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This paper proposes a new hybrid robust control method for control of an autonomous underwater vehicle. Fractional sliding mode control (FSMC) is robust against external disturbances. The main drawback of the FSMC method is creating a chattering phenomenon. Therefore, a compound control method is applied, which benefits in both robustness of the FSMC method and chattering elimination by the new control algorithm. A random noise is applied in order to verify the robustness of the proposed control method. The stability of FSMC and proposed compound control method has been verified by Lyapunov theory. The effectiveness of the proposed control method is compared with FSMC, which numerical simulation results confirm the best performance of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper designs a predefined-time convergent continuous control algorithm to stabilize a permanent-magnet synchronous motor (PMSM) system. Three cases have been considered: disturbance-free, in presence of a deterministic disturbance satisfying a Lipschitz condition, and in presence of both a stochastic white noise and a deterministic disturbance satisfying a Lipschitz condition. The designed control law is free from the restrictions of exponential control growth and exact initial conditions knowledge. This is the first predefined-time convergent continuous control algorithm applied to stabilizing a PMSM system with both deterministic and stochastic disturbances, which enables one to a priori set the predefined convergence time even in presence of various disturbances of different nature. Numerical simulations are provided for a PMSM system to validate the obtained theoretical results in each of the three considered cases. The simulation results demonstrate that the employed values of the predefined-time convergent control inputs are applicable in practice. [ABSTRACT FROM AUTHOR]

We consider adaptive optimal tracking problems for a minimum-phase discrete-time plant with uncertainty in the output channel and bounded exogenous disturbance. The coefficients of the equation of the linear time-invariant nominal model, the uncertainty gain, and the upper bound of the exogenous disturbance are assumed to be unknown, and the worst-case asymptotic tracking error serves as the performance criterion. It is shown that the statement of the optimal problem depends on a priori information about the bounded control. The solution of the problems is based on the polyhedral estimation of all unknown parameters and the use of the performance factor of the tracking problem as an identification criterion for calculating the current optimal estimates. The performance and efficiency of the proposed control algorithms is illustrated by the results of numerical simulation. [ABSTRACT FROM AUTHOR]

Abstract One of the most crucial tasks in hydrocarbon field development, it is an optimal wells placement. Simulation-based well placement optimization can be intricate to implement or computationally expensive. In this paper, a novel approach for the reduction of computational challenges by combining population-base optimization algorithm with self-adapt reservoir clusterization method to determine initial well placement position for the similar producing zones is presented. The developed methodology supported initial well placement location and allowed to shrink search space area. As a result, the computational time was decreased by 18% for particle swarm optimization and by 21% for genetic algorithm. Additionally, the project profitability was increased by 0.24% in case of genetic algorithm utilization and 0.59% for particle swarm optimization, what confirms the necessity for precise well placement localization. The average convergence factor for the employed algorithms increased from 18.43% to 60.78% for genetic algorithm and from 41.71% to 46.96% for particle swarm algorithm. An additional advantage of the developed clustering method is a notable reduction in time to reach high solution diversity in particle swarm algorithm. Increasing SOM training time for the large data set is negligible in comparison with full reservoir simulation run. The proposed methodology is based on the typical numerical model without requirement of additional dataset or streamline and can be easily transferred from field to field. The power and the workflow utility are demonstrated by the results of significant improvement for the algorithm's convergence rate and time. Highlights • Novel self-adapt methodology to enhance well placement optimization was developed. • Significant reduction of optimization time was observed. • Artificial Kohonen neural network was implemented to solve complex engineering problem. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of designing distributed control algorithms to solve the rendezvous problem for multi-robot systems with limited sensing, for situations in which random nodes may fail during execution. We first formulate a distributed solution based upon averaging algorithms that have been reported in the consensus literature. In this case, at each stage of execution a one-step sequential optimal control (i.e., näive greedy algorithm) is used. We propose a distributed stochastic optimal control algorithm that minimizes a mean-variance cost function for each stage, given that the probability distribution for possible node failures is known a priori, as well as a minimax version of the problem when the prior probability distribution is not known. We demonstrate via extensive numerical simulations that our proposed algorithm provides statistically better rendezvous task performance than contemporary algorithms in cases for which failures occur. [ABSTRACT FROM AUTHOR]

Abstract—Algorithms for tracking multiple objects using correlation filters require calculation of a composite filter based on synthetic discriminant functions in order to increase the robustness to changes in posture, partial overlapping of objects by other objects, scaling, rotation, nonuniform illumination, and complex background. The calculation algorithm has large computational complexity. In this paper, we propose recursive calculation of a composite filter using algorithms for rapid inversion of matrices to accelerate synthesis of the filter. Computer simulation results on calculation of a composite correlation filter are presented and discussed in terms of the computation accuracy and the rate of calculation. [ABSTRACT FROM AUTHOR]

This paper presents an aerodynamic model-based robust adaptive control algorithm for close formation flight. The leader aircraft is assumed to be at level and straight flight which characterizes the most common scenario for close formation flight. The formation aerodynamic effects are assumed to be unknown, but an online formation aerodynamic model is available to predict those effects. In light of the online formation aerodynamic model, a robust adaptive control algorithm is thereafter developed on the follower aircraft to counteract the unknown formation aerodynamic effects and obtain highly accurate formation tracking performance. The proposed control algorithm is composed of a baseline controller and an integrator-augmented robust adaptive controller, which can efficiently deal with both matched and mismatched formation aerodynamic effects and external disturbances. The major advantage of the proposed design is that it can achieve at least ultimate bounded tracking control with certain transient performance guaranty. The efficiency and robustness of the proposed control design are eventually validated via numerical simulations of close formation flight at two different scenarios. [ABSTRACT FROM AUTHOR]

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension. [ABSTRACT FROM AUTHOR]

In order to improve the reliability of wind turbines, avoid serious accidents and reduce operation and maintenance (O&M) costs, it is important to effectively detect faults of wind turbines operating in harsh environment. This paper proposes a radically data-driven fault detection and diagnosis (FDD) method for wind turbines, which implements deep belief network (DBN). The DBN requires no knowledge of physical model, instead, it employs historical data without any pre-selection. The method has been evaluated in a wind turbine benchmark simulink model, in comparison with four model-based algorithms and four data-driven methods, and the results have shown that the proposed method achieves the highest accuracy. Moreover, extensive evaluation has been taken to analyse the robustness of proposed method, and the simulation results indicate the stable performance of proposed method in faults diagnosis of wind turbine. [ABSTRACT FROM AUTHOR]

In this paper, a new robust adaptive controller is investigated to force an underactuated surface marine vessel to follow a predefined parameterised path at a desired speed, despite actuator saturation and the presence of model uncertainties as well as environmental disturbances induced by waves, wind and sea-currents. To ensure robustness of the path-following controller, time-varying constraint on the off-track error (i.e. the maximal distance from the ship to the reference path) is considered. To address the off-track error constraint the tan-barrier Lyapunov function is incorporated with the control scheme, where the idea of auxiliary design system introduced in Chen, Sam, and Ren (2011) is adopted and its states are used in combination with backstepping and Lyapunov synthesis to adaptive tracking control design with guaranteed stability. Furthermore, the command filters are adopted to implement physical constraints on the virtual control laws so that analytic differentiation of the virtual control laws is avoided. We show that the proposed robust adaptive control law is able to guarantee semi-global uniform ultimate bounded stability of the closed-loop system. Numerical simulations and experimental results are carried out to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is devoted to robust gain scheduled PID controller design with L2 performance for the linear time varying (LPV) uncertain system with polytopic uncertainties. The novel approach of robust controller design ensures that the obtained design procedure is convex with respect to both plant uncertainties (polytopic system) and gain scheduling parameters and gives less conservative results. Modified design procedure should be used to obtain a robust controller or robust switched controller (ideal, non-ideal switching) with arbitrarily switching algorithm. The effectiveness of the proposed approach is illustrated on the simulation examples. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is presented for getting a solution of the mobile robot path planning problem based on Adaptive Particle Swarm Optimization (APSO). The proposed APSO algorithm is smarter than conventional PSO and widely used for solving the real time problems. In this work an objective function is framed considering the distance between robot to goal and obstacle respectively. The objective function is optimized with of APSO for solving the path planning process of robot. The different simulated experiments are performed to test the ability of the proposed algorithm. The performance of the robot path planning using APSO is compared to the performance of the conventional PSO in terms path length and time in static environments. It is focused that using new approach the robot can successfully avoid obstacle and reach the target with shorter time than conventional PSO. [ABSTRACT FROM AUTHOR]

The use of an image detector to receive grating images and measure angle displacement via image processing is a relatively new technique, which yields higher resolution and better precision than the traditional moiré fringe method. To improve the robustness of image-type angle measurement, this paper proposes a robust sub-pixel subdivision algorithm based on the least square method. Firstly, by analyzing the characteristic of grating image, a new subdivision algorithm is established based on the least square method. Secondly, the simulations of robustness are completed to prove the performance in theoretically. Lastly, the proposed algorithm is used in a typical image-type angle sensor to test the performance in real case. By test, the proposed method is shown to be more accurate and with better robust than the traditional algorithm (centroid algorithm). In a typical image-type angle sensor, it successfully achieves a resolution of 0.62″ (21-bit), 2 13 -fold subdivision resolution, and precision of 12.85″. The results presented here may provide a theoretical and technological foundation for further research on small-size, high-resolution photographic rotary encoders. [ABSTRACT FROM AUTHOR]

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A robust consensus controller is proposed for heterogeneous higher-order nonlinear multi-agent systems, when the agent dynamics are involved with mismatched uncertainties. A distributed consensus protocol based on a time-varying nonhomogeneous finite-time disturbance observer and sliding mode control is designed to realize the network consensus of higher-order multi-agent systems. The time-varying finite-time disturbance observer overcomes the problem of peaking value near the initial time caused by the constant gain one and is designed to estimate the uncertainties and to mitigate the effect of mismatched uncertainties during the sliding mode. To eliminate the chattering phenomenon and ensure finite-time convergence to the sliding surface, the control law is designed by using the super twisting algorithm. Finally numerical simulations are given to illustrate the validity of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a tracking control strategy for nonlinear systems without needing a prior knowledge of the reference trajectory. The proposed method consists of a set of local controllers with appropriate overlaps in their stability regions and an on-line switching strategy which implements these controllers and uses some augmented intermediate controllers to ensure steering the system states to the desired set points without needing to redesign the controller for each value of set point changes. The proposed approach provides smooth transient responses despite switching among the local controllers. It should be mentioned that the stability regions of the proposed controllers could be estimated off-line for a range of set-point changes. The efficiencies of the proposed algorithm are illustrated via two example simulations. [ABSTRACT FROM AUTHOR]

In this paper, we analyze all possible situations of rank deficiency that cause breakdown in block conjugate gradient (BCG) solvers. A simple solution, breakdown-free block conjugate gradient (BFBCG), is designed to address the rank deficiency problem. The rationale of the BFBCG algorithm is to derive new forms of parameter matrices based on the potentially reduced search subspace to handle rank deficiency. Orthogonality properties and convergence of BFBCG in case of rank deficiency are justified accordingly with mathematical rigor. BFBCG yields faster convergence than restarting BCG when breakdown occurs. Numerical examples suffering from rank deficiency are provided to demonstrate the robustness of BFBCG. [ABSTRACT FROM AUTHOR]

Most complex engineering structures are three-dimensional in practice. The process of one-dimensional extending to three-dimensional is a challenge that must be conquered by Operational Modal Analysis (OMA) methods when these methods are applied to complex engineering applications supported by scientific researches. This study put forward a new three-dimensional structure OMA method based on Second-Order Blind Identification (SOBI) and general reversion of least square. Firstly, modal coordinates decomposition of one-dimensional structural vibration response signal with SOBI. Secondly, the reasons that modal parameters identified by SOBI including energy uncertainty, order uncertainty and modal missing are explained in theory. Thirdly, the SOBI algorithm is used to decompose the response signals of displacement of a direction whose vibration response is the largest, then the other two directions are calculated by using the least square generalized inverse algorithm, and the modal parameters of three-dimensional structures are identified by the matrix assembly method. Numerical simulation results in a cylindrical shell demonstrated that this novel method is practical and effective by applied to practice in OMA of three-dimensional structures, and robustness to Gauss measurement noise disturbances. [ABSTRACT FROM AUTHOR]

This paper presents the mathematical modeling of a multivariable process used in level control, under the assumptions that all pipes are filled with fluid and the inertial effects caused by flow variations, in case of small accelerations, are neglected. Because of the way the two tanks are interconnected and interact with each other, the resulting mathematical model is nonlinear. In the paper it is proposed a robust control algorithm for this plant developed based on H-infinity loop shaping procedure. The required computations and the study of the behavior of the system are done in the Matlab simulation environment. [ABSTRACT FROM AUTHOR]

The interaction simulations between particles and structures are often performed in the context of the combined discrete-finite element (DEM-FEM) method, where an efficient, robust and accurate contact algorithm for challenging contact problems is essential. A three-dimensional (3D) discrete and finite element contact algorithm, named ZGL here, has been proposed in our research group (Zang, M.Y. et al., 2011. A contact algorithm for 3D discrete and finite element contact problems based on penalty function method. Comput. Mech. 48, 541–550.). Despite being quite well-established, ZGL algorithm may lack efficiency, robustness and accuracy in certain situations, e.g., when special mesh pattern is applied. However, since such cases are readily to appear in practice engineering application, an algorithm named DZCell is developed to resolve these issues and thus significantly applicable to challenging contact problems. The proposed DZCell algorithm includes an improved global phase to directly find the potential segments rather than the nearest node for discrete elements as contact counterparts. Furthermore, both the memory cost and time consumption of this algorithm are linear, and the algorithm is readily to extend to parallel computing. Several numerical examples demonstrate the achievable improvements in terms of efficiency, robustness and accuracy for 3D contact analysis and validate the capability of the DZCell algorithm in the granular science and mechanical engineering. [ABSTRACT FROM AUTHOR]

This paper presents a composite control strategy integrating adaptive sliding-mode control and the linear quadratic regulator (LQR) technology for a wheeled inverted pendulum (WIP) vehicle system. The system can be partitioned into an actuated rotational subsystem and an underactuated longitudinal subsystem based on the different control input in the mathematical model. In particular, the instability analysis of zero dynamic for the underactuated longitudinal subsystem is investigated in detail using the feedback linearisation technology. Then, an adaptive sliding-mode control is designed for the trajectory tracking, where an adaptive algorithm is developed to handle with the parameter uncertainties. In addition, the LQR technique is employed to guarantee zero dynamics stability so as to achieve simultaneously the vehicle body stabilisation at the upright position. Simulation results show the good performance and strong robustness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

Satellite remote sensing image target matching recognition exhibits poor robustness and accuracy because of the unfit feature extractor and large data quantity. To address this problem, we propose a new feature extraction algorithm for fast target matching recognition that comprises an improved feature from accelerated segment test (FAST) feature detector and a binary fast retina key point (FREAK) feature descriptor. To improve robustness, we extend the FAST feature detector by applying scale space theory and then transform the feature vector acquired by the FREAK descriptor from decimal into binary. We reduce the quantity of data in the computer and improve matching accuracy by using the binary space. Simulation test results show that our algorithm outperforms other relevant methods in terms of robustness and accuracy. [ABSTRACT FROM AUTHOR]

Latent structure in the signals transmitted through dispersive channels (real or complex) was exploited to formulate the maximum a posteriori probability (MAP) equalization in closed form, which allowed robust integer processing and some benchmark results. Emphasis is mostly on the adaptive implementation of the equalizer on unknown channels, with particular attention to complexity and bit error rate (BER) performance. The latent structure, laid as rules underlying the channel templates, allowed further the design of an original robust hyper-sphere-based complexity reduction algorithm wherein the hyper-sphere radius is self-adjusting. Simulation results are provided on the algorithm behavior and on performance of the proposed adaptive equalizer. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive sensing period adjustment scheme (ASPA) which adaptively adjusts sensing periods by considering the dynamics of the channel conditions in cognitive radio sensor networks (CRSNs). The ASPA with a linearly increasing and exponentially decreasing (LIED) algorithm is a simple but robust scheme because secondary users (SUs) can adaptively adjust their sensing periods, even when sensing errors or collisions with control messages exist. A performance model for analyzing the efficiency of the ASPA is also proposed, and it is compared with simulation results. It is shown in our results that the ASPA outperforms the existing protocol in terms of the average sensing overhead and throughput as well as the average energy consumption. [ABSTRACT FROM AUTHOR]

The finite cover system plays a critical role in Numerical Manifold Method (NMM) for the unified simulation of models from continuum to discontinuum. However, large amounts of computational geometries are usually involved in the traditional finite cover generation algorithm, which makes the process of finite cover generation a time consuming and error prone task and limits the wide applications of NMM. To achieve a simple and robust finite cover generation algorithm for NMM, a new method for the generation of integration cells including closed convex or concave polygons was developed in this paper as an important supplement to our recent work [Cai et al., 2013]. In the newly developed integration cells generation algorithm, with the help of pre-defined symbol functions and 3 different matrixes, nodes including vertexes and intersection points belonging to a same integration cell were first grouped together, and then listed in a anticlockwise manner to finally form the closed circuit. In this way, large amounts of computational geometries employed in the identification of integration cells were replaced with computational algebras. Several benchmarks were simulated to investigate the accuracy of the proposed integration cells generation algorithm and to demonstrate the robustness of NMM. [ABSTRACT FROM AUTHOR]

In this paper, we present a general algorithm to automatically convert arbitrary building massing models into multi-zone building energy models (BEM). The algorithm follows current guidelines for thermal zone discretization of BEMs when actual interior space boundaries are yet undefined. Envisioned applications are for rapid model generation during schematic building design as well as for urban massing studies. We present an argument that current recommendations for separating core from perimeter zones effectively follow a straight-skeleton subdivision. Following a step-by-step explanation of the procedure, a number of example building shapes of varying complexity are shown to demonstrate the algorithm's robustness and suitability for automated multi-zone BEM generation. Going forward, it is recommended that the algorithm is adopted by software developers to ensure more consistent thermal model production within the building simulation community. [ABSTRACT FROM PUBLISHER]

In this paper, we present numerical and experimental study on beam steering with liquid-crystal optical phased array (LC-OPA) by means of stochastic parallel gradient descent (SPGD) algorithm. The numerical simulation, which is mainly based on the evolution of power-in-bucket (PIB), indicates that the beam steering strategy with LC-OPA is relatively robust, and the algorithm performs well most of the time. The results also reveal that the performance of SPGD algorithm is influenced by the order of Zernike polynomials, the radius of bucket and the steering angle. The order of Zernike polynomials and the radius of bucket are optimized to improve the accuracy and convergence of the algorithm. In a proof-of-concept experiment, the feasibility of the beam steering strategy with LC-OPA is demonstrated. [ABSTRACT FROM AUTHOR]

Proposed is a new subband adaptive filter (SAF) algorithm by minimizing Huber’s cost function that is robust to impulsive noises. Generally, this algorithm works in the mode of the normalized SAF (NSAF) algorithm, while it behaves like the sign SAF (SSAF) algorithm only when the impulsive noises appear. To further improve the robustness of this algorithm against impulsive noises, the subband cutoff parameters are updated in a recursive way. Moreover, the proposed algorithm can be interpreted as a variable step size NSAF algorithm, thus it exhibits faster convergence rate and lower steady-state error than the NSAF. Simulation results, using different colored input signals in both impulsive and free-impulsive noise environments, show that the proposed algorithm works better than some existing algorithms. [ABSTRACT FROM AUTHOR]

This paper aims to address a new class of multiscale coordination control problems for multivehicle systems that are subject to external disturbances. Each vehicle is measured by an independent scale due to the constraints of physical environments. Based on the nearest neighbor-interaction rules, three algorithms of different convergence rates are presented such that the vehicles are guaranteed to achieve consensus on a common quantity for all but of their own scales. Simulation tests are performed to validate the effectiveness and robustness of these consensus algorithms. By introducing a Lyapunov potential function, the consensus analysis is further achieved for the three multiscale algorithms, regardless of them with an exponential, finite-time, or fixed-time convergence rate and in the presence of bounded disturbances. [ABSTRACT FROM PUBLISHER]

Based on pattern synthesis of multi-input multi-output (MIMO) radar, differential particle swarm optimization (DPSO), which introduced crossover and mutation scheme to PSO, was proposed. Owing to the adjusting function of the two schemes, the new algorithm can keep diversity of the population and avoid premature of basic PSO. By random search and optimization of side lobe level and the NULL depth, the best arrangement and amplitude of arrays was reached. The simulation results show that the algorithm has faster convergence and more reliable robustness. [ABSTRACT FROM AUTHOR]

Camera calibration is a basic and crucial problem in computer vision and photogrammetry. The traditional calibration approach based on 2D planar target fails to give reliable and accurate results due to the inaccurate localization of feature points in some calibration images. An accurate and robust estimation method for camera parameters based on RANdom SAmple Consensus (RANSAC) algorithm is proposed to exclude the unreliable images in this study. First, the distance between circular point and image of the absolute conic is defined, and the recommended threshold value is given by computer simulation. Second, RANSAC has been utilized to pick out a subset of calibrating images automatically, and linear algebraic approximation is performed to estimate the intrinsic parameters and external parameters. Finally, all the camera parameters including lens distortion parameters are refined by the non-linear searching algorithm. Numerical simulation and practical experiment in this paper demonstrate the accuracy and robustness of the proposed method. The experimental results show that the proposed method is more robust and efficient in improving the calibration accuracy than the traditional methods. [ABSTRACT FROM AUTHOR]

For center deviation of space circular mark in the perspective projection transformation process, a novel method is proposed in this paper to locate the real projection center precisely based on the theory of analytic geometry and spatial perspective. First, we analyze the spatial circle error of perspective projection transforms generated and gives relevant basic principles. Then the detailed implementation of the algorithm and the specific steps are presented. Finally through the experiments verify the effectiveness of the method. Simulation and experimental results indicate that this method can achieve the actual projection point precise positioning of the space circular target. The algorithm does not require additional complex constraints and has good robustness; therefore it has a wide range of practical engineering application value. [ABSTRACT FROM AUTHOR]

A robust H∞ control method and switched control algorithm for hydraulic actuator presents in human-machine coordinated motion to solve the motion delay of lower extremity exoskeleton. After the characteristic parameters synthesis of human limb and exoskeleton linkage, the human-machine coupled motion model is constructed to estimate the appropriate hydraulic pressure, which is considered as a structural uncertainty in hydraulic model. Then the robust controller is designed to improve the robust stability and performance under the structural and parametric uncertainty disturbances. Simulation results show that, in walking mode, this robust controller can achieve a better dynamic response and aid-force efficiency than PID controller. Then, according to gait divisions of person’s limb motion, the switched control algorithm is designed to reduce the delay of exoskeleton tracking person. Finally, the experimental results show that the human-machine coordinated walk with bearing 60 kg load and squat action with no external load are realized effectively by this proposed method. [ABSTRACT FROM AUTHOR]

To avoid influences caused by pose, illumination and facial expression variations, we propose a robust three-step algorithm based on ASM and POEM for facial landmark localization. Firstly, Model Selection Factor is utilized to achieve a pose-free initialized shape. Then, we use the global shape model of ASM to describe the whole face and the texture model POEM to adjust the position of each landmark. Thirdly, a second localization is presented to discriminatively refine the subtle shape variation for some organs and contours. Experiments are conducted in four main face datasets, and the results demonstrate that the proposed method accurately localizes facial landmarks and outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Focal brachytherapy is a clinical procedure that can be used to treat low-risk prostate cancer with reduced side-effects compared to conventional brachytherapy. Current practice is to man- ually plan the placement of radioactive seeds inside the prostate to achieve a desired treatment dose. Problems with the current practice are that the manual planning is time-consuming and high doses to the urethra and rectum cause undesirable side-effects. To address this problem, we have designed an optimisation algorithm that constructs treatment plans which achieve the desired dose while minimizing dose to organs at risk. We also show that these seed plans are robust to post-operative movement of the seeds within the prostate. [ABSTRACT FROM AUTHOR]

Refractivity from clutter (RFC) technique estimates lower atmospheric refractivity from radar sea clutter returns. An important issue of RFC is how to make the estimate more robust. Rather than using only single beam radar sea clutter returns, this paper puts forward a theoretical framework of combining multi-source clutter power to improve the retrieved results by the variational adjoint approach. The derivation of the adjoint is accomplished by an analytical transformation of the parabolic equation model in a continuous domain. To test the theoretical algorithm, numerical simulations using multi-elevation clutter observations are carried. The results demonstrate that combining two elevation beams gives more stable estimate than using only single beam. [ABSTRACT FROM AUTHOR]

The paper deals with the network robustness from the perspective of nodal centrality measures and its applicability in Software-Defined Networking (SDN). Traditional graph characteristics have been evolving during the last century, and numerous of less-conventional metrics was introduced trying to bring a new view to some particular graph attributes. New control technologies can finally utilize these metrics but simultaneously show new challenges. SDN brings the fine-grained and nearly online view of the underlying network state which allows to implement an advanced routing and forwarding. In such situation, sophisticated algorithms can be applied utilizing pre-computed network measures. Since in recent version of SDN protocol OpenFlow (OF) has been revived an idea of the fast link failover, the authors in this paper introduce a novel metric, Quality of Alternative Paths centrality (QAP). The QAP value quantifies node surroundings and can be with an advantage utilized in algorithms to indicate more robust paths. The centrality is evaluated using the node-failure simulation at different network topologies in combination with the Quality of Backup centrality measure. [ABSTRACT FROM AUTHOR]

As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), robustness has a significant influence on the separate performance of ICA. However, the traditional objective functions used in fast fixed-point algorithm of ICA will be invalid in separating the original signals when the outliers mix in signals. In this paper, we introduce a new robust objective function based on the Negentropy maximization. With second order approximation with Maclaurin expansion, the proposed function enables the estimation of individual independent components. In addition, it guarantees the separate performance of ICA that the original signals whether mix with outliers. Furthermore, combined with the proposed objective function, the fast fixed-point algorithm of ICA is reliable in the scenario of the signals mix with outliers. Simulation results show that the separate performance of proposed objection function is superior to the traditional objective functions as the outliers appear in the original signals. [ABSTRACT FROM AUTHOR]

This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover’s algorithm. [ABSTRACT FROM AUTHOR]