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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]

In this paper, we seek to improve deep neural networks by generalizing the pooling operations that play a central role in the current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in: (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets. These benefits come with only a light increase in computational overhead during training (ranging from additional 5 to 15 percent in time complexity) and a very modest increase in the number of model parameters (e.g., additional 1, 9, and 27 parameters for mixed, gated, and 2-level tree pooling operators, respectively). To gain more insights about our proposed pooling methods, we also visualize the learned pooling masks and the embeddings of the internal feature responses for different pooling operations. Our proposed pooling operations are easy to implement and can be applied within various deep neural network architectures. [ABSTRACT FROM PUBLISHER]

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel algorithm for peak-to-average power ratio (PAPR) reduction of OFDM signals by executing partial transmit sequence (PTS) in minimal trellis of block codes. The proposed method uses a linear code with good minimal trellis to select the transmitted OFDM signal with minimum PAPR and to transmit side information with error correction. The goals of combining strategy of trellis and PTS are twofold: to provide a low complexity PTS for weighting vector search and to highlight error protection capability for side information. Simulation results show that the proposed scheme achieves good PAPR reduction with lower complexity, compared to other PTS approaches. [ABSTRACT FROM PUBLISHER]

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms. [ABSTRACT FROM PUBLISHER]

Telecommunication networks make extensive use of power amplifiers (PAs) to broaden the coverage from transmitter to receiver. Achieving high power efficiency is challenging and comes at a price: the wanted linear performance is degraded due to nonlinear effects. To compensate for these nonlinear disturbances, existing techniques compute the preinverse of the PA by the estimation of a nonlinear model. However, the extraction of this nonlinear model is involved and requires advanced system identification techniques. The plant inversion iterative learning control (ILC) algorithm is used here in combination with the best linear approximation to investigate whether the nonlinear modeling step can be simplified. This paper introduces the ILC framework for the preinverse estimation and predistortion of PAs. The ILC algorithm is used to obtain a high quality predistorted input for the PA under study without requiring a nonlinear model of the PA. In a second step, a nonlinear preinverse model of the amplifier is obtained. Both the nonlinear and memory effects of a PA can be compensated by this approach. The convergence of the iterative approach and the predistortion results are illustrated on a simulation of a Motorola LDMOS transistor-based PA and a measurement example using the Chalmers RF WebLab measurement setup. [ABSTRACT FROM PUBLISHER]

This paper presents a two-step approach for structural damage identification in beam structure using the influence line and bird mating optimizer (BMO). Local damage is simulated as the reduction of the elemental Young's modulus and mass of beam element. The technique for damage localization based on influence line and its derivatives before and after damage for beam structure was outlined. An objective function comprised of dynamic acceleration is utilized for BMO algorithm. The dynamic response data under external force is calculated by Newmark integration method. Numerical examples of a simply supported beam was investigated. Effect of measurement noise is studied. Studies in the paper indicate that the proposed method is efficient and robust for identifying damages in beam structures. [ABSTRACT FROM AUTHOR]

In this paper, a hydraulic turbine unit transfer function is represented, considering acceleration of the water column, velocity of the water and mechanical power. Using Matlab/Simulink software facilities, have been simulated the behaviour of the mechanical power, depending of the gate opening, with water starting time parameter affected by uncertainties. Step responses analyse are simulated in order to evaluate the performances with load affected by uncertainties and with different control algorithms. This paper can be used in power engineering and control systems fields as a guideline in order to modelling, simulate, analyse and tune the controller for hydraulic turbine unit. [ABSTRACT FROM AUTHOR]

An efficient connection scheme for the stable incorporation of several instructive finite-difference time-domain (FDTD) computing models in the non-standard (NS)-FDTD method is proposed in this paper. Based on a straightforward formulation, the novel technique achieves an optimal FDTD/NS-FDTD connection via an interlayer region. Subsequently, the proposed concept is extended to a robust subgrid algorithm for the 3-D NS-FDTD method through the perfectly matched layer condition to decrease the total computational burden. The merits of the combined methodology are numerically validated by diverse applications, such as wire antennas and microwave devices. [ABSTRACT FROM AUTHOR]

In this paper, the performance of a parallel sparse direct solver on a shared-memory multicore system is presented. Large size test matrices arising from finite element simulation of induction heating industrial applications are used to evaluate the performance improvements due to low-rank representations and multicore parallelization. [ABSTRACT FROM AUTHOR]

In this paper, a proportional-integral sliding mode control (PISMC) method is presented to achieve the attitude and position control of an unmanned helicopter system for autonomous hovering. The PISMC includes the advantages of both conventional sliding mode control (SMC) and integral sliding mode control (ISMC). The proposed PISMC algorithm is capable of stabilized attitude and position control for unmanned helicopter autonomous hovering, and it offers a performance improvement over the conventional SMC and ISMC algorithms as well. The system performance is quantified and an experimental unmanned helicopter system is developed to access the performance of the PISMC. Both simulations and actual flight tests are conducted to verify the PISMC attitude and position control. The simulation results show that the PISMC tracking error is smaller and that PISMC converges faster than SMC does. The flight test results prove that the proposed PISMC is effective in the attitude and position control of unmanned helicopter autonomous hovering. [ABSTRACT FROM AUTHOR]

Elliptical vibration cutting (EVC), as a precision machining technology, is used in many applications. In precision machining, control accuracy plays an essential role in improving the machinability of difficult-to-machine materials. To improve the control accuracy, dynamic and static characteristics of the system need to be tuned to obtain the optimal parameters. In this paper, we use a glowworm algorithm with an improved adaptive step size to tune the parameters of a robust adaptive fuzzy controller. We then obtain the optimal controller parameters through simulation. The optimal solution of the controller parameters is then applied to a 3D EVC system model for simulation and closed-loop testing experiments. The results indicate that a good agreement between the ideal curve and the tracking signal curve verifies the optimality of the controller parameters. Finally, under certain cutting conditions, the workpieces of three different materials are cut with two different cutting methods. The study revealed that the surface roughness value is reduced by 20 %–32 %, which further verifies the effectiveness of the optimal controller's parameters. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of selecting a Pareto set from among finite alternatives, where each alternative has multiple performance measures evaluated by stochastic simulations. Under limited simulation resources, we propose an efficient algorithm for solving this problem based on a statistical hypothesis test. Using the test, the proposed algorithm evaluates the uncertainty of each design based on the observed simulation results to identify whether the selected Pareto set is accurate. Based on the evaluated uncertainty, the algorithm assigns additional resources to the designs to maximize the accuracy of the selected Pareto set. Applying the sequential procedure, the algorithm increases the precision of the observed information selectively and gradually. Several experiments, including a practical case study, demonstrated its improved efficiency compared to the existing algorithms in the literature. This improved efficiency, along with low complexity and high robustness to noise, allows the proposed algorithm to be effectively applied to practical system designs. [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]

The following paper presents methods for application of mode superposition to the analytical part of the hybrid simulation, when Real Time Finite Element Method is used to model the analytical substructure. Introducing mode superposition can bring significant computation time decrease in real time computations, by performing some calculations offline. The paper describes requirements, methods and algorithms for introducing mode superposition to explicit and implicit integration schemes. In explicit schemes mode superposition allows to increase the minimum time step often enabling real time computations that were not possible before. In implicit schemes mode superposition allows to reduce the time step of the analysis (and therefore increase accuracy). Numerical examples proving the effectiveness of the presented algorithms are given. [ABSTRACT FROM AUTHOR]

Driven by increasing user requirements and expectations, the fast development of telecommunications networks brings new challenging optimization problems. One of them is routing and spectrum allocation (RSA) of three types of network flows (unicast, anycast, multicast) in elastic optical networks (EONs) implementing dedicated path protection (DPP). In the paper, we model this problem as integer linear programming (ILP) and we introduce two new optimization approaches--a dedicated heuristic algorithm and a column generation (CG)-based method. Then, relying on extensive simulations, we compare algorithm performance with reference methods and evaluate CG efficiency in detail. The results show that the proposed CG method significantly outperforms reference algorithms and achieves results very close to optimal ones (the average distance to optimal results was at most 2.1%). [ABSTRACT FROM AUTHOR]

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm. [ABSTRACT FROM AUTHOR]

Background: Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest. Methods: In this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation is presented. Additional contributions include the development of a criterion for the selection of state-variable elimination via conservation analysis and use of an 'averaged' lumping inverse. This combined algorithm is highly automatable and of particular applicability in the context of 'controlled' biochemical networks. Results: The algorithm is demonstrated here via application to two examples; an 11 dimensional model of bacterial chemotaxis in Escherichia coli and a 99 dimensional model of extracellular regulatory kinase activation (ERK) mediated via the epidermal growth factor (EGF) and nerve growth factor (NGF) receptor pathways. In the case of the chemotaxis model the algorithm was able to reduce the model to 2 state-variables producing a maximal relative error between the dynamics of the original and reduced models of only 2.8% whilst yielding a 26 fold speed up in simulation time. For the ERK activation model the algorithm was able to reduce the system to 7 state-variables, incurring a maximal relative error of 4.8%, and producing an approximately 10 fold speed up in the rate of simulation. Indices of controllability and observability are additionally developed and demonstrated throughout the paper. These provide insight into the relative importance of individual reactants in mediating a biochemical system's input-output response even for highly complex networks. Conclusions: Through application, this paper demonstrates that combined model reduction methods can produce a significant simplification of complex Systems Biology models whilst retaining a high degree of predictive accuracy. In particular, it is shown that by combining the methods of proper lumping and empirical balanced truncation it is often possible to produce more accurate reductions than can be obtained by the use of either method in isolation [ABSTRACT FROM AUTHOR]

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault. [ABSTRACT FROM AUTHOR]

Non-circular properties of non-circular signals can be used to improve the performance of the direction-of-arrival (DOA) estimation. However, most ready-made algorithms are not applicable to the general case in which both non-circular and circular signals exist. In this paper, we present a novel DOA estimation algorithm for mixed signals, namely MS-MUSIC (Mixed Signals-Multiple Signals Classification), which can deal with the two kinds of signals simultaneously. And on this basis, we derive the Cramer-Rao Lower Bound (CRLB) of the azimuth and elevation estimation. The effectiveness of the algorithm is confirmed by the simulation results. Meanwhile, it acquires higher accuracy than the traditional algorithms. [ABSTRACT FROM AUTHOR]

Recently the integrated modular avionics (IMA) architecture has been widely adopted by the avionics industry due to its strong partition mechanism. Although the IMA architecture can achieve effective cost reduction and reliability enhancement in the development of avionics systems, it results in a complex allocation and scheduling problem. All partitions in an IMA system should be integrated together according to a proper schedule such that their deadlines will be met even under the worst case situations. In order to help provide a proper scheduling table for all partitions in IMA systems, we study the schedulability of independent partitions on a multiprocessor platform in this paper. We firstly present an exact formulation to calculate the maximum scaling factor and determine whether all partitions are schedulable on a limited number of processors. Then with a Game Theory analogy, we design an approximation algorithm to solve the scheduling problem of partitions, by allowing each partition to optimize its own schedule according to the allocations of the others. Finally, simulation experiments are conducted to show the efficiency and reliability of the approach proposed in terms of time consumption and acceptance ratio. [ABSTRACT FROM AUTHOR]

In this paper, the reconstruction of perfectly electric conductor (PEC) scatterers with transverse electric (TE) wave illumination is investigated using the continuous-type subspace-based optimization method (SOM). The PEC scatterers can be of arbitrary locations, shapes and quantities and no prior information about the scatterers is required in the reconstructions. Compared with the transverse magnetic (TM) counterpart, the TE case is not only mathematically more complex, but also numerically more demanding in the sense that the triangular basis is used to represent induced electric current. Moreover, under better mesh strategy and more strict objective function, numerical simulations for arbitrary PEC scatterers generate satisfactory results and are well robust against noise. The algorithm is also examined under the experimental scattering data and still obtains good reconstruction results. [ABSTRACT FROM PUBLISHER]

For different applications with different requirements, many synthetic aperture radar (SAR) modes have been developed in the literature, such as, Terrain Observation by Progressive Scans (TOPS) SAR and sliding spotlight SAR. In this paper, we call TOPS SAR, sliding spotlight SAR, and spotlight SAR as beam steering SAR (BS-SAR for short). Comparing with stripmap SAR, BS-SAR can obtain a wide diversity of resolutions by increasing or reducing the azimuth synthetic time. Traditional polar formation algorithm (PFA) is an efficient algorithm which is mainly developed for spotlight SAR. The PFA has been validated to obtain well-focused results of raw data. In this paper, we extend the traditional PFA to process sliding spotlight SAR and TOPS SAR data, and we call it generalized PFA (GPFA). Comparing with the traditional PFA, GPFA contains a different azimuth deramping function and an additional azimuth scaling operation. The simulated and real data are used to validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The protection effectiveness is an important metric to judge whether a security system is good or not. In this paper, a security system deployed in a guard field is regarded abstractly as a security network. A quantitative protection effectiveness evaluation method based on entropy theory is introduced. We propose the protection intensity model, which can be used to calculate the protection intensity of a stationary or moving object provided by a security system or a security network. Using the protection intensity model, an algorithm, specifically for finding the minimal protection intensity paths of a field deployed multiple security system, is also put forward. The minimal protection intensity paths can be considered as the effectiveness measure of security networks. Finally, we present the simulation of the methods and models in this paper. [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]

Absolute adaptive current statistical (AACS) model and modified strong tracking unscented filter (MSTUF) are proposed for maneuvering target tracking (MTT) under nonlinear measurement in this paper. The key point of the AACS model is to associate the instantaneous acceleration variance with some elements of state covariance matrix by constructing acceleration increment models of the acceleration limit and acceleration mean in the CS model, while the maneuvering frequency can adjust itself according to the change of the measurement residual. MSTUF is proposed for high maneuver tracking under nonlinear measurement by incorporating the modified strong tracking filter (STF) into the unscented filter (UF). Since the state covariance, process noise covariance and maneuvering frequency can adjust themselves jointly according to the residual, the proposed algorithm, called the AACS-MSTUF, has a good performance on both maneuver and non-maneuver. Simulation results indicate that the overall performance of the proposed algorithm is better than the interacting multiple-model unscented filter (IMM-UF), UF and original strong tracking unscented filter (STUF) based on the CS model (CS-STUF) when considering tracking accuracy, stability, convergence and computational complexity. [ABSTRACT FROM AUTHOR]

Model checking is a formal method for verifying whether the system satisfies a user-defined specification. Compared to simulation, model checking is restricted in capacity. On the other hand, simulation is weak in detecting bugs that require long and complex sequences of events to be exposed. This paper combines model checking and simulation in an abstraction-refinement scheme to mitigate the problems of both methods. Abstraction refinement iteratively constructs a simplified model to verify the original model. While a simplified model mitigates the weakness of model checking, the set of simplified error traces model helps guide simulation toward deep bugs. In abstraction refinement, concretization—a process of deriving an error trace in the original model from the abstract ones—is used to invalidate spurious abstract error traces or to refute a property. In this paper, we describe a novel concretization algorithm that combines simulation with satisfiability to efficiently refute properties with very long error traces. [ABSTRACT FROM AUTHOR]

Different from other electronics measurement, the bandwidth of the instrument can affect the test results more seriously in jitter and bit-error rate (BER) test. This paper analyzes the effect on the jitter and BER test results caused by instrument bandwidth in high-speed serial interconnection. An algorithm is presented to measure and estimate the equivalent frequency response of instruments. Computed data-dependent jitter (DDJ) results are produced based on the S-parameter of the serial interconnected channels and the equivalent frequency response of instruments, which are perfectly consistent with actual DDJ measure results. These methods can be used to measure and calibrate the instrument bandwidth effect precisely in jitter and BER test. At last, the paper gives the results of the instrument bandwidth effect for three channels and a set of instruments according to simulation, and presents a new concept “equivalent bandwidth” to roughly estimate the test error of DDJ caused by instrument bandwidth. [ABSTRACT FROM AUTHOR]

This paper studies a multiple-input multiple-output (MIMO) broadcast relay channel (BRC), in which a multiple-antenna base station (BS) communicates with multiple-antenna users through an infrastructure-based multiple-antenna relay station (RS). Applying dirty paper coding (DPC) at the BS and linear processing at the RS, our aim is to find the input covariance matrices and the RS beamforming matrix that maximize the system sum-rate. To solve this non-convex problem, a more tractable dual multiple access relay channel (MARC) is investigated and an alternating-minimization algorithm is proposed. Furthermore, the mapping from the resulting covariance matrices for the MARC to the covariance matrices for the BRC is derived. Unlike other existing single-antenna-user schemes, our solution is applicable to a more general network with any number of antennas at the users. Compared with two such single-antenna-user schemes, simulations show that the proposed scheme outperforms the all-pass relay design and performs similarly to the SVD-relay design. Moreover, the proposed design performs close to the sum-rate upper bound with the performance gap decreasing with increasing number of antennas per user. It is also observed that having more antennas at the RS than at the BS is desirable for better system performance. [ABSTRACT FROM PUBLISHER]

Purpose: This paper introduces a procedure for solving the staffing problem in a service system (i.e., determining the number of servers for each staffing period). Design/methodology: The proposed algorithm combines the use of queueing theory to find an initial solution with the use of simulation to adjust the number of servers to meet previously specified target non-delay probabilities. The basic idea of the simulation phase of the procedure is to successively fix the number of servers from the first staffing period to the last, without backtracking. Findings: Under the assumptions that the number of servers is not upper-bounded and there are no abandonments and, therefore, no retrials, the procedure converges in a finite number of iterations, regardless of the distributions of arrivals and services, and requires a reasonable amount of computing time. Originality/value: The new procedure proposed in this paper is a systematic, robust way to find a good solution to a relevant problem in the field of service management and it is very easy to implement using no more than commonly accessible tools. [ABSTRACT FROM AUTHOR]

The quality of an ultrasonic array image depends on accurate information about its acoustic properties. Inaccurate acoustic properties can cause image degradation such as blurring, mislocation of reflectors, and the introduction of artifacts. In this paper, for the specific case of an inhomogeneous and anisotropic austenitic steel weld, Monte Carlo Markov Chain (MCMC) inversion is used to estimate unknown acoustic properties from array data. The approach uses active beacons that transmit ultrasound through the anisotropic weld; the ultrasound is then captured by a receiving array. A forward model of the ultrasonic array data is then optimized with respect to the experimental data using an MCMC inversion. The result of this process is the extraction of a material property map that describes the anisotropy distribution within the weld region. These extracted material properties are then used within an imaging algorithm?the total focusing method in this paper?to produce autofocused images. This MCMC inversion approach is first applied to simulated data to test the convergence, robustness, and accuracy of the method and its implementation. The extracted weld map is used to show improved imaging of defects within the weld, relative to an image formed assuming a constant velocity. Finally, the MCMC inversion approach is used on experimental data from a 110-mm-thick steel plate containing an austenitic weld. Here the extracted weld map is used to show that defect location errors of greater than 5 mm are reduced to around 2 mm when the extracted weld map is used [ABSTRACT FROM PUBLISHER]

Navigation employing low cost MicroElectroMechanical Systems (MEMS)sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. [ABSTRACT FROM AUTHOR]

A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller. [ABSTRACT FROM PUBLISHER]

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]

Target field (TF) method is very important for design of shim and gradient coils which have been used in magnetic resonance imaging (MRI) and other applications. Many works based on TF have been proposed. To restrict the current distribution on cylindrical coils with a given finite length, the current density is expanded by trigonometric series and the unknown coefficients are estimated based on the standard Least Square (LS) with L2 norm regularization (LS-L2). In this paper, we propose to estimate the coefficients using LS with L1 norm regularization (LS-L1), also named Least Absolute Shrinkage and Selection Operator (LASSO), which is solved via Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The proposed method can obtain sparse solutions and better approximations, which can be used to design cylindrical shim and gradient coils for MRI. Simulation results show that the proposed LS-L1 method performs better than the LS-L2 TF method. [ABSTRACT FROM PUBLISHER]

This paper focuses on a lot merging–splitting problem in a semiconductor wafer fabrication facility in which a relatively large number of wafer types are produced according to orders with different due dates. In the fab, two or more lots can be merged into a single lot if routes and all processing conditions of the lots are the same for a number of subsequent operations, and the merged lot is split into the original lots at the point where the routes or processing conditions become different. We suggest lot merging–splitting algorithms to reduce the total tardiness of the orders and the cycle times of the lots. The suggested algorithms are evaluated through a series of simulation experiments and the result shows that the algorithms work better than a method used in a real fab. [ABSTRACT FROM PUBLISHER]

In this paper we introduce the transparent dead leaves (TDL) random field, a new germ-grain model in which the grains are combined according to a transparency principle. Informally, this model may be seen as the superposition of infinitely many semitransparent objects. It is therefore of interest in view of the modeling of natural images. Properties of this new model are established and a simulation algorithm is proposed. The main contribution of the paper is to establish a central limit theorem, showing that, when varying the transparency of the grain from opacity to total transparency, the TDL model ranges from the dead leaves model to a Gaussian random field. [ABSTRACT FROM AUTHOR]

In this paper, the directional carrier sense multiple access/collision avoidance (CSMA/CA) protocol in the immediate acknowledgement mode for IEEE 802.15.3c is analyzed under saturation environments. For the analysis, a sensing region and an exclusive region with a directional antenna are computed probabilistically and a Markov chain model in which the features of IEEE 802.15.3c and the effects of using directional antennas are incorporated is analyzed. An algorithm to find the maximal number of concurrently transmittable frames is proposed. The system throughput and the average transmission delay are obtained in closed forms. The numerical results show the impact of directional antennas on the CSMA/CA media access control (MAC) protocol. For instance, the throughput with a small beamwidth of antenna is more than ten times larger than that for an omnidirectional antenna. The overall analysis is verified by a simulation. The obtained results will be helpful in developing an MAC protocol for enhancing the performance of mmWave wireless personal area networks. [ABSTRACT FROM AUTHOR]

Resource scheduling, or the allocation of resources over time, is a challenging problem in large-scale or multiple-project environments. Traditional network scheduling techniques are ineffective in modeling the dynamic nature and resource interactions of large or multiunit projects. This paper presents a simulation-based auction protocol (SBAP) to solve resource scheduling problems in large-scale construction projects. SBAP is a hybrid framework that integrates multi agent resource allocation (MARA) in a simulation environment. SBAP deploys a centralized resource allocation approach, referred to as an auction protocol, whereby agents bid on different combinations of resources at the start of a simulation cycle. Agents attempt to improve their individual welfare by acquiring a combination of resources; an auctioneer looks at the entire system and allocates resources to the agents using a combinatorial algorithm to maximize an overall objective function (e.g., maximizing the system's revenue or minimizing total costs). The auction is repeated on a regular basis. Simulation is also employed in large-scale projects to track the availability of resources, capture and release the resources, and satisfy constraints of the problem. This paper demonstrates the architecture of the SBAP framework and discusses implementation of SBAP in a real case study of crane allocation in an industrial project. [ABSTRACT FROM AUTHOR]

This paper describes three data driven methods to monitor electromechanical oscillations in interconnected power system operation. The objective is to compare and contrast the performance of the methods. The accuracy of damping ratio and frequency of oscillations are the measures of the performance of the algorithms. The advantages and disadvantages of various techniques and their limitations to measurement noise have been considered while assessing performance. The target frequency and damping are computed using the Nordic power system simulation model. [ABSTRACT FROM PUBLISHER]

This paper presents a method of approaching the attitude control of an autonomously hovering unmanned helicopter system. The proportional-integral sliding mode controller (PISMC) features a combination of the conventional sliding mode control (SMC) and the integral sliding mode control (ISMC). By applying the PISMC algorithm, the controller is capable of providing stable roll and pitch control of the main rotor simultaneously. To show how PISMC may improve system performance, this paper develops an experimental unmanned helicopter system to assess the performance of the proposed controller. The results of the simulation show that the tracking error of the PISMC is not only smaller but also converges more quickly than that of the conventional SMC. Resulting roll and pitch controllers are successfully verified in computer simulations and actual flight tests. The flight test results presented in the paper are found to be consistent with the simulation results. [ABSTRACT FROM AUTHOR]

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, nonlinear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. The proposed algorithm interprets the problem as an unexplored terrain for climbing. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The efficiency of the proposed algorithm is demonstrated by application to conventional test functions. In this paper, the simulation results show that skewing does not necessarily reduce the cogging torque but may cause it to increase for certain pole-arc to pole-pitch ratio. The developed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one. [ABSTRACT FROM AUTHOR]

This paper describes new strategies to improve the transient response time of harmonic detection using adaptive filters applied to shunt active power filters. Two cases are presented and discussed, both using an adaptive notch filter, but one uses the least mean square algorithm to adjust the coefficients and the other uses the recursive least squares algorithm. The synchronization of the adaptive notch filter orthogonal input signals, which are generated by the Clarke transformation of the load currents, is achieved automatically without the need of a phase-locked loop. This procedure significantly reduces the real-time computation burden. Simulations using Matlab/Simulink are presented to clarify the algorithm, and practical implementation is performed using the DSP Texas Instruments TMS320F2812. The experimental results are presented and discussed. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging modality of determining imaging the interior conductivity distribution within an object. In the paper, generalized back projection algorithm (GBPA) is proposed for EIT image reconstruction. The algorithm is based on node back projection algorithm (NBPA), and extends projection range to arbitrary part of the object. To evaluate the proposed method, GBPA is first applied to image reconstruction using a simulated cylinder model. Compared with conventional back projection algorithm (BPA) and NBPA, the images reconstructed by GBPA are more accurate in terms of several metrics used in the paper. In addition, we present image reconstruction results using a 128-channel EIT system to demonstrate the performance of GBPA in real experiments. [ABSTRACT FROM PUBLISHER]

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]

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented. [ABSTRACT FROM AUTHOR]

This paper presents an online procedure for the automatic search of the maximum-torque-per-ampere operating region for a synchronous reluctance motor. The algorithm is based on a signal-injection method with a random-based perturbation pattern applied to a common direct-torque-controlled drive. Among motor parameters, only the stator resistance is required to perform the automatic procedure. Simulations and experimental results are presented in the paper, demonstrating the benefits of the proposed algorithm. The solution is easily extended to any ac drive. [ABSTRACT FROM PUBLISHER]