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This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation. [ABSTRACT FROM AUTHOR]

In the light of the problems that exist in the current cutting algorithms: first, the number of tetrahedron elements increases as the cutting goes on, which leads to the decrease of the algorithm efficiency. Second, the topology structure is constantly updated during the cutting process, which leads to the stutter phenomenon in cutting display. In this paper, a cutting simulation implementation method based on transient display and delay adjustment strategy is proposed. Firstly, the feature points in the original path are screened by the feature criterion function, and the feature path is established. Then, by using the concept of minimum sum of square defined in this paper, the optimal path of the separation is found with the help of feature path points, and the pseudo effect of cutting is directly displayed by designing the transient display module. The delay adjustment is carried out in the cutting gap, and the topology separation in the delay adjustment is implemented by the node offset spread algorithm and the duplicate removal process with timeliness. Finally, the advantages and feasibility of the cutting algorithm proposed in this paper are verified by practical tests. The test results show that the transient display algorithm proposed in this paper takes about 20 ms. With the number of triangular patches increasing, the growth rate of transient display time remains low. The cutting simulation is implemented by using transient display and delay adjustment strategy, which greatly improves the simulation efficiency. [ABSTRACT FROM AUTHOR]

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]

Recent mobile applications have required high data rate. In metropolitan areas where data traffic is higher than other areas, heterogeneous networks (HetNet) are used to meet the traffic demand (Hoadley and Maveddat in IEEE Commun Mag 19(2):4-5, 2012). The HetNet has several low power base stations or remote radio heads in a macro cell coverage and sometimes coordinates the low power transmission points forming virtual multiple-input and multiple-output system. In this paper, inter-relay interference (IRI) cancellation for two-path relay system is considered. In existing schemes, IRI cancellation is performed at a destination node but proposed method performs the cancellation at relay nodes to reduce the complexity of detection algorithm for the destination node. Simulation results also show that the method of this paper achieves better BER performance than the existing schemes. [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 problem we are trying to solve in this paper is the authenticity identification of speaker digital recording data. As an important basis of the judicial identification, it is crucial to ensure the authenticity of digital recording data. A large number of solutions have been proposed to address the problem. However, classic methods are usually based on logical symbol rather than the physical detection of energy or phase, and these solutions show drawbacks in terms of identification inefficiency, algorithm instability and heavy time overhead. In this paper, we propose to utilize the quantum theory to address the problem. Any tampering operation for digital recording data can lead to the change of charge in the memory, and it can utilize the subtle change to implement the identification. First, we analyze the quantum nature of storage and investigate to extract the transmittance of speech signal as the characteristic value through quantum tunneling theory. Second, aiming at the characteristics of speech signal, we utilize the transmittance to define the rotation angle step function and propose an improved quantum genetic algorithm to detect the change of phase. The proposed method achieves the authenticity identification based on phase detection. The results obtained in this research include the problem can be addressed by phase detection solution based on quantum genetic algorithm, and it shows performance benefits compared with existing solutions by simulation experiment. It is not only theoretically but also practically feasible to realize authenticity identification of speak digital recording data. [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]

In this paper, an appropriate selection for the key is investigated to enhance the security level of the speech cryptosystem based on blind sources separation. In fact, if an appropriate key is not selected, the cryptosystem may be attacked and therefore each confidential signal can be recovered only from the encrypted ones. Two important conditions which are studied in this paper are the number of quantized levels for a digital key and also non-sparsity in time-frequency domain. In the case of the first condition, simulation results show that with smaller coefficient for the confidential signal in comparison with the key, the number of quantized levels for the key should be more to guarantee the security. In the case of the second condition, an algorithm is proposed to recover the confidential signal only from the encrypted signal when the key is sparse in time-frequency domain. [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]

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]

Automatic trajectory generation for robot manipulators is a highly desirable application for robots, In this paper, we take into account the geometric aspects of trajectory planning, especially the workpiece reconstruction and description used to generate the trajectories. Generating manipulator trajectories for free-form surfaces to satisfy manufacturing requirements is highly challenging due to the complex geometry of free-form surfaces. This paper describes the development of a 3D surface reconstruction guided manipulator trajectory generation system for a magnetic scattering film plating robot. After reviewing existing methods of trajectory generation, an effective trajectory generation method for robotic film plating is introduced and a trajectory planning algorithm that can be used to control a magnetic scattering film plating robot is proposed. A kinematics verification method is also provided to verify the generated trajectories. Results obtained from the simulation and experiments are presented, the results show that the trajectory generation system achieves satisfactory performance. The paper fulfils an identified need for an effective trajectory generation method for magnetic scattering robotic film plating based on the workpiece surface reconstruction after the mesh subdivision. The trajectory generation system can also be used to generate trajectories for many other surface-guided robot trajectory planning applications. [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]

Product line testing is challenging due to the potentially huge number of configurations. Several approaches have tackled this challenge; most of them focused on reducing the number of tested products by selecting a representative subset. However, little attention has been paid to product line test optimization using test results, while tests are executed. This paper aims at optimizing the testing process of product lines by increasing the fault detection rate. To this end we propose a dynamic test prioritization approach. In contrast to traditional static test prioritization, our dynamic test prioritization leverages information of tests being executed in specific products. Processing this information, the initially prioritized tests are rearranged in order to find non-discovered faults. The proposed approach is valid for any kind of product lines, but we have adapted it to the context of configurable simulation models, an area where testing is especially time-consuming and optimization methods are paramount. The approach was empirically evaluated by employing two case studies. The results of this evaluation reveal that the proposed test prioritization approach improves both the static prioritization algorithm and the selected baseline technique. The results provide a basis for suggesting that the proposed dynamic test prioritization approach is appropriate to optimize the testing process of product lines. [ABSTRACT FROM AUTHOR]

Multiple supply voltage based ( V) Systems on Chip (SoCs) allow designers to implement large, complex systems for diverse applications. However, the need for level conversion imposes penalties and often results in non-optimal SoCs. Thus, the level converters are overhead for the circuits in which they are being used. If power consumption of the level converters continues to grow, then they will fail to serve the very purpose for which they were built. This paper proposes the power (leakage)-delay optimization of a DC to DC universal voltage level converter (ULC) using a dual- T (dual-oxide CMOS or DOXCMOS) technique and exploiting transistor geometry. The proposed ULC is a novel circuit proposed here for the first time and performs level-up, level-down conversion, or blocking of the input signal, based on the requirements. The paper further proposes a novel design methodology accompanied by an optimization algorithm for the parasitic-aware power-delay optimization of the ULC circuit. The entire design has been implemented in 90 nm CMOS up to layout, including DRC/LVS and parasitic (RC) re-simulation, and was subjected to process variation of 10 process parameters. The optimal ULC with 20 transistors yields power savings of 87.5 %, delay improvement of 87.3 % and area savings of 21 % over the baseline design. It is a robust design performing a stable voltage level conversion for voltages as low as 0.6 V (50 % of V) and loads varying from 10 to 200 fF. [ABSTRACT FROM AUTHOR]

This paper studies the application of extreme value statistics (EVS) theory on analysis for stock data, based on interior penalty function algorithm and Bootstrap methods. The generalized Pareto distribution (GPD) models are considered in analyzing the closing price data of Shanghai stock market. The maximum likelihood estimates (MLEs) are obtained by using the interior penalty function algorithm. Correspondingly, the bias and standard errors of MLEs, and the hypothesis test on the shape parameter are concerned through Bootstrap methods. Some simulations are performed to demonstrate the efficacy of parameter estimation and the power of the test. The estimates of the tail index in this paper are compared with those obtained via classical methods. At last, the model is diagnosed by numerical and graphical methods and the Value-at-Risk (VaR) is estimated. [ABSTRACT FROM AUTHOR]

Sunken relief is an art form whereby the depicted shapes are sunk into a given flat plane with a shallow overall depth. In this paper, we propose an efficient sunken relief generation algorithm based on a single image by the technique of feature line enhancement. Our method starts from a single image. First, we smoothen the image with morphological operations such as opening and closing operations and extract the feature lines by comparing the values of adjacent pixels. Then we apply unsharp masking to sharpen the feature lines. After that, we enhance and smoothen the local information to obtain an image with less burrs and jaggies. Differential operations are applied to produce the perceptive relief-like images. Finally, we construct the sunken relief surface by triangularization which transforms two-dimensional information into a three-dimensional model. The experimental results demonstrate that our method is simple and efficient. [ABSTRACT FROM AUTHOR]

In order to understand the role of crossover in differential evolution, theoretical analysis and comparative study of crossover in differential evolution are presented in this paper. Two new crossover methods, namely consecutive binomial crossover and non-consecutive exponential crossover, are designed. The probability distribution and expectation of crossover length for binomial and exponential crossover used in this paper are derived. Various differential evolution algorithms with different crossover methods including mutation-only differential evolution are comprehensively compared at system level instead of parameter level. Based on the theoretical analysis and simulation results, the effect of crossover on the reliability and efficiency of differential evolution algorithms is discussed. Some insights are revealed. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) enables high speed vehicles to communicate with each other. This kind of communication can provide road safety and passengers' comfort. Covert channels are used to transmit information secretly over the network. Network covert channel is not only used as a hacking tool, but also used to convey secret information such as private keys. Unlike wired and conventional wireless networks, few studies are conducted on covert communication in VANET. The goal of this paper is to develop a hybrid (timing and storage) covert channel in VANET. In the timing part, covert messages are sent by altering the timing pattern of the service and control packets. The proposed covert timing algorithm is dynamically changed based on the vehicular traffic volume in the transmitter's radio range. This dynamism is used to achieve better covert capacity with an acceptable error rate. On the other hand, some fields of the periodic status messages, sent in the control channel, are utilized in the storage part. An encoding algorithm is also proposed to embed the covert data in the mentioned covert timing and storage opportunities. The encoding algorithm provides a high embedding capacity, even if the number of opportunities' possible values is not any power of two. Finally, the transmitted secret data volume, the packet loss ratio, the channel error rate and the effect of the proposed method on other vehicles' throughput are evaluated in a simulation process. [ABSTRACT FROM AUTHOR]

Delay tolerant network is a type of network where the end-to-end path is not available from source to destination due to the node mobility, dynamic topology and network partitioning or such a path is highly unstable and may split almost immediately after it has been explored. In this background, existing ad hoc routing protocols would be unsuccessful. Therefore, the concept of store-carry-forward mechanism is utilized by introducing a new layer on the top of transport layer called as bundle layer. The bundle layer store message(s) in a finite size buffer for the long duration of time. As a result, node buffer runs out of space and drop messages to overcome congestion. In this paper, we aim to schedule the node buffer by using local knowledge-based buffer management policies called as best message-size selections buffer management policy. We have utilized the local information available in message header such as message-size to control the drop of messages. The evaluation of proposed policy with the existing DOA, DLA, LIFO, MOFO, N-drop and SHLI have been analyzed in ONE simulator in terms of reducing message drop, overhead, latency and raising the delivery ratio. [ABSTRACT FROM AUTHOR]

In this paper, we consider an exact test of goodness of fit for binomial distribution in sparse data situation. A conventional way is viewing this problem as an independence test problem of a two-way contingency table. We propose an approach to promote the efficiency of the Diaconis-Sturmfels (DS) algorithm when n (sample size) is much larger than m [the first parameter of a binomial distribution B(m, p)] through representing the data and then utilizing minimal Markov bases of the corresponding multinomial model. Simulation results and real data analysis indicate that our method makes the DS algorithm computationally faster. [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]

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE. [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]

Bayesian optimization has become a widely used tool in the optimization and machine learning communities. It is suitable to problems as simulation/optimization and/or with an objective function computationally expensive to evaluate. Bayesian optimization is based on a surrogate probabilistic model of the objective whose mean and variance are sequentially updated using the observations and an “acquisition” function based on the model, which sets the next observation at the most “promising” point. The most used surrogate model is the Gaussian Process which is the basis of well-known Kriging algorithms. In this paper, the authors consider the pump scheduling optimization problem in a Water Distribution Network with both ON/OFF and variable speed pumps. In a global optimization model, accounting for time patterns of demand and energy price allows significant cost savings. Nonlinearities, and binary decisions in the case of ON/OFF pumps, make pump scheduling optimization computationally challenging, even for small Water Distribution Networks. The well-known EPANET simulator is used to compute the energy cost associated to a pump schedule and to verify that hydraulic constraints are not violated and demand is met. Two Bayesian Optimization approaches are proposed in this paper, where the surrogate model is based on a Gaussian Process and a Random Forest, respectively. Both approaches are tested with different acquisition functions on a set of test functions, a benchmark Water Distribution Network from the literature and a large-scale real-life Water Distribution Network in Milan, Italy. [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, 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]

This paper considers the contaminated channel estimation in multicell very large multiuser multiple-input multiple-output networks. To provide desirable spectral efficiency of these systems, we propose two adaptive algorithms which their implementations do not require orthogonal pilot sequences. The first algorithm (i.e., normalized least mean square) has low complexity and also gives an acceptable spectral efficiency in channels with low attenuation. The second algorithm (i.e., recursive least square) is more robust against pilot contamination and provides the desirable spectral efficiency in very large number of antennas at the base stations. In simulations, the performance of the proposed algorithms is evaluated and also compared with the performance of the conventional minimum mean square error estimation. [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 this paper, load balancing problem is investigated and a new game theory automatic load balancing scheme in LTE networks is proposed based on non-cooperative differential game. Optimal load allocated to each cell is formulated as a Nash Equilibrium in this paper. It is proved that the non-cooperative differential game algorithm is applicable and the optimal load solution can be achieved. Simulation results show that system capacity is increased and load unbalancing caused inappropriate load allocation is decreased. [ABSTRACT FROM AUTHOR]

This article presents a multi-domain fast multipole hybrid boundary node method for composite materials in 3D elasticity. The hybrid boundary node method (hybrid BNM) is a meshless method which only requires nodes constructed on the surface of a domain. The method is applied to 3D simulation of composite materials by a multi-domain solver and accelerated by the fast multipole method (FMM) in this paper. The preconditioned GMRES is employed to solve the final system equation and precondition techniques are discussed. The matrix-vector multiplication in each iteration is divided into smaller scale ones at the sub-domain level and then accelerated by FMM within individual sub-domains. The computed matrix-vector products at the sub-domain level are then combined according to the continuity conditions on the interfaces. The algorithm is implemented on a computer code written in C + +. Numerical results show that the technique is accurate and efficient. [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]

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]

Efficient energy consumption is crucial for energy constrained networks such as Wireless Sensor Networks (WSN). Using a mobile sink to collect the data of the nodes is a good method to balance the energy level of the nodes and prolong the lifetime of the whole network. For the mobile sink, an efficient path planning can make the mobile sink visit significantly more nodes during a limited period and shorten the latency of information gathering. Considering the communication range of the nodes, we can deduce this routing problem as a special case of traveling salesman problem with neighborhoods (TSPN), which is a NP-hard problem [1]. In this paper, we propose a novel routing design algorithm based on Variable Dimension Particle Swarm Optimization (VD-PSO). In this algorithm, every feasible path solution of TSPN is expressed as a particle. Each dimension of the particle is the coordinates of a rendezvous point (RP, the point where the mobile sink stays to gather data). The dimensionality of the particle is equal to the number of the rendezvous points in the path. Using the evolutionary method of the particles, we can derive the optimal path of the mobile sink. Simulation results show that the proposed algorithm has fast convergence speed, and the result is quite approximate to the optimal solution. [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]

Due to the time-variant channel, outdated channel state information (CSI) largely reduce the performance of double codebook beamforming system. This paper studies the two codebooks independently and proposes an adaptive feedback period adjustment Strategy for long-term codebook and a prediction estimation algorithm for short-term codebook respectively. The former dynamically adjusts feedback period so as to improve the feedback timeliness and uplink feedback resource utilization. The later adopts improved Kalman prediction algorithm to predict CSI. Finally simulation results shows that the proposed schemes can effectively improve the performance of double codebook estimation with outdated CSI. [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]

We consider single channel wireless networks with interference constraint among the links that can be activated simultaneously. The traffic flows are assumed to be single hop. Delay performance of the well known throughput optimal maximum weight link scheduling algorithm has been studied recently. In this paper, we study the relation between network topology and delay of maximum weight link scheduling algorithm. First, we consider 1-hop interference model. Under this interference model, an upper bound for the average delay of packets is derived analytically in terms of edge chromatic number of the network graph. Then the results have been extended to the case of general interference model. Under this model of interference, an upper bound for delay as a function of chromatic number of conflict graph is derived. Since chromatic number and edge chromatic number are network topology parameters, the results show that how the upper bound of delay is affected by network topology. Simulation results confirm our analytical relations. [ABSTRACT FROM AUTHOR]

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters ( K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters. [ABSTRACT FROM AUTHOR]

This paper presents an optimized path generation algorithm for direction parallel milling, which is commonly used in the roughing and finishing stages. First, a geometrically efficient tool path generation algorithm using an intersection points graph is introduced. Second, the generated tool path is modified as an optimized tool path that maintains a constant material removal rate to achieve a constant cutting force and avoid chatter vibration, and the results are verified. Additional tool path segments are appended to the basic tool path through a pixel-based simulation technique. The algorithm is implemented for two-dimensional contiguous end milling operations with flat end mills, and cutting tests are conducted by measuring the spindle current, which reflects the changing machining situations, to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

One of the major concerns in wireless ad-hoc networks design is energy efficiency. Wireless devices are typically equipped with a limited energy supply sufficient only for a limited amount of time which is reversely proportional to the transmission power of the device. The network lifetime is defined as the time the first device runs out of its initial energy charge. In this paper we study the maximum network lifetime problem for broadcast and data gathering in wireless settings. We provide polynomial time approximation algorithms, with guaranteed performance bounds while considering omnidirectional and unidirectional transmissions. We also consider an extended variant of the maximum lifetime problem, which simultaneously satisfies additional constraints, such as bounded hop-diameter and degree of the routing tree, and minimizing the total energy used in a single transmission. Finally, we evaluate the performance of some of our algorithms through simulations. [ABSTRACT FROM AUTHOR]

Injection molding process parameters such as injection temperature, mold temperature, and injection time have direct influence on the quality and cost of products. However, the optimization of these parameters is a complex and difficult task. In this paper, a novel surrogate-based evolutionary algorithm for process parameters optimization is proposed. Considering that most injection molded parts have a sheet like geometry, a fast strip analysis model is adopted as a surrogate model to approximate the time-consuming computer simulation software for predicating the filling characteristics of injection molding, in which the original part is represented by a rectangular strip, and a finite difference method is adopted to solve one dimensional flow in the strip. Having established the surrogate model, a particle swarm optimization algorithm is employed to find out the optimum process parameters over a space of all feasible process parameters. Case studies show that the proposed optimization algorithm can optimize the process parameters effectively. [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]

Block diagonalization (BD) and successive optimization (SO) are two suboptimal but more practical (compared to dirty paper coding (DPC)) orthogonal linear precoding techniques for the downlink of multiuser MIMO systems. Since the numbers of users supported by BD or SO for a given number of transmit antennas are limited, BD or SO should be combined with scheduling so that a subset of users is selected at a given time slot while meeting the dimensionality requirements of these techniques. On the other hand, receive antenna selection (RAS) is a promising hardware complexity reduction technique. In this paper, we consider user scheduling in conjunction with receive antenna selection. Since exhaustive search is computationally prohibitive, we propose simplified and suboptimal user scheduling algorithms for both BD and SO. For BD, we propose capacity and Frobenius-norm based suboptimal algorithms with the objective of sum rate maximization. Starting from an empty set, each step of proposed algorithms adds the best user from the set of users not selected yet until the desired number of users have been selected. Proposed receive antenna selection works in conjunction with user scheduling to further enhance the sum rate of BD. For SO, a Frobenius-norm based low complexity algorithm is proposed, which maximizes the ratio of the squared Frobenius norm of the equivalent channel (projected to the joint null space of the previously selected users) to the sum of the squared Frobenius norms of the previously selected users’ preprocessed channels. Simulation results demonstrate that the proposed algorithms achieve sum rates close to exhaustive search algorithms with much reduced complexity. We also show that in addition to reduced hardware complexity at the receiver, antenna selection enhances multiuser diversity gain that is achieved with user scheduling. [ABSTRACT FROM AUTHOR]

Joint time-frequency representations offer a rich representation of event related potentials (ERPs) that cannot be obtained through individual time or frequency domain analysis. This representation, however, comes at the expense of increased data volume and the difficulty of interpreting the resulting representations. Therefore, methods that can reduce the large amount of time-frequency data to experimentally relevant components are essential. In this paper, we present a method that reduces the large volume of ERP time-frequency data into a few significant time-frequency parameters. The proposed method is based on applying the widely used matching pursuit (MP) approach, with a Gabor dictionary, to principal components extracted from the time-frequency domain. The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data. The results show that the proposed PCA-Gabor approach performs better than either the PCA alone or the standard MP data reduction methods, by using the smallest amount of ERP data variance to produce the strongest statistical separation between experimental conditions. [ABSTRACT FROM AUTHOR]

This paper is devoted to the approximate solution of a strongly NP-hard resource-constrained scheduling problem which arises in relation to the operability of certain high availability real time distributed systems. We present an algorithm based on the simulated annealing metaheuristic and, building on previous research on exact solution methods, extensive computational results demonstrating its practical ability to produce acceptable solutions, in a precisely defined sense. Additionally, our experiments are in remarkable agreement with certain theoretical properties of our simulated annealing scheme. The paper concludes with a short discussion on further research. [ABSTRACT FROM AUTHOR]

Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal in most cases, global optimization techniques are required for avoiding local minima. In this paper, we employ a global optimization algorithm, Quantum-behaved particle swarm optimization (QPSO) that was proposed by us previously, and itsmutated version in the design of digital IIR filter. The mechanism inQPSO is based on the quantum behaviour of particles in a potential well and particle swarm optimization (PSO) algorithm. QPSO is characterized by fast convergence, good search ability, and easy implementation. The mutated QPSO (MuQPSO) is proposed in this paper by using a random vector in QPSO to increase the randomness and to enhance the global search ability. Experimental results on three examples show that QPSO andMuQPSO are superior to genetic algorithm (GA), differential evolution (DE) algorithm, and PSO algorithm in quality, convergence speed, and robustness. [ABSTRACT FROM AUTHOR]

In longitudinal studies, it is of interest to investigate how repeatedly measured markers are associated with time to an event. Joint models have received increasing attention on analyzing such complex longitudinal-survival data with multiple data features, but most of them are mean regression-based models. This paper formulates a quantile regression (QR) based joint models in general forms that consider left-censoring due to the limit of detection, covariates with measurement errors and skewness. The joint models consist of three components: (i) QR-based nonlinear mixed-effects Tobit model using asymmetric Laplace distribution for response dynamic process; (ii) nonparametric linear mixed-effects model with skew-normal distribution for mismeasured covariate; and (iii) Cox proportional hazard model for event time. For the purpose of simultaneously estimating model parameters, we propose a Bayesian method to jointly model the three components which are linked through the random effects. We apply the proposed modeling procedure to analyze the Multicenter AIDS Cohort Study data, and assess the performance of the proposed models and method through simulation studies. The findings suggest that our QR-based joint models may provide comprehensive understanding of heterogeneous outcome trajectories at different quantiles, and more reliable and robust results if the data exhibits these features. [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]

This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually reducing population size is proposed in this paper. It improves the efficiency and robustness of the algorithm and can be applied to any variant of a Differential Evolution algorithm. The proposed modification is tested on commonly used benchmark problems for unconstrained optimization and compared with other optimization methods such as Evolutionary Algorithms and Evolution Strategies. [ABSTRACT FROM AUTHOR]