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This paper concerns the correction of refracted propagation effects for airborne radar tracking. An algorithm based on the Kalman filter (KF) is developed that compensates for range and elevation refraction errors, which were analytically derived using a simplified tropospheric model. In particular, the refraction error model applies to a spherically stratified troposphere and does not include the effects of azimuthal refraction errors, small-scale fluctuations, or ducting. The effectiveness of the proposed tracking algorithm is assessed in the presence of tropospheric parameter mismatch and measurement noise. Both theoretical and numerical results are described for a medium range scenario, and a quasi-ellipsoidal racetrack is chosen for the air platform that carries the radar. [ABSTRACT FROM PUBLISHER]

In this paper, an algorithm is proposed to extract two illuminant invariant chromaticity features from three image sensor responses. The algorithm extracts these chromaticity features at pixel level and therefore can perform well in scenes illuminated with nonuniform illuminant. An approach is proposed to use the algorithm with cameras of unknown sensitivity. The algorithm was tested for separability of perceptually similar colors under the International Commission on Illumination standard illuminants and obtained a good performance. It was also tested for color-based object recognition by illuminating objects with typical indoor illuminants and obtained a better performance compared to other existing algorithms investigated in this paper. Finally, the algorithm was tested for skin detection invariant to illuminant, ethnic background and imaging device. In this investigation, daylight scenes under different weather conditions and scenes illuminated by typical indoor illuminants were used. The proposed algorithm gives a better skin detection performance compared to widely used standard color spaces. Based on the results presented, the proposed illuminant invariant chromaticity space can be used for machine vision applications including illuminant invariant color-based object recognition and skin detection. [ABSTRACT FROM AUTHOR]

In this paper, an error surface-considered modeling algorithm for quarter-pixel motion estimation during video encoding is presented. We previously proposed two algorithms: a model-based quarter-pixel motion estimation (MBQME) algorithm and a hierarchical modelbased quarter-pixel motion estimation (HMBQME) algorithm. MBQME is an interpolation-free algorithm that has a minimum motion estimation time, while HMBQME has selective interpolation according to the decision process. Consequently, the peak signal-to-noise ratio (PSNR) for HMBQME is better than that of MBQME, but the motion estimation time is also increased. As an alternative method, we propose an error surface-considered modeling algorithm. In this scheme, the tendency of the error surface is first assessed. Using the strength of the edge at the error surface, we can classify the error surface region as plain or textured. For plain regions, interpolation-free and simplestructured modeling is appropriate for the quarter-pixel motion estimation method. In this case, we modified conventional mathematical modeling algorithm suitable for plain region. For textured regions, additional interpolation is needed for more accurate modeling. We calculate the half-pixel SAD values and perform more accurate modeling so as to find the best motion vector (MV). The experimental results show that the proposed scheme has better PSNR performance than any previous algorithms with minimum motion estimation time. [ABSTRACT FROM PUBLISHER]

Evolutionary algorithms can be applied to a variety of constrained network communication problems with centric type models. This paper shows that with real-world complex network communication problems of this type, sophisticated statistical search is required. This situation occurs due to the fact that these optimization problems are at least NP-complete. In order to appreciate the formal modeling of realistic communication networks, historical network design problems are presented and evolved into more complex real-world models with associated deterministic and stochastic solution approaches discussed. This discussion leads into the design of an innovative multiobjective evolutionary algorithm (MOEA) to solve a very complex network design problem variation called the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid real-world communication architecture as reflected in real-world network centric models with directional communications, multiple objectives including costs, delays, robustness, vulnerability, and operating reliability within network constraints. Nodes in such centric systems can have multiple and varying link capacities as well as rates and information (commodity) quantities to be sent and received. Each commodity can also have independent prioritized bandwidth and spectrum requirements. The nondominated sorting genetic algorithm (NSGA-II) is selected as the MOEA framework which is modified and parallelized to solve the generic MCNDP. Since the MCNDP is highly constrained but with an enormous number of possible network communication topologies, a novel initialization procedure and mutation method are integrated resulting in reduced search space. Empirical results and analysis indicate that effective topological Pareto solutions are generated for use in highly constrained, communication-based network design. [ABSTRACT FROM AUTHOR]

In the coming years, plug-in hybrid electric vehicles (PHEVs) will strongly penetrate the French car fleet. Their impact on each element of the electric distribution grid needs to be estimated. In this paper, the impact of PHEVs on the life duration of the medium-voltage/low-voltage (LV) transformer using a thermal model to estimate the hot-spot temperature is studied. Based on probabilistic algorithms, three penetration rates of PHEVs are studied. A stochastic method is applied to construct domestic daily load profiles of houses constituting an LV residential electric grid supplied by the transformer. Correlated with the initial load rate (without PHEVs) of the transformer, two indices are proposed; the DTR\PHEV\X and VTR\PHEV\X to calculate, respectively, the life duration and aging rate of transformer in the presence of PHEVs. It is shown that the aging rate of the transformer is quadratic in the presence of PHEVs. [ABSTRACT FROM AUTHOR]

Coordination of directional overcurrent relays in a multiloop subtransmission or distribution network is formulated as an optimization problem. In this paper, the coordination of directional overcurrent relays is formulated as a mixed-integer nonlinear programming problem and is then solved by a new seeker optimization technique. Based on the act of human searching, in the proposed seeker technique, the search direction and step length are determined in an adaptive way. The proposed method is implemented in three different test cases. The results are compared with previously proposed analytic and evolutionary approaches. [ABSTRACT FROM AUTHOR]

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data, which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm, where each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements that it provides over existing techniques for model error compensation in SAR. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the image blind super-resolution (BSR) problem in the case of affine interframe motion. Although the tasks of image registration, blur identification, and high-resolution (HR) image reconstruction are coupled in the imaging process, when dealing with nonisometric interframe motion or without the exact knowledge of the blurring process, classic SR techniques generally have to tackle them (maybe in some combinations) separately. The main difficulty is that state-of-the-art deconvolution methods cannot be straightforwardly generalized to cope with the space-variant motion. We prove that the operators of affine warping and blur commute with some additional transforms and derive an equivalent form of the BSR observation model. Using this equivalent form, we develop an iterative algorithm to jointly estimate the triple-coupled variables, i.e., the motion parameters, blur kernels, and HR image. Experiments on synthetic and real-life images illustrate the performance of the proposed technique in modeling the space-variant degradation process and restoring local textures. [ABSTRACT FROM AUTHOR]

This paper deals with the exponential stabilization problem for a class of nonlinear spatially distributed processes that are modeled by semilinear parabolic partial differential equations (PDEs), for which a finite number of actuators are used. A fuzzy control design methodology is developed for these systems by combining the PDE theory and the Takagi–Sugeno (T–S) fuzzy-model-based control technique. Initially, a T–S fuzzy parabolic PDE model is proposed to accurately represent a semilinear parabolic PDE system. Then, based on the T–S fuzzy model, a Lyapunov technique is used to design a continuous fuzzy state feedback controller such that the closed-loop PDE system is exponentially stable with a given decay rate. The stabilization condition is presented in terms of a set of spatial differential linear matrix inequalities (SDLMIs). Furthermore, a recursive algorithm is presented to solve the SDLMIs via the existing linear matrix inequality optimization techniques. Finally, numerical simulations on the temperature profile control of a catalytic rod are given to verify the effectiveness of the proposed design method. [ABSTRACT FROM PUBLISHER]

The direct torque control (DTC) technique of permanent-magnet synchronous motors (PMSMs) receives increasing attention due to its advantages in eliminating the current controllers and quicker dynamic response, compared with other motor control algorithms. However, high torque and stator flux ripples remain in the system when using DTC technologies. This means large stator voltage and current harmonic contents exist in the PM motors. Since the variation of motor electromagnetic torque is related to the voltages that are applied to the motor, by analyzing the relationships between stator flux, torque, and voltages, a PMSM torque predictive control scheme is proposed in this paper. In each digital signal processor cycle, the optimized voltage is utilized to reduce torque ripple, and the voltage vector angle is determined by the output of torque and flux hysteresis controllers. The proposed scheme is simulated and experimentally verified. Both simulation and experimental results have shown that low torque ripple and reduced stator current harmonics are achieved by using the proposed scheme. [ABSTRACT FROM PUBLISHER]

Fire-flame detection using a video camera is difficult because a flame has irregular characteristics, i.e., vague shapes and color patterns. Therefore, in this paper, we propose a novel fire-flame detection method using fuzzy finite automata (FFA) with probability density functions based on visual features, thereby providing a systemic approach to handling irregularity in computational systems and the ability to handle continuous spaces by combining the capabilities of automata with fuzzy logic. First, moving regions are detected via background subtraction, and the candidate flame regions are then identified by applying flame color models. In general, flame regions have a continuous irregular pattern; therefore, probability density functions are generated for the variation in intensity, wavelet energy, and motion orientation and applied to the FFA. The proposed algorithm is successfully applied to various fire/non-fire videos, and its detection performance is better than that of other methods. [ABSTRACT FROM AUTHOR]

Electromagnetic scattering by penetrable bodies often is modelled by the Poggio-Miller-Chan-Harrington-Wu-Tsai (PMCHWT) integral equation. Unfortunately the spectrum of the operator involved in this equation is bounded neither from above or below. This implies that the equation suffers from dense discretization breakdown; that is, the condition numbers of the matrix resulting upon discretizing the equation rise with the mesh density. The electric field integral equation, often used to model scattering by perfect electrically conducting bodies, is susceptible to a similar breakdown phenomenon. Recently, this breakdown was cured by leveraging the Calderón identities. In this paper, a Calderón preconditioned PMCHWT integral equation is introduced. By constructing a Calderón identity for the PMCHWT operator, it is shown that the new equation does not suffer from dense discretization breakdown. A consistent discretization scheme involving both Rao-Wilton-Glisson and Buffa-Christiansen functions is introduced. This scheme amounts to the application of a multiplicative matrix preconditioner to the classical PMCHWT system, and therefore is compatible with existing boundary element codes and acceleration schemes. The efficiency and accuracy of the algorithm are corroborated by numerical examples. [ABSTRACT FROM AUTHOR]

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis. [ABSTRACT FROM AUTHOR]

An accurate estimation of critical load levels (CLLs) is beneficial in evaluating the confidence level for the predicted locational marginal prices (LMPs) in power markets. This paper proposes an efficient algorithm to quickly identify CLLs under the with-loss-DCOPF framework. The proposed method approximates the trajectory of DCOPF solutions with respect to the system load variations. The quadratic coefficients of the trajectory can be efficiently obtained by solving a small-scale nonlinear system of equations formed by the variable substitution method, which does not require additional OPF runs and is thus more computationally efficient than existing methods such as binary search and interpolation. The obtained trajectory can be used for a quick calculation of OPF solutions without re-running OPF. Further, through trajectory extrapolation, new congestions and other binding constraints can be identified simultaneously with the CLL predictions. Particularly, the proposed method is not restricted to a specific DCOPF-based model and can be easily applied to other DCOPF-based variants. Numeric studies on a revised IEEE 30-bus system are presented to illustrate the high computational efficiency and accuracy of the approach. It is further tested on the IEEE 118-bus system using four different DCOPF-based models to illustrate its model adaptability. [ABSTRACT FROM PUBLISHER]

A new digital signal processing approach to shaping intersymbol interference (ISI) and static mismatch errors simultaneously in oversampled multi-level digital to analog converters (DAC) has recently been proposed. In this paper, a mathematical framework is established for analyzing ISI errors as well as comparing the ISI sensitivities of different mismatch shaping algorithms. The framework is used to analyze the fundamental problems of popularly used algorithms such as data-weighted-averaging (DWA) in the presence of nonlinear ISI: Large-signal even-order distortion and frequency modulated harmonics at low signal levels. The new ISI-shaping algorithm results in significant improvement over previous schemes including the modified Mismatch Shaper (MMS) which also addresses ISI error. The new ISI shaper, while increasing the digital complexity, practically eliminates the need for conventional ISI mitigation techniques such as time consuming, layout-critical, non-automated and process specific analog design methods. The advantages of ISI shaping is further verified on an experimental audio DAC with simple non-return-to-zero (NRZ) current steering segments implemented in a 45 nm CMOS process and running off a single-phase clock of only 3.072 MHz. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the simultaneous estimation of frequency and power components is investigated. It is solved through a new recursive estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. By this, a simple nonlinear parameter model for the instantaneous power, taking into account the fundamental components of voltage and current, is used as a starting point for the power components and frequency estimation. In the model, the system frequency is considered as an unknown model parameter, and it is simultaneously estimated with other unknown parameters. This resulted in an efficient numerical algorithm for the measurement of power components, which is not sensitive to variations of the system frequency. This is an important achievement, which improves the accuracy of the method during network transients. The new estimator has been improved by the strategy for tuning its forgetting factor according to the system dynamics, and it is tested through computer simulations and by using data records obtained under laboratory conditions. The algorithm was developed for a single-phase system, and the approach might be easily extended to multiphase systems. [ABSTRACT FROM AUTHOR]

Motion capture provides the best interface to the understanding of trajectory planning and formations in the central nervous system, which has enormously benefited the research in interactive game and learning, animation, film special effects, health care, and navigation. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors on chip, a ubiquitous real-time low-cost human motion capture system that uses wearable microsensors becomes possible. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and present a novel upper limb motion estimation algorithm by hierarchical fusion of sensor data and human skeleton constraints. Our method represents the orientations of upper limb segments in quaternion, which is computationally effective and can avoid the singularity problem. To address the nonlinear human body segment motion, a particle filter is proposed to fuse inertial and magnetic sensor data. To compensate for the drift, which is the most challenging issue in motion estimation using inertial sensors, we present a novel solution by modeling the geometrical constraint in elbow joint and fuse the constraint in the framework of particle filter to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results compared to the ground truth. [ABSTRACT FROM AUTHOR]

Antilock braking systems (ABSs) are usually designed based on controlling the wheel slip ratio so as to maintain each wheel in a presumed stable region. Since the optimal slip ratio (which results in maximum tire–road friction) varies with the type of road, current methods are not efficient in the sense of achieving the shortest possible stopping distance. This paper introduces an efficient ABS, in which the brake commands directly maximize the longitudinal component of tire–road friction at each wheel of the vehicle independently. The tire–road friction is estimated using a torque balance equation at each wheel, and within those equations, real-time estimates of the effective radius of tire are used. A step-by-step algorithm for computing the brake commands that maximize the tire–road friction is also presented. Three challenging braking scenarios were tested using the comprehensive CarSim simulation environment. The results show that, in comparison to conventional ABS, our method significantly reduces the stopping distance and improves the vehicle stability. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of optimal trajectory generation for a team of heterogeneous robots moving in a plane and tracking a moving target by processing relative observations, i.e., distance and/or bearing. Contrary to previous approaches, we explicitly consider limits on the robots’ speed and impose constraints on the minimum distance at which the robots are allowed to approach the target. We first address the case of a single tracking sensor and seek the next sensing location in order to minimize the uncertainty about the target’s position. We show that although the corresponding optimization problem involves a nonconvex objective function and a nonconvex constraint, its global optimal solution can be determined analytically. We then extend the approach to the case of multiple sensors and propose an iterative algorithm, i.e., the Gauss–Seidel relaxation (GSR), to determine the next best sensing location for each sensor. Extensive simulation results demonstrate that the GSR algorithm, whose computational complexity is linear in the number of sensors, achieves higher tracking accuracy than gradient descent methods and has performance that is indistinguishable from that of a grid-based exhaustive search, whose cost is exponential in the number of sensors. Finally, through experiments, we demonstrate that the proposed GSR algorithm is robust and applicable to real systems. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of providing aggregated regulation services with small loads, such as water heaters or air conditioners. A direct-load control algorithm is presented to aggregate the water heater load for the purpose of regulation. A dual-element electric water heater model is developed, which accounts for both thermal dynamics and users' water consumption. A realistic regulation signal was used to evaluate the number of water heaters needed and the operational characteristics of a water heater when providing 2-MW regulation service. Modeling results suggest that approximately 33 000 water heaters are needed to provide a 2-MW regulation service 24 hours a day. However, if water heaters only provide regulation from 6:00 to 24:00, approximately 20 000 will be needed. Because the control algorithm has considered the thermal setting of the water heater, customer comfort is maintained. Therefore, the aggregated regulation service provided by water heater loads can become a major source of revenue for load-serving entities when the smart grid enables the direct load control. [ABSTRACT FROM PUBLISHER]

This paper presents a method for determining the double outage contingencies that threaten the system without solving the full contingency set. Two methods for contingency screening with complementary properties are presented. The results of the algorithms are compared to the full double outage contingency analysis results for a large North American case. The results show that the screening algorithms are able to detect nearly all of the contingencies that will result in violations, while requiring only a small fraction of the contingencies to be solved. [ABSTRACT FROM PUBLISHER]

This paper outlines the use of phasor measurement unit (PMU) records to validate models of fixed speed induction generator (FSIG)-based wind farms during frequency transients. Wind turbine manufacturers usually create their own proprietary models which they can supply to power system utilities for stability studies, subject to confidentiality agreements. However, it is desirable to confirm the accuracy of supplied models with measurements from the particular installation, in order to assess their validity under real field conditions. This is prudent due to possible changes in control algorithms and design retrofits, not accurately reflected or omitted in the supplied model. One important aspect of such models, especially for smaller power systems with limited inertia, is their accuracy during system frequency transients. This paper, therefore, assesses the accuracy of FSIG models with regard to frequency stability, and hence validates a subset of the model dynamics. Such models can then be used with confidence to assess wider system stability implications. The measured and simulated response of a wind farm using doubly fed induction generator (DFIG) technology is also assessed. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel approach to optimize a generator's profit in an offer-based electricity market cleared by an optimal power flow (OPF) program. A generator's offer is optimized based on its transmission-constrained residual demand derivative (TCRDD), which avoids representing the full network model in the optimization. The TCRDD can be easily calculated based on a solved OPF without changing existing OPF algorithms and programs. As demonstrated with an IEEE 118-bus example, the TCRDD approach is computationally efficient. The TCRDD approach can help market participants to bid into electricity markets, and help market monitors to diagnose bidding behaviors in the presence of transmission constraints. [ABSTRACT FROM PUBLISHER]

Presenting a more efficient mathematical model for the problem of integrating lot sizing, loading, and scheduling in capacitated flexible flow shops with sequence-dependent setups is the main novelty of this paper. In comparison to the former model, fewer variables and constraints of the current model makes it easier to be solved. Comparison between this model and the former one shows the superiority of this model. To validate the new model, an efficient lower bound has been provided. Mixed integer programming-based algorithms have been used to solve this model. [ABSTRACT FROM AUTHOR]

Requirements for system availability for ultrahigh reliability electronic systems such as airborne and space electronic systems are driving the need for advanced health monitoring techniques for the early detection of the onset of damage. Aerospace electronic systems usually face a very harsh environment, requiring them to survive the high strain rates, e.g., during launch and reentry, and thermal environments, including extremely low and high temperatures. Traditional health monitoring methodologies have relied on reactive methods of failure detection often providing little or no insight into the remaining useful life of the system. In this paper, a mathematical approach for the interrogation of the system state under cyclic thermomechanical stresses has been developed for six different lead-free solder alloy systems. Data have been collected for leading indicators of failure for alloy systems, including \Sn3\Ag0.5\Cu, \Sn0.3\Ag0.7\Cu, \Sn1\Ag0.5\Cu, \Sn0.3\Ag0.5\Cu0.1\Bi, \Sn0.2\Ag0.5\Cu0.1\Bi0.1\Ni, and 96.5\Sn3.5\Ag second-level interconnects under the application of cyclic thermomechanical loads. The methodology presented resides in the prefailure space of the system in which no macroindicators such as cracks or delamination exist. Systems subjected to thermomechanical damage have been interrogated for the system state and the computed damage state correlated with the known imposed damage. The approach involves the use of condition monitoring devices which can be interrogated for damage proxies at finite time intervals. The interrogation techniques are based on the derivation of damage proxies and system prior-damage-based nonlinear least square methods, including the Levenberg–Marquardt algorithm. The system's residual life is computed based on residual-life computation algorithms. [ABSTRACT FROM AUTHOR]

Adaptive dynamic programming (ADP) is a promising research field for design of intelligent controllers, which can both learn on-the-fly and exhibit optimal behavior. Over the past decades, several generations of ADP design have been proposed in the literature, which have demonstrated many successful applications in various benchmarks and industrial applications. While many of the existing researches focus on multiple-inputs-single-output system with steepest descent search, in this paper we investigate a generalized multiple-input-multiple-output (GMIMO) ADP design for online learning and control, which is more applicable to a wide range of practical real-world applications. Furthermore, an improved weight-updating algorithm based on recursive Levenberg–Marquardt methods is presented and embodied in the GMIMO approach to improve its performance. Finally, we test the performance of this approach based on a practical complex system, namely, the learning and control of the tension and height of the looper system in a hot strip mill. Experimental results demonstrate that the proposed approach can achieve effective and robust performance. [ABSTRACT FROM AUTHOR]

Accurate assessment of the quality of concrete bridge decks and identification of corrosion induced delamination lead to economic management of bridge decks. It has been demonstrated that ground penetrating radar (GPR) can be successfully used for such purposes. The growing demand on GPR has brought into the challenge of developing automatic processes necessary to produce a final accurate interpretation. However, there have been few publications targeting at automatic detection of bridge deck delamination from GPR data. This paper proposes a novel method using partial differential equations to detect rebar (or steel-bar) mat signatures of concrete bridges from GPR data so that the delamination within the bridge deck can be effectively located. The proposed algorithm was tested on both synthetic and real GPR images and the experimental results have demonstrated its accuracy and reliability, even for diminished image contrast and low signal-to-noise ratio. Therefore, an accurate deterioration map of the bridge deck can be generated automatically. [ABSTRACT FROM PUBLISHER]

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton–Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface. [ABSTRACT FROM PUBLISHER]

Electronically controlled air suspension systems have been used in vehicles to improve ride comfort and handling safety by adjusting vehicle height. This paper proposes a new nonlinear controller to adjust the height of the vehicle sprung mass (height control) and to regulate the roll and pitch angles of the vehicle body (leveling control) by an air suspension system. A sliding mode control algorithm is designed to improve the tracking accuracy of the control and to overcome nonlinearities and uncertainties in the air suspension system. A mathematical model of the air suspension system is formulated in a nonlinear affine form to describe the dynamic behavior of the system and to derive the control algorithm. The sliding mode observer is also designed to estimate the pressures inside four air springs. The effectiveness and performance of the proposed control algorithm are verified by simulations and actual vehicle tests. [ABSTRACT FROM PUBLISHER]

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models. [ABSTRACT FROM PUBLISHER]

This correspondence paper presents an algorithm in which the independent components of link lengths are used as a medium to analyze the forward kinematics of a six-degree-of-freedom Stewart platform. The link lengths are firstly transformed into independent components through independent component analysis. Then, the value of positional variables is computed by using the Nelder–Mead algorithm by taking advantage of the relationships between the independent components and the positional variables. Simulations have been conducted to test the proposed algorithm. The experimental results show that the proposed algorithm can achieve a better performance than the other published algorithms. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to solve mathematical model equations on solid tumour growth and compute their parameter values by applying growth rates of prostate cancer cell lines in vivo. For these computations, we investigate previously developed C3(1)/Tag transgenic models of prostate cancer. To make the computations fast, we have constructed an algorithm, which is based on small amounts of spatial grid-points and obtained a correspondence between the in vivo growth of tumours and the solutions of the model equations. [ABSTRACT FROM AUTHOR]

This paper proposes a local polynomial modeling (LPM) approach and variable bandwidth selection (VBS) algorithm for identifying time-varying linear systems (TVLSs). The proposed method models the time-varying coefficients of a TVLS locally by polynomials, which can be estimated by least squares estimation with a kernel having a certain bandwidth. The asymptotic behavior of the proposed LPM estimator is studied, and the existence of an optimal local bandwidth which minimizes the local mean-square error is established. A new data-driven VBS algorithm is then proposed to estimate this optimal variable bandwidth adaptively and locally. An individual bandwidth is assigned for each coefficient instead of the whole coefficient vector so as to improve the accuracy in fast-varying systems encountered in fault detection and other applications. Important practical issues such as online implementation are also discussed. Simulation results show that the LPM-VBS method outperforms conventional TVLS identification methods, such as the recursive least squares algorithm and generalized random walk Kalman filter/smoother, in a wide variety of testing conditions, in particular, at moderate to high signal-to-noise ratio. Using local linearization, the LPM method is further extended to identify time-varying systems with mild nonlinearities. Simulation results show that the proposed LPM-VBS method can achieve a satisfactory performance for mildly nonlinear systems based on appropriate linearization. Finally, the proposed method is applied to a practical problem of voltage-flicker-tracking problem in power systems. The usefulness of the proposed approach is demonstrated by its improved performance over other conventional methods. [ABSTRACT FROM AUTHOR]

This paper presents State-space Model Interpolation of Local Estimates (SMILE), a technique to estimate linear parameter-varying (LPV) state-space models for multiple-input multiple-output (MIMO) systems whose dynamics depends on multiple time-varying parameters, called scheduling parameters. The SMILE technique is based on the interpolation of linear time-invariant models estimated for constant values of the scheduling parameters. As the linear time-invariant models can be either continuous- or discrete-time, both continuous- and discrete-time LPV models can be obtained. The underlying interpolation technique is formulated as a linear least-squares problem that can be efficiently solved. The proposed technique yields homogeneous polynomial LPV models in the multi-simplex that are numerically well-conditioned and therefore suitable for LPV control synthesis. The potential of the SMILE technique is demonstrated by computing a continuous-time interpolating LPV model for an analytic mass-spring-damper system and a discrete-time interpolating LPV model for a mechatronic XY-motion system based on experimental data. [ABSTRACT FROM AUTHOR]

In this paper a new approach to gas leakage detection in high pressure natural gas transportation networks is proposed. The pipeline is modelled as a Linear Parameter Varying (LPV) System driven by the source node massflow with the gas inventory variation in the pipe (linepack variation, proportional to the pressure variation) as the scheduling parameter. The massflow at the offtake node is taken as the system output. The system is identified by the Successive Approximations LPV System Subspace Identification Algorithm which is also described in this paper. The leakage is detected using a Kalman filter where the fault is treated as an augmented state. Given that the gas linepack can be estimated from the massflow balance equation, a differential method is proposed to improve the leakage detector effectiveness. A small section of a gas pipeline crossing Portugal in the direction South to North is used as a case study. LPV models are identified from normal operational data and their accuracy is analyzed. The proposed LPV Kalman filter based methods are compared with a standard mass balance method in a simulated 10% leakage detection scenario. The Differential Kalman Filter method proved to be highly efficient. [ABSTRACT FROM AUTHOR]

In this paper, the development of a software tool for linear parameter varying (LPV)/linear fractional transformation (LFT) modeling and data-based validation techniques is described. This tool represents a step towards industrialization of these techniques, which although still suffering from a certain theoretical complexity are increasingly shown to help deal effectively with important real world applications. The tool is a key component for the development of an integrated LPV/LFT framework for control modeling, analysis, and design which is also a necessity for successful transfer of LPV/LFT techniques to industry. The focus of this paper is on describing the development of the integrated modeling/validation software and examining the applicability problems associated with these techniques. The technical readiness of the tool is exemplified through the development of validated LFT models used for LPV control design and analysis of a nonlinear longitudinal model of the NASA HL-20 re-entry vehicle. [ABSTRACT FROM AUTHOR]

This paper deals with a novel approach for the real-time tracking of the maximum-torque-per-ampere (MTPA) trajectory of an interior permanent-magnet (IPM) motor drive. The proposed approach is based on the injection of proper test signals and allows the MTPA trajectory to be learned and updated in the drive during steady-state conditions. The analytical development of the estimation algorithm, which is supported by a complete validation by simulation, is given in this paper. Finally, experimental results are presented based on a prototype IPM drive system. [ABSTRACT FROM AUTHOR]

In this paper, a novel maximum power point tracking algorithm based on the perturb-and-observe (P&O) technique is introduced. The novelty of the approach is represented by the perturbation of more control variables, rather than just one. This allows the increase of the power extracted from the photovoltaic (PV) field as compared to the case of perturbation of a single control variable. The proposed technique overcomes the limitations of any existing tracking technique dedicated to PV arrays exhibiting a unique maximum power point, thus not only of the P&O approach, whenever dynamic constraints ensuring the correct system behavior must be fulfilled too. The proposed multivariable approach is described by means of its application to a single-stage one-cycle controlled PV inverter. [ABSTRACT FROM AUTHOR]

This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample. [ABSTRACT FROM AUTHOR]

Demographic projection models describe the development over time of the population in terms of events. A consistency problem arises if projected numbers of events are required to satisfy certain constraints: the consistency problem can be seen as a generalization of the well-known two-sex problem in nuptiality models. This paper presents a very general characterization of consistency problems, using matrix notation, as well as a slightly less general algorithm to solve them. The preferred specification of the objective function to be minimized by the algorithm leads to a solution that can be interpreted as a generalization of the harmonic-mean approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of an online method for diagnosing high-resistance connection (HRC) in delta-connected permanent magnet synchronous machine (PMSM) using zero-sequence current component (ZSCC). The development of this method begins with the establishment of the mathematic model of the PMSM with the HRC. Based on the model, a ZSCC-based method is proposed to diagnose the HRC. The fault indicator and angle differences are defined for fault diagnosis. To implement online diagnosis, an effective frequency tracking algorithm is applied. Finally, the fault diagnosis steps are presented. This method is capable of detecting the fault, evaluating the fault severity, and identifying the faulty phases. The simulation and experiment are carried out to verify the proposed method. Both the results show the validity of the proposed method, leading to the effective diagnosis of the HRC in the delta-connected PMSM. [ABSTRACT FROM AUTHOR]

Challenges such as advances in technology, demands of the global market, and limited warehouse spaces resort manufacturing industries to subcontracting. Subcontracting has been a considerable alternative in the manufacturing industries and is utilized as a strategic tool to diminish operation costs primarily to address the problem of scarcity when the firm faces a large demand on the commodity it supplies. The present study employed a mathematical model among firms engaging in subcontracting in search of an optimal schedule in the manufacture of the product and distribution of production time involved with an objective of obtaining a maximum profit. The constraints in the mathematical formulation included the total demand, processing capacity, available supply, processing rate, and time. The plausibility and the possible utility of the mathematical model has been explored employing sequential quadratic programming algorithm in the search of the optimal solutions. [ABSTRACT FROM AUTHOR]

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from $166\pm 39$ to $150\pm 16$ in the adult populations ($p=0.03$) and from $167\pm 25$ to $162\pm 23$ in the adolescent population ($p=0.06$). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population. [ABSTRACT FROM PUBLISHER]

Cross-docking is a technique firstly proposed to reduce the storage space and flow time, simultaneously. This paper addresses a truck scheduling problem, in which a position-based learning effect is taken into consideration for unloading and loading tasks done by human labors in many related environments. The goal of the given problem is to minimize the mean completion time of outbound trucks. Therefore, a mathematical model is proposed inspired by an available model in the literature of this field. Furthermore, four heuristic algorithms are developed along with a simulated annealing (SA) algorithm in order to overwhelm the complexity of large-sized problems. The performance of the proposed algorithms is compared with the optimal solutions obtained by a complete enumeration method. [ABSTRACT FROM AUTHOR]

The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks. [ABSTRACT FROM AUTHOR]

A mathematical model for characterizing the performance of wireless access networks based on a carrier-sense multiple-access (CSMA) mechanism is derived. The model is developed through a statistical description of the interference power coming from stochastic geometry and by taking into account the hidden node problem. As an example of a specific application of the model, the IEEE 802.15.4 standard is considered. Nodes are assumed to be uniformly distributed according to a Poisson point process (PPP) in a 2-D infinite plane. They have to communicate with the network coordinator through direct links (star topology), following a query-based application. The total interference power is modeled considering the actual density of nodes that are simultaneously transmitting. This takes into account the operation of the CSMA with collision avoidance (CSMA/CA) algorithm used by nodes to access the channel, according to the IEEE 802.15.4 standard. Results show the impact of node density, packet length, and sensing range on the system performance, which is evaluated in terms of success probability in transmitting packets from nodes to the coordinator. [ABSTRACT FROM PUBLISHER]

To forecast the optimum route passage of a vessel in the real weather conditions using the so-called velocity characteristics of the ship. These are developed in the way, as to have a very simple form, which can not guarantee high precision in calculating the speed of the vessel. The paper presents a mathematical model of the external forces operating in the vessel and the algorithm to solve this model for calculating the instantaneous speed of the vessel in the selected weather conditions. Made of a computer program, after appropriate research and testing, can be used to optimize the transport route of the ship. [ABSTRACT FROM AUTHOR]

Oscillometry is a popular technique for automatic estimation of blood pressure (BP). However, most of the oscillometric algorithms rely on empirical coefficients for systolic and diastolic pressure evaluation that may differ in various patient populations, rendering the technique unreliable. A promising complementary technique for automatic estimation of BP, based on the dependence of pulse transit time (PTT) on cuff pressure (CP) (PTT–CP mapping), has been proposed in the literature. However, a theoretical grounding for this technique and a nonparametric BP estimation approach are still missing. In this paper, we propose a novel coefficient-free BP estimation method based on PTT–CP dependence. PTT is mathematically modeled as a function of arterial lumen area under the cuff. It is then analytically shown that PTT–CP mappings computed from various points on the arterial pulses can be used to directly estimate systolic, diastolic, and mean arterial pressure without empirical coefficients. Analytical results are cross-validated with a pilot investigation on ten healthy subjects where 150 simultaneous electrocardiogram and oscillometric BP recordings are analyzed. The results are encouraging whereby the mean absolute errors of the proposed method in estimating systolic and diastolic pressures are 5.31 and 4.51 mmHg, respectively, relative to the Food and Drug Administration approved Omron monitor. Our work thus shows promise toward providing robust and objective BP estimation in a variety of patients and monitoring situations. [ABSTRACT FROM PUBLISHER]