonplace. Quaternion tracking error control is usually presented in rather theoretical works, where the control algorithm is almost exclusively chosen to be a suboptimal one. However, the application of optimal control techniques is usually associated to simplified attitude models frequently aimed at solving real-life problems. The work presented in this paper aims to formally merge the development of a complete theoretical quaternion error model with an optimal control strategy. Moreover, the application of optimal control algorithms to a fully defined quaternion error state-space model and the validation of the same in a real-time experimental setup is the focus of this research. The result is a novel controller named Optimal Quaternion Tracking of Attitude Error Linearization (OQTAL). The paper provides a comprehensive proof of stability, full simulation validation for a planetary landing gravity turn trajectory, and evidence of repeatable experimental work for a real-time quadrotor UAV on a motion capture testbed. OQTAL is compared with proven optimal forms of (PID) proportional-integral-derivative control and linear quadratic regulator control and is shown to have a 10%–20% reduction in error for the experimental setup trajectory tracking trials and an even larger tracking error reduction in the gravity turn simulation trials. Furthermore, for close tracking conditions, OQTAL behaves almost like a linear and time-invariant system, therefore requiring limited computation time for performing the trajectory tracking. [ABSTRACT FROM PUBLISHER]

In this paper, we propose model predictive control methods to reduce the common-mode voltage of three-phase voltage source inverters (VSIs). In the reduced common-mode voltage-model predictive control (RCMV-MPC) methods proposed in this paper, only nonzero voltage vectors are utilized to reduce the common-mode voltage as well as to control the load currents. In addition, two nonzero voltage vectors are selected from the cost function at every sampling period, instead of using only one optimal vector during one sampling period. The two selected nonzero vectors are distributed in one sampling period in such a way as to minimize the error between the measured load current and the reference. Without utilizing the zero vectors, the common-mode voltage controlled by the proposed RCMV-MPC algorithms can be restricted within ±Vdc/6. Furthermore, application of the two nonzero vectors with optimal time sharing between them can yield satisfactory load current ripple performance without using the zero vectors. Thus, the proposed RCMV-MPC methods can reduce the common-mode voltage as well as control the load currents with fast transient response and satisfactory load current ripple performance compared with the conventional model predictive control method. Simulation and experimental results are included to verify the effectiveness of the proposed RCMV-MPC methods. [ABSTRACT FROM AUTHOR]

This paper considers identifying the multiple input single output finite impulse response (MISO-FIR) systems with unknown time delays and orders. Generally, parameters, orders and time delays of an MISO system are separately identified from different algorithms. In this paper, we aim to perform the model identification and time delay estimation simultaneously from a limited number of observations. For an MISO-FIR system with many inputs and unknown input time delays, the corresponding identification model contains a large number of parameters, requiring a great number of observations for identification and leading to a heavy computational burden. Inspired by the compressed sensing (CS) recovery theory, a threshold orthogonal matching pursuit algorithm (TH-OMP) is presented to simultaneously identify the parameters, the orders and the time delays of the MISO-FIR systems. The proposed algorithm requires only a small number of sampled data compared to the conventional identification methods, such as the least squares method. The effectiveness of the proposed algorithm is verified by simulation results. [ABSTRACT FROM AUTHOR]

Abstract: Interactions among competing units are crucial to maintaining biodiversity, and non-hierarchical interactions can promote biodiversity in cyclic competing systems. In the present study, we explore the role of hierarchical interactions, existing ubiquitously in reality, in the co-evolution of a cyclic competing system. In systems composed of cyclic competing species with hierarchy interactions in which one predator species has more than one prey, we find that hierarchy disappears in a rather short evolving time. In the process of co-evolution, a hierarchical competing system tends to transit to a cyclic non-hierarchical competing system described by the rock–paper–scissors game. In other words, the cyclic competing interactions appear to eradicate hierarchy. This conclusion is analyzed by a mean-field approach and is tested by stochastic simulations. [Copyright &y& Elsevier]

Motivated by the work of Dahrouj and Yu in applying the Han-Kobayashi transmission strategy for mitigating the intercell interference in a multi-cell multi-user multiple-input single-output interference network (MISO IN), this paper considers splitting messages into private and common parts in a multi-cell multi-user MIMO IN. Specifically, the covariances of the private messages and common messages are designed to optimize either the sum rate or the minimal rate. The common messages and private messages are decoded in sequence using successive decoding. This paper shows how these difficult optimization problems can be adequately solved by means of d.c. (difference of concave functions) optimization over a simple convex set. Numerical and simulation results also reveal the great advantage of our proposed solutions for various types of INs. In particular, the proposed solutions are shown to outperform the algorithm developed by Dahrouj and Yu for the simpler case of the MISO IN. [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to propose a new four-node membrane element model with bending modification based on the equilibrium principle of element nodal internal forces and bending moments for the application of the one-step algorithm for bus rollover collision. And it can be concluded whether the proposed four-node membrane element model has practical value in engineering application or not. Design/methodology/approach – Based on the equilibrium principle of element nodal internal forces and bending moments, the paper puts forward a four-node membrane element model with bending modification. A case study on the rollover of a typical bus body section is carried out by using the one-step algorithm for bus rollover collision to verify the effectiveness of the proposed element model. Findings – For the simulation of bus rollover collision, the computational accuracy can be guaranteed, meanwhile, the calculated amount is much smaller than the shell element, and computational efficiency is improved significantly. Originality/value – The proposed four-node membrane element model is used for the simulation of bus rollover collision for the first time. It holds the advantage of high computational efficiency of membrane element, and the computational accuracy is improved as well. In conclusion, it has some practical value in engineering application. [ABSTRACT FROM AUTHOR]

The paper presents new results of development of stochastic modelling algorithms for radiation transfer aimed for the study of effects caused by a stochastic radiation model and variation of its parameters. This research was initiated by G. I. Marchuk in order to solve urgent problems of atmospheric optics. Some formulations of corresponding new mathematical problems are given in the paper. [ABSTRACT FROM AUTHOR]

This paper deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

In this paper, an impedance control algorithm based on velocity for capturing two low impact docking mechanisms (LIDMs) is presented. The main idea of this algorithm is to track desired forces when the position errors of two LIDMs are random by designing the relationship between the velocity and contact forces measured by a load sensing ring to achieve low impact docking. In this paper, the governing equation of an impedance controller between the deviation of forces and velocity is derived, and simulations are designed to verify how impedance parameters affect the control characteristics. The performance of the presented control algorithm is validated by using the MATLAB and ADAMS software for capturing simulations. The results of capturing simulations demonstrate that the impedance control algorithm can respond fast and has excellent robustness when the environmental errors are random, and the contact forces and torques satisfy the low impact requirements. [ABSTRACT FROM AUTHOR]

Mixed models: Models allowing for continuous heterogeneity by assuming that value of one or more parameters follow a specified distribution have become increasingly popular. This is known as ‘mixing’ parameters, and it is standard practice by researchers - and the default option in many statistical programs - to base test statistics for mixed models on simulations using asymmetric draws (e.g. Halton draws). Problem 1: Inconsistent LR tests due to asymmetric draws:: This paper shows that when the estimated likelihood functions depend on standard deviations of mixed parameters this practice is very likely to cause misleading test results for the number of draws usually used today. The paper illustrates that increasing the number of draws is a very inefficient solution strategy requiring very large numbers of draws to ensure against misleading test statistics. The main conclusion of this paper is that the problem can be solved completely by using fully antithetic draws, and that using one dimensionally antithetic draws is not enough to solve the problem. Problem 2: Maintaining the correct dimensions when reducing the mixing distribution:: A second point of the paper is that even when fully antithetic draws are used, models reducing the dimension of the mixing distribution must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. Again this is not standard in research or statistical programs. The paper therefore recommends using fully antithetic draws replicating the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood and that this should become the default option in statistical programs. JEL classification: C15; C25. [ABSTRACT FROM AUTHOR]

This paper presents a research that investigates solutions and algorithms for determining the optimum machine layout served by autonomous material handling system, like mobile robot or automated guided vehicle. Unlike previous works which solved the layout problem by optimizing the distance between facilities, in this paper the machine layout is addressed based on optimizing the travel time of the material handling system. The proposed approach can include boundary kinematic constraints of vehicle while optimizing the objective function such as velocity, acceleration, orientation, and trajectory curvature. The nonlinear constrained model is transformed to unconstrained problem using penalty method. Then, a simulated annealing-based algorithm is used to search for the optimum locations of machines among all possible feasible layouts. The simulation results showed that the proposed approach was efficient enough to use in real factories due to including a vehicle path planner integrated in an overall layout design scheme that involves searching of vehicle control parameters. [ABSTRACT FROM AUTHOR]

The Type-II progressive censoring scheme has become very popular for analyzing lifetime data in reliability and survival analysis. However, no published papers address parameter estimation under progressive Type-II censoring for the mixed exponential distribution (MED), which is an important model for reliability and survival analysis. This is the problem that we address in this paper. It is noted that maximum likelihood estimation of unknown parameters cannot be obtained in closed form due to the complicated log-likelihood function. We solve this problem by using the EM algorithm. Finally, we obtain closed form estimates of the model. The proposed methods are illustrated by both some simulations and a case analysis. [ABSTRACT FROM PUBLISHER]

Abstract: Verification of identity based on the analysis of dynamic signatures is an important problem of biometrics. The effectiveness of the verification significantly increases when the dynamic characteristics of signature (e.g. velocity of the pen) are considered. These characteristics are individual for each user and difficult to imitate. The effectiveness of the verification can be further improved by using partitioning. In this paper we propose a new method which uses partitioning. In our method partitions represent areas of high and low speed of signature and high and low pen׳s pressure. All selected partitions are used by our algorithm, but more important in the classification process are these partitions, in which the signatures of the user acquired during training phase are more stable. Moreover, final classification is interpretable. In this paper we present the simulation results of the proposed method for the two databases: SVC2004 and BioSecure Database. [Copyright &y& Elsevier]

In this paper, a novel technique based on magnetic field sensing is proposed for underground power cable detection and inspection. In this technique, the current sources of the underground power cables are reconstructed based on a set of measured magnetic field values at the ground surface level emanated by the electric currents carried by the underground power cables. The stochastic optimization technique developed with an artificial immune system algorithm is applied to realize the reconstruction. The principle of this method was proved and verified experimentally by our laboratory setup. Application of this method was demonstrated on the simulation models of 11- and 132-kV underground power cables. The reconstruction results of the electrical and spatial parameters of the cables match accurately with the actual source parameters of the cables in the models. This paper shows that the proposed method is able to remotely detect the horizontal locations and vertical depths of underground power cables with high accuracy at the ground surface level requiring no prior knowledge about the exact locations of the cables. Thus, it can be potentially used to develop a portable locator for providing a map of the underground electrical cables by simultaneous detection of multiple power lines. This method can also enable engineers to correctly inspect the operation states of the target cables during onsite maintenance. This technique is applicable to various laying conditions and cable configurations (three core or single core) of the underground power cables. In addition, this is an entirely passive method and does not need any signal injection into the cables. [ABSTRACT FROM PUBLISHER]

Thermal asperity (TA) causes a major problem in data detection process. Without the TA detection and correction algorithm, the system performance (even with perfect synchronization) can be unacceptable, depending on how severe the TA effect is. This paper proposes a new method to suppress the TA effects in perpendicular magnetic recording channels. The TA detection is a threshold-based approach, while the TA correction is done by averaging the readback signal and applying a least-squares fitting technique to estimate the TA signal. Then, the corrected readback signal is obtained by subtracting the TA-affected readback signal by the reconstructed TA signal. Results indicate that the proposed method performs better than the existing one in terms of bit-error rate and is robust to changes in the peak TA amplitude. [ABSTRACT FROM AUTHOR]

Synchronization of bio-inspired pulse-coupled oscillators is receiving increased attention due to its wide applications in sensor networks and wireless communications. However, most existing results are obtained in the absence of malicious attacks. Given the distributed and unattended nature of wireless sensor networks, it is imperative to enhance the resilience of pulse based synchronization against malicious attacks. To achieve this goal, we propose a new pulse based interaction mechanism to improve the resilience of pulse based synchronization. We rigorously characterize the condition for mounting stealthy attacks under the proposed pulse based interaction mechanism and prove analytically that synchronization of legitimate oscillators can be achieved in the presence of multiple stealthy attackers even when the initial phases are unrestricted, i.e., randomly distributed in the entire oscillation period. This is in distinct difference from most existing attack-resilient synchronization algorithms (including the seminal paper from L. Lamport and P. M. Melliar-Smith, “Synchronizing Clocks in the Presence of Faults,” J. ACM, vol. 32, no. 1, pp. 52–78, 1985), which require a priori (almost) synchronization among legitimate nodes. Numerical simulations are given to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

This paper applies the Deep Neural Networks to the in-flight parameter identification for detection and characterization of the aircraft icing. General dynamics of the aircraft are firstly presented, ice effects on the dynamics are characterized. Deep Neural Networks (DNNs) including Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are briefly introduced. We propose a “state-image” approach for the pre-processing of the input flight state, then we design a DNN structure which models both local connectivity (using CNN) and temporal characteristics (using RNN) of the flight state. The identified parameters are exported from the DNN output layer directly. To fully evaluate the performance of the DNN-based approach, we conduct simulation tests for different cases which correspond to clean and aircraft icing at different locations (wing, tail, wing and tail) with different severities (moderate, severe). A comparison of the DNN-based approach with a baseline H ∞ -based identification algorithm (state-of-the-art for aircraft icing) is also delivered. Based on the test and comparison results, the DNN-based approach yields more accurate identification performance for more parameters, which shows promising applicability to the in-flight parameter identification problem. [ABSTRACT FROM AUTHOR]

This paper deals with grid integration of photovoltaic systems through single-phase unified power quality conditioner (PV-1UPQC) based on notch filter novel control algorithm for its function such as better phase detection, voltage sag/swell, voltage unbalance, voltage and current harmonics eliminations. A notch filter-based control algorithm includes a phase-locked loop (PLL) mechanism which is responsible to avoid multiple zero crossing at the time of highly distorted grid voltage detection. Notch filter PLL-based control algorithm is applied to both series and shunt inverters of PV-tied UPQC. In addition to normalizing voltage and current perturbations, proposed controller has feature of phase detection and perfect grid synchronization. Synchronous reference d-q frame controller and unit vector control algorithm with conventional PLL is also studied, utilized and evaluated for control of PV-1UPQC. Hardware setup is developed in laboratory using DS1103 dSPACE processor. Performance and efficiency of PV-1UPQC are analyzed through simulation and experimentation for various operating conditions. [ABSTRACT FROM AUTHOR]

This paper studies the complexity of distributed construction of purely additive spanners in the CONGEST model. We describe algorithms for building such spanners in several cases. Because of the need to simultaneously make decisions at far apart locations, the algorithms use additional mechanisms compared to their sequential counterparts. We complement our algorithms with a lower bound on the number of rounds required for computing pairwise spanners. The standard reductions from set-disjointness and equality seem unsuitable for this task because no specific edge needs to be removed from the graph. Instead, to obtain our lower bound, we define a new communication complexity problem that reduces to computing a sparse spanner, and prove a lower bound on its communication complexity. This technique significantly extends the current toolbox used for obtaining lower bounds for the CONGEST model, and we believe it may find additional applications. [ABSTRACT FROM AUTHOR]

This paper considers the problems of unknown input observer (UIO) designs when the so-called observer matching condition (OMC) is not satisfied. Firstly, the system left-invertibility (SLI) concept is investigated in detail and some new criteria are given. Secondly, based on the SLI concept, an augmented output vector formed partly by the original measurable output and partly by the auxiliary output is obtained. By this constructed output vector, the new system is allowed to be transformed into an equivalent linear dynamic system which contains no unknown input, and thus a simple Luenberger observer can be constructed to simultaneously estimate the original states and unknown inputs. Compared with the existing results, the present UIO design can be taken under the single strong detectability condition and the OMC is not needed any more. Finally, the simulations of a single-link flexible joint robotic model and a linearized vertical take-off and landing helicopter model are given to show the effectiveness of our method and its superiority over existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we study the energy efficiency (EE) optimization for simultaneous wireless information and power transfer in multiple-input-multiple-output two-way amplify-and-forward relay networks, where the energy-constrained relay harvests energy from two sources’ signals by power splitting (PS) scheme to forward the information of two sources. Our objective is to jointly design the sources and relay precoding matrices and the PS ratio to maximize the EE of the network, under the transmit power constraints at sources and relay and the minimum spectral efficiency requirement. We transform the nonconvex precoding and PS design problem into a difference of convex programming and propose an efficient iterative algorithm based on the constrained concave convex procedure to obtain a local optimum. To reduce computational complexity, we propose a generalized singular value decomposition based channel diagonalization scheme to simplify the optimization into a joint source and relay power allocation problem. The alternating optimization method is employed to solve this problem and a suboptimal solution to the EE maximization problem is obtained. Simulation results demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]