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A nonparametric procedure to estimate the conditional probability that a nonstationary geostatistical process exceeds a certain threshold value is proposed. The method consists of a bootstrap algorithm that combines conditional simulation techniques with nonparametric estimations of the trend and the variability. The nonparametric local linear estimator, considering a bandwidth matrix selected by a method that takes the spatial dependence into account, is used to estimate the trend. The variability is modeled estimating the conditional variance and the variogram from corrected residuals to avoid the biasses. The proposed method allows to obtain estimates of the conditional exceedance risk in non-observed spatial locations. The performance of the approach is analyzed by simulation and illustrated with the application to a real data set of precipitations in the USA.Supplementary materials accompanying this paper appear on-line. [ABSTRACT FROM AUTHOR]

To enhance the convergence performance of the affine projection sign subband adaptive filter (AP-SSAF) algorithm under highly correlated inputs, the novel AP-SSAF (NAP-SSAF) algorithm was proposed through fully utilizing the decorrelation characteristic of SAF, which obtains quicker convergence behavior than the AP-SSAF algorithm. However, under unknown system identification, the estimation accuracy of the NAP-SSAF algorithm may unavoidably decrease when the input of the adaptive filter is contaminated by noise. To alleviate this issue, in this paper, we propose the bias-compensated NAP-SSAF (BC-NAP-SSAF) algorithm to alleviate the estimated bias through employing unbiasedness criterion. Benefiting from the unbiased criterion, the proposed BC-NAP-SSAF algorithm achieves an asymptotically unbiased estimation. Moreover, the convergence bounds of the proposed BC-NAP-SSAF algorithm in the mean and mean-square senses are provided to ensure stability, which are obtained by resorting to Price's theorem along with some reasonable assumptions. The effectiveness of the derived theoretical convergence bounds is also corroborated. Finally, numerical simulations under system identification and acoustic echo cancellation applications demonstrate that the proposed BC-NAP-SSAF algorithm outperforms other algorithms in terms of estimation accuracy and tracking capability for noisy input under impulsive noise environment. [ABSTRACT FROM AUTHOR]

Digital image technology is penetrating into various fields of people's life, and it has been very mature and can effectively store and transmit data. Moreover, there are still various researches on image recognition, the core of this technology. The algorithm is mainly based on computer technology to obtain the target image for different scene categories, thus completely replacing the traditional classification form. Because of the limitations of traditional identification technology, there are some problems in the actual use. It does not depend on the prior knowledge requirements and can carry out complex feature space division. In this paper, an image recognition computer system is established by introducing an improved neural network algorithm. The algorithm is designed and tested, and the results show it has lower image recognition error rate. Subsequently, this research result is applied to the actual scene for testing. The test results show that the improved neural network optimization algorithm can make the extracted features more accurately expressed in the image processing, which is more effective than the traditional algorithm. [ABSTRACT FROM AUTHOR]

The service performance and life of the aero-engine are directly affected by the blade machining accuracy. In the end milling of the flexible blade, the profile tolerance would usually be violated by the deflection errors induced by excessive static deformations under the action of milling force. In order to predict the blade deflection errors, this paper presents an iterative algorithm considering the coupling relationship between milling force and elastic deformation based on the milling finite element simulation model. Then a novel multipoint rod-shaped auxiliary support technology is proposed to suppress the blade deflection errors. Taking the influence of layout parameters on blade deflection errors into account, the layout of auxiliary support is optimized by a genetic algorithm. To verify the effectiveness of the proposed method, an auxiliary support mechanism is designed and manufactured, and the deflection errors suppression experiment is implemented. The measured results show that the prediction error of blade maximum deflection is 17.6%. Furthermore, the blade average deflection error on two measured curves reduces from 45.88 μm to 24.39 μm with the support of the designed mechanism. Thus, this auxiliary support mechanism and layout optimization method can achieve higher accuracy in blade milling. [ABSTRACT FROM AUTHOR]

The paper proposes a numerical criterion to evaluate zoning quality for a given number of classes. The originality of the criterion is to simultaneously quantify how zones are heterogeneous on the whole field under study and how neighbouring zones are similar. This approach allows comparison between maps either with different zones or different labels, which is of importance for zone delineation algorithms aiming at maximizing inter-zone variability. In addition, this study also proposes an optimisation procedure that yields interpretable within-field zones in which each zone is assigned a clear label. The zoning procedure involves contour delineation based on quantile values. The key point of the paper is to use the proposed numerical zoning quality criterion to guide the optimisation procedure showing the complementarity of both proposals in delineating relevant within-field zones. In order to demonstrate the relevancy of the criterion, the zoning procedure and the implementation of both together, the method was tested on 50 theoretical fields with known variability and known spatial structure. A real plot with yield monitoring data was also used to demonstrate the value of the approach on a real case. Results show the relevancy of the methodology to compare maps with different zones and to sort them. Results also demonstrate the interest of the optimisation procedure to provide a ranked set of possible maps with different within-field zones. This set of relevant maps may constitute a decision support for practitioners who may consider additional expert information to choose the most appropriate map in the specific conditions under consideration. [ABSTRACT FROM AUTHOR]

During a conventional multiple-point statistics simulation, the algorithm may not find a matched neighborhood in the training image for some unsimulated pixels. These pixels are referred to as the dead-end pixels; the existence of the dead-end pixels means that multiple-point statistics simulation is not a simple sequential simulation. In this paper, the multiple-point statistics simulation is cast as a combinatorial optimization problem, and the efficient backtracking algorithm is developed to solve this optimization problem. The efficient backtracking consists of backtracking, forward checking, and conflict-directed backjumping algorithms that are introduced and discussed in this paper. This algorithm is applied to simulate multiple-point statistics properties of some synthetic training images; the results show that no anomalies occurred in any of the produced realizations as opposed to previously published methods for solving the dead-end pixels. In particular, in simulating a channel system, all the channels generated by this method are continuous, which is of paramount importance in fluid flow simulation applications. The results also show that the presence of hard data does not degrade the quality of the generated realizations. The presented method provides a robust algorithmic framework for performing MPS simulation. [ABSTRACT FROM AUTHOR]

In this paper, a method for apnea bradycardia detection in preterm infants is presented based on coupled hidden semi Markov model (CHSMM). CHSMM is a generalization of hidden Markov models (HMM) used for modeling mutual interactions among different observations of a stochastic process through using finite number of hidden states with corresponding resting time. We introduce a new set of equations for CHSMM to be integrated in a detection algorithm. The detection algorithm was evaluated on a simulated data to detect a specific dynamic and on a clinical dataset of electrocardiogram signals collected from preterm infants for early detection of apnea bradycardia episodes. For simulated data, the proposed algorithm was able to detect the desired dynamic with sensitivity of 96.67% and specificity of 98.98%. Furthermore, the method detected the apnea bradycardia episodes with 94.87% sensitivity and 96.52% specificity with mean time delay of 0.73 s. The results show that the algorithm based on CHSMM is a robust tool for monitoring of preterm infants in detecting apnea bradycardia episodes. Graphical Abstract Apnea Bradycardia detection using Coupled hidden semi Markov Model from electrocardiography. In this model, a sequence of hidden states is assigned to each observation based on the effects of previous states of all observations. [ABSTRACT FROM AUTHOR]

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud. [ABSTRACT FROM AUTHOR]

This paper presents a new control algorithm development approach for induction machines by using model-based design and a systematically built model architecture implemented in MATLAB/Simulink. The model architecture follows a three-layer structure, and it is developed according to the principle of functional decomposition and the needs of reusability and expandability. The first model layer consists of elementary model and algorithm components, the second contains a machine simulation model and a field-oriented control (FOC) algorithm, built upon the first layer's components, and the third realises the executable models by connecting the models and algorithms defined in the second layer. Furthermore, rapid control prototyping (RCP) is discussed as an experimental validation method, and an experimental setup with RCP is also introduced. The application of the presented methods is demonstrated by simulations as well as by experiments, and by using a control algorithm based on FOC as an example. [ABSTRACT FROM AUTHOR]

Although Durr and Hoyer have proposed state-of-the-art quantum algorithm (DHA) for searching minimum value, the lower limit of DHA's successful probability is 1/2. Also, DHA requires approximately (log 2 N) 2 copies of the initial state. In this paper, we propose a new quantum maximum or minimum searching algorithm (QUMMSA). In big data scenarios, according to sparse sampling with different densities, we can estimate the corresponding precision parameters. QUMMSA can improve the successful probability close to 100 % . Furthermore, with the quantum exact search algorithm, QUMMSA only requires approximately log 2 N copies of the initial state to solve this problem. Since preparing an arbitrary quantum state is a problem with exponential complexity, our algorithm has a greater advantage with the increasing database size. In addition, we first propose a general method for circuits construction, which can be used in any database. An experiment implemented in an IBM superconducting processor and a numerical simulation of a 6-qubit system to solve a real issue indicate the feasibility and efficiency of QUMMSA. QUMMSA can serve as a subroutine in various quantum algorithms which involves searching maximum or minimum. [ABSTRACT FROM AUTHOR]

The nonparametric evidential regression (EVREG) method provides flexible forms of prediction regarding the value of output, allowing the output of training instances to be partially unknown. However, the superfluous training instances still have negative effects on the parameter learning in EVREG. To relax this limitation, this paper introduces an interactive nonparametric evidential regression (IEVREG) algorithm with instance selection. More specifically, the significance of an instance is firstly measured by defining the evaluation functions, taking into account both the prediction accuracy of regression model and the spatial information between that instance with other ones. According to a search strategy, the instances with high degree of significance are then selected to maximize an objective function. Different from existing instance selection methods, the selection of training instances is synchronously accomplished with the parameter learning in IEVREG, rather than just a separated data preprocessing operation as traditional methods do. Furthermore, the noise and redundant instances can be simultaneously removed and the performance of IEVREG is robust to the order of presentation of instances in raw data set. Experimental results show that the proposed IEVREG algorithm has appropriate prediction accuracy, while performing well selection of the representative training instances from the raw data set. Simulations on synthetic and UCI real-world data sets validate our conclusions. [ABSTRACT FROM AUTHOR]

Multi-body collision detection is a key and important technology in societies of computer graphics, system simulation, virtual reality, etc. and has been widely used in various graphics applications. To deal with the collision detection problem in large-scale multi-body scenes, we in this paper proposed a robust and efficient algorithm based on the kinetic "sweep and prune" technique and the event-driven mechanism. Our algorithm first culls redundant detection calculations in finding object pairs that do not collide in the scene with very large number of moving objects and then automatically generates events to predict the object collisions, which probably take place in near future. All the events are pushed into an optimized priority queue to drive the proposed algorithm. By introducing a hybrid bounding box hierarchy in the event processing process, the algorithm can detect the positions where the object pairs collide. Based on our finding that event blocking is an important factor affecting the robustness of the algorithm, we further propose several techniques to alarm blockings to be occurred or relieve the system from blocking state. Experimental results show that our algorithm has good stability and robustness, and it can improve the operating efficiency of multi-body continuous collision detections in an efficient way. [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]

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]

In this paper, the conditional distance correlation (CDC) is used as a measure of correlation to develop a conditional feature screening procedure given some significant variables for ultrahigh-dimensional data. The proposed procedure is model free and is called conditional distance correlation-sure independence screening (CDC-SIS for short). That is, we do not specify any model structure between the response and the predictors, which is appealing in some practical problems of ultrahigh-dimensional data analysis. The sure screening property of the CDC-SIS is proved and a simulation study was conducted to evaluate the finite sample performances. Real data analysis is used to illustrate the proposed method. The results indicate that CDC-SIS performs well. [ABSTRACT FROM AUTHOR]

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases. [ABSTRACT FROM AUTHOR]

In this paper, two arctangent-based normalized subband adaptive filter (Arc-NSAF) algorithms are proposed, which are derived from two different forms of arctangent cost function and their difference is the order of the arctangent computation and the summation operation of the normalized subband error signal. Benefiting from the excellent property of the arctangent function for suppressing impulsive interferences, the proposed Arc-NSAF algorithms not only possess good robustness against impulsive interferences, but also obtain faster convergence rate for the colored input signals. To further improve the convergence performance of the Arc-NSAFs when identifying sparse system, the proportionate versions of the Arc-NSAF algorithms are also proposed. Simulation results have demonstrated the superiority of the proposed algorithms in impulsive interferences environments as compared to their respective competitors. [ABSTRACT FROM AUTHOR]

Cross-efficiency evaluation is an effective method for ranking decision making units (DMUs) in data envelopment analysis, which is performed with peer-evaluation and self-evaluation. From different points of view, various cross-efficiency evaluations have been proposed with different secondary goals. Yet they usually lead to different average cross-efficiencies and different rankings. In this paper, we develop a concept of the aggressive game cross-efficiency, and propose an aggressive secondary model to minimize the cross-efficiencies of other DMUs under the constraints that the aggressive game cross-efficiency of the evaluated DMU is guaranteed. To achieve the aggressive game cross-efficiency, we develop an iterative algorithm. Mathematically, it is proved that all conventional average cross-efficiencies are sure to converge to the same aggressive game cross-efficiency by the iterative algorithm. Finally, numerical examples are presented to show the effectiveness of our approach in evaluating and ranking DMUs. [ABSTRACT FROM AUTHOR]

This paper proposes a robust set-membership affine projection algorithm with coefficient vector reuse (RSM-APA-CVR) for high background noise environment. In the proposed algorithm, the sum of the squared $$L_{2}$$ norms of the differences between the updated weight vector and past weight vectors is minimized and a new robust error bound is designed. Simulation results in acoustic echo cancellation context show that the proposed algorithm has faster convergence rate and smaller steady-state misalignment as compared to the conventional RSM-APA. [ABSTRACT FROM AUTHOR]

Explosive welding involves processes like the detonation of explosive, impact of metal structures and strong fluid-structure interaction, while the whole process of explosive welding has not been well modeled before. In this paper, a novel smoothed particle hydrodynamics (SPH) model is developed to simulate explosive welding. In the SPH model, a kernel gradient correction algorithm is used to achieve better computational accuracy. A density adapting technique which can effectively treat large density ratio is also proposed. The developed SPH model is firstly validated by simulating a benchmark problem of one-dimensional TNT detonation and an impact welding problem. The SPH model is then successfully applied to simulate the whole process of explosive welding. It is demonstrated that the presented SPH method can capture typical physics in explosive welding including explosion wave, welding surface morphology, jet flow and acceleration of the flyer plate. The welding angle obtained from the SPH simulation agrees well with that from a kinematic analysis. [ABSTRACT FROM AUTHOR]

In computational structural dynamics, particularly in the presence of nonsmooth behavior, the choice of the time-step and the time integrator has a critical impact on the feasibility of the simulation. Furthermore, in some cases, as in the case of a bridge crane under seismic loading, multiple time-scales coexist in the same problem. In that case, the use of multi-time scale methods is suitable. Here, we propose a new explicit-implicit heterogeneous asynchronous time integrator (HATI) for nonsmooth transient dynamics with frictionless unilateral contacts and impacts. Furthermore, we present a new explicit time integrator for contact/impact problems where the contact constraints are enforced using a Lagrange multiplier method. In other words, the aim of this paper consists in using an explicit time integrator with a fine time scale in the contact area for reproducing high frequency phenomena, while an implicit time integrator is adopted in the other parts in order to reproduce much low frequency phenomena and to optimize the CPU time. In a first step, the explicit time integrator is tested on a one-dimensional example and compared to Moreau-Jean's event-capturing schemes. The explicit algorithm is found to be very accurate and the scheme has generally a higher order of convergence than Moreau-Jean's schemes and provides also an excellent energy behavior. Then, the two time scales explicit-implicit HATI is applied to the numerical example of a bridge crane under seismic loading. The results are validated in comparison to a fine scale full explicit computation. The energy dissipated in the implicit-explicit interface is well controlled and the computational time is lower than a full-explicit simulation. [ABSTRACT FROM AUTHOR]

With the development of casting process and its numerical simulation technique, many demands of realism and clearness in post-processing have arisen. This paper introduces the implicit surface to post-processing for the results of casting simulation so as to meet these demands. In this paper, a robust and fully automatic algorithm is proposed for extracting the implicit surface in the signed distance function calculated from the import stereolithography files. Then several applications of implicit surface in the post-processing of casting simulation are listed in two categories. The applications in the first category include fast rendering of massive data, section view in arbitrary direction, visualization of residual liquid, and casting deformation, giving clear and pretty visualization of the results in simulation, and making it possible for engineers and process designers to look through the opaque mold, gain insights to casting process, and locate possible defects. The applications in the second category involve thickness distribution and modulus calculation, which offer important characteristics of castings and facilitate the optimization of casting processes. [ABSTRACT FROM AUTHOR]

To create realistic virtual soil specimens for Discrete Element Method simulations, a library containing nearly 100,000 'clumps' was developed. A clump essentially models a soil particle. It consists of numerous overlapping spheres in 3D, or circles in 2D, that are tangent to the particle perimeter. By using a unique corner-preserving algorithm based on the classic 2D definition of particle roundness, the clump generation requires many fewer circles than by previous algorithms. In this paper, the clumps are based on 2D images of real soil particles and they are indexed in the library by their roundness R and sphericity S values. A real soil can be simulated by choosing particles from the library to match the soil's actual distributions of R and S. The clumps are also enlarged or reduced to match a desired particle size distribution. The utility of the clump library in parametric studies was demonstrated by direct shear tests on five very different virtual materials created from clumps in the library. [ABSTRACT FROM AUTHOR]

This paper presents a new proportionate normalized subband adaptive filter (PNSAF) algorithm, called the IAF-PNSAF, which assigns an individual activation factor for each filter coefficient instead of a common one for all filter coefficients as in the standard PNSAF algorithm. This algorithm achieves an improved performance in terms of the convergence rate and tracking capability for identifying a highly sparse system. Moreover, to overcome the trade-off of the fixed-step-size IAF-PNSAF algorithm between the fast convergence rate and small steady-state misalignment, its adaptive combination version is proposed. Simulation results for identifying the impulse response with high sparseness have demonstrated that the proposed algorithms outperform their counterparts. [ABSTRACT FROM AUTHOR]

Reliable spectrum detection of the primary user (PU) performs an important role in the cognitive radio network since it's the foundation of other operations. Spectrum sensing and cognitive signal recognition are two key tasks in the development of cognitive radio (CR) technology in both commercial and military applications. However, when the CR terminals receiving signals have little knowledge about the channel or signal types, these two tasks will become much more difficult. In this paper, we propose a reliable cooperative spectrum detection scheme, which combines the cooperative spectrum sensing with distributed cognitive signal recognition. A novel improved cooperative sensing algorithm is achieved by using a credibility weight factor and the 'tug-of-war' rule, which is based on the double threshold detection and Dempster-Shafer theory, to determine whether the PU signals exist. In this scheme, cognitive signal recognition can be used to identify the signal type when the PU signal is present. During the cognitive signal recognition processing, the CR terminals make local classification of the received signals by using Daubechies5 wavelet transform and Fractional Fourier Transform, and send their recognition results to the globe decision making center. A distributed processing uses these cognitive terminals' local results to make final decisions under the Maximum Likelihood estimation algorithm. Simulation results show that the proposed method can achieve good sensing probability and recognition accuracy under the Additive White Gaussian Noise channel. [ABSTRACT FROM AUTHOR]

This paper presents a new hybridized algorithm known as Firefly algorithm-based least square (FA-LS) method for power system harmonic estimation. It uses a Firefly algorithm-based approach to estimate the phases and a least-square method for estimating the amplitudes of the harmonic signals. The simulation results are presented to demonstrate the estimation accuracy of FA-LS with the recently proposed particle swarm optimization with passive congregation and artificial bee colony with LS algorithms. The obtained results of FA-LS reveal that this algorithm is best in terms of accuracy and computational time. Practical validation is also made with the experimentation of the algorithm with real-time data obtained from a switch mode power supply with the power quality analyzer and estimation is performed under simulating environment. [ABSTRACT FROM AUTHOR]

In this paper, an indirect feedback active noise control (ANC) scheme, based on the fundamental frequency-estimation method, is proposed for systems with multiple tonal noises. The engine noise consists of the harmonic components of the rotation of the crankshaft in the engine. When it is difficult to obtain reference signals, which are necessary for a feedforward ANC, conventional ANC algorithms do not work. In this paper, a new method is proposed to generate reference signals with estimated frequencies. The proposed algorithm consists of two parts: the frequency-estimation (FE) algorithm for the estimation of the fundamental frequency of the rotating machinery and then the conventional filtered-x LMS (FX-LMS) algorithm. In the second algorithm, the reference signal is generated using the fundamental frequency as estimated in the first algorithm. The FE algorithm uses a second-order adaptive notch filter, which is insensitive to impulse noise. In addition, the FE algorithm has good tracking capability and a lower variance of frequency-estimation error for a constant sinusoid signal and chirp signal. The performance of the proposed ANC method is verified through simulations and an experiment using a DSP board (DS-1104) inside a short duct. [ABSTRACT FROM AUTHOR]

To study the feasibility of the classical image least significant bit (LSB) information hiding algorithm on quantum computer, a least significant qubit (LSQb) information hiding algorithm of quantum image is proposed. In this paper, we focus on a novel quantum representation for color digital images (NCQI). Firstly, by designing the three qubits comparator and unitary operators, the reasonability and feasibility of LSQb based on NCQI are presented. Then, the concrete LSQb information hiding algorithm is proposed, which can realize the aim of embedding the secret qubits into the least significant qubits of RGB channels of quantum cover image. Quantum circuit of the LSQb information hiding algorithm is also illustrated. Furthermore, the secrets extracting algorithm and circuit are illustrated through utilizing control-swap gates. The two merits of our algorithm are: (1) it is absolutely blind and (2) when extracting secret binary qubits, it does not need any quantum measurement operation or any other help from classical computer. Finally, simulation and comparative analysis show the performance of our algorithm. [ABSTRACT FROM AUTHOR]

To overcome the performance degradation of adaptive filtering algorithms in the presence of impulsive noise, a novel normalized sign algorithm (NSA) based on a convex combination strategy, called NSA-NSA, is proposed in this paper. The proposed algorithm is capable of solving the conflicting requirement of fast convergence rate and low steady-state error for an individual NSA filter. To further improve the robustness to impulsive noises, a mixing parameter updating formula based on a sign cost function is derived. Moreover, a tracking weight transfer scheme of coefficients from a fast NSA filter to a slow NSA filter is proposed to speed up the convergence rate. The convergence behavior and performance of the new algorithm are verified by theoretical analysis and simulation studies. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study the performance comparison between flat and cluster-based routings in MANETs. Previous study shows that an earlier proposed cluster-based routing, Cluster based Routing Protocol (CBRP), has poorer performance due to its inefficient clustering algorithms based on Least Clusterhead Change (LCC) and Lowest ID (LID). Thus, in this paper, we first propose a new cluster-based routing, namely efficient cluster based routing protocol (ECBRP), which is based on a more efficient and stable clustering scheme. In addition, a new information table update mechanism utilizing routing events and data forwarding events is introduced in ECBRP to enhance the clustering and routing performance. The performance comparison in terms of packet delivery ratio and normalized routing overhead between flat and cluster based routings are studied extensively. Two flat routings (Dynamic Source Routing (DSR) and Ad Hoc On-demand Distance Vector (AODV)) and two cluster-based routing (CBRP and ECBRP) under various scenarios such as traffic load, mobility, network size and network area are considered in our study. By evaluating the performance of ECBRP and CBRP, we can study how the performance of a CBRP is affected by its underlying cluster structure. Simulation results show that ECBRP can achieve satisfying routing performance and outperform other routing schemes, DSR, AODV and CBRP, under the same simulated scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose an auto-tuning of mobility algorithm in the mobile communication systems as a load balancing self-optimization use case. This paper presents a distributed approach in which the traffic load can be balanced systematically by utilizing the effective load information of neighboring cells. The proposed load balancing scheme detects a load imbalance in the network and resolves the problem automatically by controlling the handover parameter as a nonlinear function of the load difference. Based on control theory, we analyze the adaptive behavior of the proposed controller and derive condition for system stability. Results from asymptotic analysis and simulation indicate that the proposed local controller of each individual cell effectively achieves the global properties, such that the load difference in the network is stabilized globally within a predetermined threshold. Extensive simulation results also show that the proposed scheme outperforms the existing methods by reducing call blocking rate of the overloaded cells. [ABSTRACT FROM AUTHOR]

Traditional simulation methods that are based on some form of kriging are not sensitive to the presence of strings of connectivity of low or high values. They are particularly inappropriate in many earth sciences applications, where the geological structures to be simulated are curvilinear. In such cases, techniques allowing the reproduction of multiple-point statistics are required. The aim of this paper is to point out the advantages of integrating such multiple-statistics in a model in order to allow shape reproduction, as well as heterogeneity structures, of complex geological patterns to emerge. A comparison between a traditional variogram-based simulation algorithm, such as the sequential indicator simulation, and a multiple-point statistics algorithm (e.g., the single normal equation simulation) is presented. In particular, it is shown that the spatial distribution of limestone with meandering channels in Lecce, Italy is better reproduced by using the latter algorithm. The strengths of this study are, first, the use of a training image that is not a fluvial system and, more importantly, the quantitative comparison between the two algorithms. The paper focuses on different metrics that facilitate the comparison of the methods used for limestone spatial distribution simulation: both objective measures of similarity of facies realizations and high-order spatial cumulants based on different third- and fourth-order spatial templates are considered. [ABSTRACT FROM AUTHOR]

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization. [ABSTRACT FROM AUTHOR]

The advent of multiple-point geostatistics (MPS) gave rise to the integration of complex subsurface geological structures and features into the model by the concept of training images. Initial algorithms generate geologically realistic realizations by using these training images to obtain conditional probabilities needed in a stochastic simulation framework. More recent pattern-based geostatistical algorithms attempt to improve the accuracy of the training image pattern reproduction. In these approaches, the training image is used to construct a pattern database. Consequently, sequential simulation will be carried out by selecting a pattern from the database and pasting it onto the simulation grid. One of the shortcomings of the present algorithms is the lack of a unifying framework for classifying and modeling the patterns from the training image. In this paper, an entirely different approach will be taken toward geostatistical modeling. A novel, principled and unified technique for pattern analysis and generation that ensures computational efficiency and enables a straightforward incorporation of domain knowledge will be presented. In the developed methodology, patterns scanned from the training image are represented as points in a Cartesian space using multidimensional scaling. The idea behind this mapping is to use distance functions as a tool for analyzing variability between all the patterns in a training image. These distance functions can be tailored to the application at hand. Next, by significantly reducing the dimensionality of the problem and using kernel space mapping, an improved pattern classification algorithm is obtained. This paper discusses the various implementation details to accomplish these ideas. Several examples are presented and a qualitative comparison is made with previous methods. An improved pattern continuity and data-conditioning capability is observed in the generated realizations for both continuous and categorical variables. We show how the proposed methodology is much less sensitive to the user-provided parameters, and at the same time has the potential to reduce computational time significantly. [ABSTRACT FROM AUTHOR]

Abstract  The integration of optimization methodologies with computational analyses/simulations has a profound impact on the product design. Such integration, however, faces multiple challenges. The most eminent challenges arise from high-dimensionality of problems, computationally-expensive analysis/simulation, and unknown function properties (i.e., black-box functions). The merger of these three challenges severely aggravates the difficulty and becomes a major hurdle for design optimization. This paper provides a survey on related modeling and optimization strategies that may help to solve High-dimensional, Expensive (computationally), Black-box (HEB) problems. The survey screens out 207 references including multiple historical reviews on relevant subjects from more than 1,000 papers in a variety of disciplines. This survey has been performed in three areas: strategies tackling high-dimensionality of problems, model approximation techniques, and direct optimization strategies for computationally-expensive black-box functions and promising ideas behind non-gradient optimization algorithms. Major contributions in each area are discussed and presented in an organized manner. The survey exposes that direct modeling and optimization strategies to address HEB problems are scarce and sporadic, partially due to the difficulty of the problem itself. Moreover, it is revealed that current modeling research tends to focus on sampling and modeling techniques themselves and neglect studying and taking the advantages of characteristics of the underlying expensive functions. Based on the survey results, two promising approaches are identified to solve HEB problems. Directions for future research are also discussed. [ABSTRACT FROM AUTHOR]

Clustering in mobile ad hoc network (MANET) play a vital role in improving its basic network performance parameters like routing delay, bandwidth consumption and throughput. One-hop clustering scheme adopts the simple mechanism to make the logical partition of the dynamic network where the network topology changes constantly resulting an unstable clustering. This paper makes a comprehensive survey of some bench-mark one-hop clustering algorithms to understand the research trends in this area. The literature provides the logic of cluster formation for different algorithms in achieving a linked cluster architecture and an intensive simulation survey of their performance on the cluster maintenance aspects such as cluster density, frequency of cluster reelection, frequency of cluster changes by the nodes and the granularity of cluster heads. This paper should facilitate the researchers as well as practitioners in choosing a suitable clustering algorithm on the basis of their formation and maintenance overhead, before any routing scheme is adopted in the mobile ad hoc network. [ABSTRACT FROM AUTHOR]

Abstract  This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multi-robot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper focuses on the challenge of constructing a coverage spanning tree for both online and offline coverage that minimizes the time to complete coverage. Our general approach involves building a spanning tree by growing sub-trees from the initial location of the robots. This paper first describes a polynomial time tree-construction algorithm for offline coverage. The use of this algorithm is shown by extensive simulations to significantly improve the coverage time of the terrain even when used as a basis for a simple, inefficient, coverage algorithm. Second, this paper provides an algorithm for online coverage of a finite terrain based on spanning-trees, that is complete and guarantees linear time coverage with no redundancy in the coverage. In addition, the solutions proposed by this paper guarantee robustness to failing robots: the offline trees are used as base for robust multi-robot coverage algorithms, and the online algorithm is proven to be robust. [ABSTRACT FROM AUTHOR]

This paper introduces an approach for generating scenarios of sea port container terminals. The scenarios can be used as input data for simulation models. Furthermore, they can be employed as test data for algorithms to solve optimization problems in container terminal logistics such as berth planning and crane scheduling. A scenario consists of arrivals of deep sea vessels, feeder ships, trains, and trucks together with lists of containers to be loaded and unloaded. Moreover, container attributes such as size, empty, reefer, weight. and destination are included. The generator is based on a large number of parameters that allow the user to produce realistic scenarios of any size. The purpose of this paper is to outline the parameters that are important to produce realistic scenarios of high practical relevance and to propose an algorithm that computes scenarios on the basis of these parameters. The generator discussed here has been developed within the simulation project at the HHLA Container-Terminal Altenwerder in Hamburg, Germany. Nevertheless, its structure is general enough to be applied to any other terminal as well. [ABSTRACT FROM AUTHOR]

In this paper, we propose a procedure for detecting multiple change-points in a mean-shift model, where the number of change-points is allowed to increase with the sample size. A theoretic justification for our new method is also given. We first convert the change-point problem into a variable selection problem by partitioning the data sequence into several segments. Then, we apply a modified variance inflation factor regression algorithm to each segment in sequential order. When a segment that is suspected of containing a change-point is found, we use a weighted cumulative sum to test if there is indeed a change-point in this segment. The proposed procedure is implemented in an algorithm which, compared to two popular methods via simulation studies, demonstrates satisfactory performance in terms of accuracy, stability and computation time. Finally, we apply our new algorithm to analyze two real data examples. [ABSTRACT FROM AUTHOR]

Duality quantum computing is a new mode of a quantum computer to simulate a moving quantum computer passing through a multi-slit. It exploits the particle wave duality property for computing. A quantum computer with n qubits and a qudit simulates a moving quantum computer with n qubits passing through a d-slit. Duality quantum computing can realize an arbitrary sum of unitaries and therefore a general quantum operator, which is called a generalized quantum gate. All linear bounded operators can be realized by the generalized quantum gates, and unitary operators are just the extreme points of the set of generalized quantum gates. Duality quantum computing provides flexibility and a clear physical picture in designing quantum algorithms, and serves as a powerful bridge between quantum and classical algorithms. In this paper, after a brief review of the theory of duality quantum computing, we will concentrate on the applications of duality quantum computing in simulations of Hamiltonian systems. We will show that duality quantum computing can efficiently simulate quantum systems by providing descriptions of the recent efficient quantum simulation algorithm of Childs and Wiebe (Quantum Inf Comput 12(11-12):901-924, ) for the fast simulation of quantum systems with a sparse Hamiltonian, and the quantum simulation algorithm by Berry et al. (Phys Rev Lett 114:090502, ), which provides exponential improvement in precision for simulating systems with a sparse Hamiltonian. [ABSTRACT FROM AUTHOR]

Wireless sensor and actor networks (WSAN) are captivating significant attention because of their suitability for safety-critical applications. Efficient actor placement in such applications is extremely desirable to perform effective and timely action across the deployment region. Nonetheless, harsh application environment inherently favors random placement of actors that leads to high concentration deployment and strangles coverage. Moreover, most of the published schemes lack rigorous validation and entirely rely on informal techniques (e.g., simulation) for evaluating nonfunctional properties of algorithms. This paper presents a localized movement control actor relocation (MCAR) algorithm that strives to improve connected coverage while minimizing movement overhead. MCAR pursues post-deployment actor repositioning in such a way that actors repel each other for better coverage while staying connected. We employ complementary formal and informal techniques for MCAR verification and validation. We model WSAN as a dynamic graph and transform MCAR to corresponding formal specification using Z notation. The resulting specification is analyzed and validated using Z eves tool. We simulate the specification to quantitatively demonstrate the efficiency of MCAR. Simulation results confirm the efficiency of MCAR in terms of movement overhead and connected coverage compared to contemporary schemes. The results show that MCAR can reduce distance movement up to 32 % while improving coverage up to 29 % compared to published schemes. [ABSTRACT FROM AUTHOR]

The continuous progress in the performance of supercomputers has made possible the understanding of many fundamental problems in science. Simulation, the third scientific pillar, constantly demands more powerful machines to use algorithms that would otherwise be unviable. That will inevitably lead to the deployment of an exascale machine during the next decade. However, fault tolerance is a major challenge that has to be overcome to make such a machine usable. With an unprecedented number of parts, machines at extreme scale will have a small mean-time-between-failures. The popular checkpoint/restart mechanism used in today's machines may not be effective at that scale. One promising way to revamp checkpoint/restart is to use message-logging techniques. By storing messages during execution and replaying them in case of a failure, message logging is able to shorten recovery time and save a substantial amount of energy. The downside of message logging is that memory footprint may grow to unsustainable levels. This paper presents a technique that decreases the memory pressure in message-logging protocols by only storing the necessary messages in collective-communication operations. We introduce Camel, a protocol that has a low memory overhead for multicast and reduction operations. Our results show that Camel can reduce memory footprint in a molecular dynamics benchmark for more than 95 % on 16,384 cores. [ABSTRACT FROM AUTHOR]

Fast Decoupled Load Flow (FDLF) is a very popular and widely used power flow analysis method because of its simplicity and efficiency. Even though the basic FDLF algorithm is well investigated, the same is not true in the case of additional schemes/modifications required to obtain adjusted load flow solutions using the FDLF method. Handling generator Q limits is one such important feature needed in any practical load flow method. This paper presents a comprehensive investigation of two classes of schemes intended to handle this aspect i.e. the bus type switching scheme and the sensitivity scheme. We propose two new sensitivity based schemes and assess their performance in comparison with the existing schemes. In addition, a new scheme to avoid the possibility of anomalous solutions encountered while using the conventional schemes is also proposed and evaluated. Results from extensive simulation studies are provided to highlight the strengths and weaknesses of these existing and proposed schemes, especially from the point of view of reliability. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a localization algorithm in which nodes are able to estimate their speeds, directions and motion types. By this way, node's next state can be estimated and the particles can be distributed closer to the predicted locations. Hence, accuracy and the precision of the localization are increased considerably. Sequential Monte Carlo method is used to represent the posterior distribution of a node's possible locations with a set of weighted samples. The developed algorithm is designed for a general network where no restrictions are made on the densities and the distributions of the nodes. Eight localization algorithms, namely Centroid, APIT, Amorphous, DV-Hop, MCL, SMCL, MCB and the developed algorithm SMCLA are implemented and compared for different parameters and mobility models. Simulation results show that the developed algorithm performs best for each of the simulation comparisons. [ABSTRACT FROM AUTHOR]

If the three dimensional self-avoiding walk (SAW) is conformally invariant, then one can compute the hitting densities for the SAW in a half-space and in a sphere (Kennedy in Phys Rev Lett 111: 165703, ). The ensembles of SAW's used to define these hitting densities involve walks of arbitrary lengths, and so these ensembles cannot be directly studied by the pivot Monte Carlo algorithm for the SAW. We show that these mixed length ensembles should have the same scaling limit as certain weighted ensembles that only involve walks with a single length, thus providing a fast method for simulating these ensembles. Preliminary simulations which found good agreement between the predictions and Monte Carlo simulations for the SAW were reported in Kennedy (Phys Rev Lett 111: 165703, ). In this paper we present more accurate simulations testing the predictions and find even stronger support for the prediction that the SAW is conformally invariant in three dimensions. [ABSTRACT FROM AUTHOR]

The understanding and prediction of the generated surface topography are very important in milling process. This paper presents a surface topography model and an on-line simulation method of surface topography considering tool wear in a multi-axis milling process. First, the cutting edge equation is established by dividing the cutting edge into a series of cutting edge elements considering tool wear influence and tilt angle of tool axis on each discrete cutting edge element, and an algorithm is proposed to determine the range of divided position angle. Then, the sculptured surface workpiece is divided into a series of elements along the feed direction with the same or less than discrete precision of cutting edge. The intersection points between the discrete cutting edge elements and the planes of both sides of each divided element on workpiece can be obtained based on the established cutting edge equation in the cutting process. The left cutting trajectory points on workpiece are got by reserving the points with the minimum coordinate value in the Z-axis direction under the same coordinate value in the X-axis and Y-axis direction among the intersection points and points on workpiece. Thus, surface topography model can be constructed based on the established cutting edge equation, the determined range of position angle, discrete cutting edge elements, and divided element on workpiece surface. Finally, an on-line simulation algorithm is proposed to simulate the generation process of surface topography in multi-axis milling operation. Experimental work and validation of the established model are performed on a five-axis milling center by using stainless steel 0Cr17Ni12Mo2 and cemented carbides milling cutter. The results about the topography generation of the constructed B-spline surface show that the predictions of the established surface topography model correlate well with the experimental results. [ABSTRACT FROM AUTHOR]

In this paper we proposed a new extended grid-based broadcasting algorithm (EGBB) in mobile ad-hoc networks (MANETs) which reduces considerably the broadcast storm problem. EGBB algorithm is based on a logical 2-dimensional grid cells view of the geographical region of the MANET. EGBB algorithm minimizes the number of rebroadcasts by using gateway nodes for each grid cell, where only gateway nodes are responsible for rebroadcasting the message in order to solve network congestion problem. We extended the neighborhood of a node in a grid cell from the nodes located in only eight adjacent grid cells as originally in grid based broadcast (GBB) to any node in any grid cell within the node's transmission range to increase the reachability and reduce the number of hops. We also added a new adaptive feature to suite different traffic load and mobility conditions. We developed a simulation model based on NS2 simulator to measure the performance of EGBB and compare the results with the GBB; the efficient counter based scheme (ECB); and the position-aware counter-based algorithm (PCB). The simulation experiments showed good results for our new algorithm EGBB. EGBB outperforms GBB, ECB and PCB in terms of end-to-end delay, number of saved rebroadcasts, and packet collision ratio under different traffic load, network density, and mobility conditions. [ABSTRACT FROM AUTHOR]

Advanced forging simulation codes leads die designer toward more efficient methods in waste material reduction. In this paper, an approach has been presented to reduce waste material by controlling material flow with variable gutter. Initially, an algorithm is offered for decision making. A control criterion (δ) has been suggested in order to determine the new modified gutter thickness and width. A new formula has been offered to calculate the new dimensions of gutter width and thickness base on (δ) criterion in each step. To evaluate the effectiveness of the proposed criterion and formula a set of Finite volume and finite element simulations has been done on different forging parts. Then a 'T shape' part has been chosen as a case study to investigate the effect of variable gutter width method and variable gutter thickness method separately. The FVM and FEM simulation results for variable thickness and width show a waste material reduction of 12% and 14%, respectively. Finally an experimental test has been setup to verify the simulation result of variable gutter thickness method. The simulation and experimental results have a good agreement. [ABSTRACT FROM AUTHOR]

Time synchronization is an essential building block of sensor network systems. In this paper, we present a comprehensive study of least squares algorithms for time synchronization in sensor networks, with a focus on continuous monitoring and data collection applications. We propose a set of algorithms to address a number of issues in practical implementation on typical low-cost sensor network platforms, including a scaled signal model to achieve numerical stability in an ill-conditioned problem, sequential estimators for the scaled signal model to reduce computational complexity, a fast initialization scheme to improve energy efficiency, and outlier detection algorithms to improve robustness in long-term autonomous operations. The proposed algorithms are implemented and a measurement-based simulation method is employed to study the performance. [ABSTRACT FROM AUTHOR]