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Dominance robustness (DR) has been proposed for assessing the ability of the Pareto-optimal solutions to remain to be nondominated when the decision variables are subject to noise. There are two main challenges in search for dominance robust optimal solutions in dominance robust multiobjective optimization (MOP), namely, accurate estimation of the DR measure and a good balance between convergence and DR in the presence of uncertainty. In this article, a novel robust MOP evolutionary algorithm based on decision variable assortment (DVA) is proposed to tackle these challenges. To be specific, an indicator, termed as dominance robust indicator, is proposed to measure the DR based on the dominance level and dominance relationship of the sampled points. Then, the decision variables are divided into low DR-related variables and high DR-related variables based on the DVA strategy. Finally, low and high DR related variables are optimized separately to obtain the dominance robust optimal solutions. In addition, performance indicators to quantify the performance of dominance robust optimal solution set obtained by robust MOP algorithm are proposed. Experimental results have demonstrated that the proposed algorithm is competitive in search for dominance robust optimal solutions.

This article considers the bearing-only formation control problem, where the control of each agent only relies on relative bearings of their neighbors. A new control law is proposed to achieve target formations in finite time. Different from the existing results, the control law is based on a time-varying scaling gain. Hence, the convergence time can be arbitrarily chosen by users, and the derivative of the control input is continuous. Furthermore, sufficient conditions are given to guarantee almost global convergence and interagent collision avoidance. Then, a leader-follower control structure is proposed to achieve global convergence. By exploring the properties of the bearing Laplacian matrix, the collision avoidance and smooth control input are preserved. A multirobot hardware platform is designed to validate the theoretical results. Both simulation and experimental results demonstrate the effectiveness of our design.

This article presents a smart meter based two-layer state estimation technique, design to enable integrated monitoring of medium-voltage (MV) and low-voltage (LV) power distribution networks. The main contributions of this work include the development of a novel topology reduction technique for LV networks in order to carry out state estimation with a reduced number of smart meter based measurements, and a linear LV state estimation technique, which reduces convergence problems in LV networks. In addition, a detailed framework for integrated MV/LV network state estimation in realistic, unbalanced three-phase networks is proposed and demonstrated using the IEEE 13 bus and IEEE 906 LV networks. The results suggest that the proposed two-layer state estimation technique is robust and provides improved distribution system state estimation accuracy compared to traditional approaches.

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives.

Only a small number of function evaluations can be afforded in many real-world multi-objective optimization problems where the function evaluations are economically/computationally expensive. Such problems pose great challenges to most existing multi-objective evolutionary algorithms which require a large number of function evaluations for optimization. Surrogate-assisted evolutionary algorithms have been employed to solve expensive multi-objective optimization problems. Specifically, a certain number of expensive function evaluations are used to build computationally cheap surrogate models for assisting the optimization process without conducting expensive function evaluations. Infill sampling criteria in most existing surrogate-assisted evolutionary algorithms take all requirements on convergence, diversity, and model uncertainty into account, which is, however, not the most efficient in exploiting the limited computational budget. Thus, this paper proposes a Kriging-assisted two-archive evolutionary algorithm for expensive many-objective optimization. The proposed algorithm uses one influential point-insensitive model to approximate each objective function. Moreover, an adaptive infill criterion that identifies the most important requirement on convergence, diversity, or uncertainty is proposed to determine an appropriate sampling strategy for re-evaluations using the expensive objective functions. Experimental results on a set of expensive multi/many-objective test problems have demonstrated its superiority over five state-of-the-art surrogate-assisted evolutionary algorithms.

Recent years have bore witness to the proliferation of distributed filtering techniques, where a collection of agents communicating over an ad-hoc network aim to collaboratively estimate and track the state of a system. These techniques form the enabling technology of modern multi-agent systems and have gained great importance in the engineering community. Although most distributed filtering techniques come with a set of stability and convergence criteria, the conditions imposed are found to be unnecessarily restrictive. The paradigm of stability and convergence in distributed filtering is revised in this manuscript. Accordingly, a general distributed filter is constructed and its estimation error dynamics is formulated. The conducted analysis demonstrates that conditions for achieving stable filtering operations are the same as those required in the centralized filtering setting. Finally, the concepts are demonstrated in a Kalman filtering framework and validated using simulation examples.

Online adaptation of temperature-sensitive motor parameters is of significance for the electric drives in reliability-critical applications. Recursive prediction error method (RPEM) is widely used for this purpose. Gauss-Newton Algorithm (GNA), a prediction-error-gradients based algorithm, is adopted in this paper to find RPEM-gains for the parameter identification. This paper first investigates the simultaneous identifiability of permanent magnet flux linkage ( $\Psi_{\mathrm{m}}$ ), and stator resistance (Rs) of interior permanent magnet synchronous machine (IPMSM) using both nonlinear observability theorem and RPEM. Subsequently, GNA is analyzed for its tracking capability, speed of convergence, need of gain-scheduling and computational demand in comparison to stochastic gradient (SGA), another algorithm of the same class, using steady and dynamic state simulations.

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we consider the problem of estimating and tracking an R-dimensional subspace with relevant information embedded in an N-dimensional ambient space, given that N>>R. We focus on a formulation of the signal subspace that interprets the problem as a least squares optimization. The approach we present relies on the geometrical concepts behind the Affine Projection Algorithms (APA) family to obtain the Affine Projection Subspace Tracking (APST) algorithm. This on-line solution possesses various desirable tracking capabilities, in addition to a high degree of configurability, making it suitable for a large range of applications with different convergence speed and computational complexity requirements. The APST provides a unified framework that generalises other well-known techniques, such as Oja’s rule and stochastic gradient based methods for subspace tracking. This algorithm is finally tested in a few synthetic scenarios against other classical adaptive methods. This work has been supported by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22 / AEI / 10.13039/501100011033) and by the Catalan Government (AGAUR) under grant 2017 SGR 578.

The convergence of artificial intelligence, high-performance computing (HPC), and data science brings unique opportunities for marked advance discoveries and that leverage synergies across scientific domains. Recently, deep learning (DL) models have been successfully applied to a wide spectrum of fields, from social network analysis to climate modeling. Such advances greatly benefit from already available HPC infrastructure, mainly GPU-enabled supercomputers. However, those powerful computing systems are exposed to failures, particularly silent data corruption (SDC) in which bit-flips occur without the program crashing. Consequently, exploring the impact of SDCs in DL models is vital for maintaining progress in many scientific domains. This paper uses a distinctive methodology to inject faults into training phases of DL models. We use checkpoint file alteration to study the effect of having bit-flips in different places of a model and at different moments of the training. Our strategy is general enough to allow the analysis of any combination of DL model and framework—so long as they produce a Hierarchical Data Format 5 checkpoint file. The experimental results confirm that popular DL models are often able to absorb dozens of bit-flips with a minimal impact on accuracy convergence

It is particularly challenging for evolutionary algorithms to quickly converge to the Pareto front in large-scale multiobjective optimization. To tackle this problem, this article proposes a large-scale multiobjective evolutionary algorithm assisted by some selected individuals generated by directed sampling (DS). At each generation, a set of individuals closer to the ideal point is chosen for performing a DS in the decision space, and those nondominated ones of the sampled solutions are used to assist the reproduction to improve the convergence in evolutionary large-scale multiobjective optimization. In addition, elitist nondominated sorting is adopted complementarily for environmental selection with a reference vector-based method in order to maintain diversity of the population. Our experimental results show that the proposed algorithm is highly competitive on large-scale multiobjective optimization test problems with up to 5000 decision variables compared to five state-of-the-art multiobjective evolutionary algorithms.

This paper develops a fully distributed differentially-private learning algorithm based on the alternating direction method of multipliers (ADMM) to solve nonsmooth optimization problems. We employ an approximation of the augmented Lagrangian to handle nonsmooth objective functions. Furthermore, we perturb the primal update at each agent with a time-varying Gaussian noise with decreasing variance to provide zero-concentrated differential privacy. The developed algorithm has competitive privacy-accuracy trade-off and applies to nonsmooth and non necessarily strongly convex problems. Convergence and privacy-preserving properties are confirmed via both theoretical analysis and simulations.

Constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.

In this brief, we propose a novel method based on the Taylor expansion for the estimation of input delay for a class of input-affine dynamical systems. The proposed method guarantees the asymptotic convergence of the estimation error to zero. An application to the input delay estimation of a continuous stirred tank reactor system shows the effectiveness of the proposed method.

Channel modeling is a challenging vital step towards the development of diffusion-based molecular communication networks (DMCNs). Analytical approaches for diffusion channel modeling are limited to simple and specific geometries and boundary conditions. Also, simulation- and experiment-driven methods are very time-consuming and computationally complex. In this paper, the channel model for DMCN employing the fundamental concentration Green’s function (CGF) is characterized. A general homogeneous boundary condition framework is considered that includes any linear reaction systems at the boundaries in the environment. To obtain the CGF for a general DMCN including multiple transmitters, receivers, and other objects with arbitrary geometries and boundary conditions, a semi-analytical method (SAM) is proposed. The CGF linear integral equation (CLIE) is analytically derived. By employing the numerical method of moments, the problem of CGF derivation from CLIE is transformed into an inverse matrix problem. Moreover, a sequential SAM is proposed that converts the inversion problem of a large matrix into multiple smaller matrices reducing the computational complexity. Particle-based simulator confirms the results obtained from the proposed SAM. The convergence and run time for the proposed method are examined. Further, the error probability of a simple diffusion-based molecular communication system is analyzed and examined using the proposed method.

Driven by the rapid development of modern industrial processes, Cyber-Physical Systems (CPSs), which tightly conjoin computational and physical resources, have become ever more prevalent during recent years. However, due to the intrinsical vulnerability of the cyber layer, the system performances of CPSs are easily degraded by malicious false data injection (FDI) attacks which are launched by adversary. In this work, the issue of secure reconstruction is considered for linear CPSs with simultaneous sparse actuator and sensor attacks. First, an adaptive counteraction searching strategy is proposed to identify the potential combinational attack mode. In this way, malicious FDI attacks are excluded. Second, by constructing a descriptor switched sliding mode observer (SMO), the sparse FDI attacks and the system state are reconstructed effectively. Meanwhile, sufficient conditions of the error convergence can be derived. Finally, a numerical simulation is utilized to illustrate the applicability of the proposed theoretical derivation.

This brief studies a clustered multitask diffusion least mean-square strategy that accounts for communication delays in the inter- and intra-cluster information exchanges. We conduct detailed performance analysis and establish convergence criteria, both in mean and mean-square senses, to investigate the effect of inter-node communication delays on the stability and estimation performance. In particular, we derive convergence conditions and closed-form expressions for transient and steady-state mean square deviation. The concepts are verified using simulation examples, which show a precise match between theoretical and experimental steady-state MSD results.

In this article, the problem of collaborative tracking of an underwater target using autonomous surface vehicles (ASVs) is studied. Distance-based formation control with a collision-avoidance potential function is employed as a solution. A formation control protocol is devised and applied to the formation tracking problem. With the protocol, the vehicles form a desired formation around a moving target in order to continuously estimate its position, while the centroid of the formation tracks the target. Almost global stability is proved for the case with three tracking agents. A fully operational platform with four ASVs was built to implement the derived algorithms. One of the vehicles was used to simulate a target and the rest to form a triangular formation around it. Power usage of a naval vessel is highly affected by water resistance forces which increases significantly with the velocity. This was accounted for by adding an additional term to the formation tracking protocol, thereby increasing the overall system endurance. Experimental results are presented. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Recently, the first result on backstepping-based adaptive control of a 1-D linear hyperbolic partial differential equation (PDE) with an uncertain transport speed was presented. The system also had an uncertain, constant in-domain coefficient, and the derived controller achieved convergence to zero in the L ∞ -sense in finite time. In this paper, we extend that result to systems with a spatially varying in-domain coefficient, achieving asymptotic convergence to zero in the L ∞ -sense. Additionally, for the case of having a constant in-domain coefficient, the new method is shown to have a slightly improved finite-time convergence time. The theory is illustrated in simulations. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

We construct a method of dealing with the problem of overparameterization in model reference adaptive control (MRAC) of 2 ×2 linear hyperbolic partial differential equations (PDEs). The method is based on linear interpolation of the uncertain parameters. The proposed method is demonstrated in simulations of the fluid mechanics of a drilling system, and compared to a previously derived, overparameterized MRAC scheme, showing improved tracking and convergence properties. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

In this paper, we propose enhancements to Beetle Antennae search &#x0028 BAS &#x0029 algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation &#x0028 ADAM &#x0029 update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer &#x0028 PSO &#x0029 and the original BAS algorithm.

© [2019 Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved. Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear and thus lead to model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving the model’s error convergence time as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it. Besides knowledge usage, the KM process entails knowledge discovery, knowledge sharing, and knowledge assimilation. Specifically, knowledge sharing and assimilation are based on distributing and combining ML models, so specific methods are proposed for combining models. Two use cases are used to evaluate the proposed KM architecture and methods. Exhaustive simulation results show that model-based KM provides the best error convergence time with reduced data being shared.

©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear thus leading into model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving models error convergence time, as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it. The research leading to these results has received funding from the European Commission through the METROHAUL project (G.A. nº 761727), from the Spanish MINECO TWINS project (TEC2017-90097-R), and from the Catalan Institution for Research and Advanced Studies (ICREA).

This paper proposes a method for online estimation of electrical parameters of interior permanent magnet synchronous machines (IPMSM) based on the recursive prediction error method (RPEM). The parameter-sensitivity functions (herein known as the gradient functions, Ψ T ) both in dynamic and steady -states are exploited for this purpose. The RPEM has been computed using the stochastic gradient algorithm (SGA). The scalar Hessian matrix, r[k] appearing in the algorithm has been analyzed for both its steady and dynamic states. Different combinations of Ψ T and r[k] -states have been simulated and compared with respect to performance when used for parameter adaptation. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings, we present a unified pipeline with onboard localization and a distributed, collision-free motion planning strategy that scales to a large number of vehicles. Since distributed collision avoidance strategies are known to result in gridlock, we also present a decentralized task assignment solution to deconflict vehicles. We experimentally validate our pipeline in simulation and hardware. The results show that our approach for solving the optimization problem associated with motion planning gives solutions within seconds in cases where general purpose solvers fail due to high complexity. In addition, our lightweight assignment strategy leads to successful and quicker formation convergence in 96-100% of all trials, whereas indefinite gridlocks occur without it for 33-50% of trials. By enabling large-scale, deconflicted coordination, this pipeline should help pave the way for anytime, anywhere deployment of aerial swarms., 8 main pages, 1 additional page, accepted to RA-L and IROS'20

This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises. Our variational Bayesian SGPR (VBSGPR) models jointly treat both the distributions of the inducing variables and hyperparameters as variational parameters, which enables the decomposability of the variational lower bound that in turn can be exploited for stochastic optimization. Such a stochastic optimization involves iteratively following the stochastic gradient of the variational lower bound to improve its estimates of the optimal variational distributions of the inducing variables and hyperparameters (and hence the predictive distribution) of our VBSGPR models and is guaranteed to achieve asymptotic convergence to them. We show that the stochastic gradient is an unbiased estimator of the exact gradient and can be computed in constant time per iteration, hence achieving scalability to big data. We empirically evaluate the performance of our proposed framework on two real-world, massive datasets., To appear in Proceedings of the International Joint Conference on Neural Networks 2019 (IJCNN'19)

Conventional evolutionary algorithms are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multi-tasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial evolutionary algorithm, leading to a faster convergence of the expensive problem. However, existing multifactorial evolutionary algorithms do not work well in solving multi-tasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing multifactorial evolutionary algorithm is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized multifactorial evolutionary algorithm, empirical studies have been conducted on eight multi-tasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed generalized multifactorial evolutionary algorithm works more efficiently for multi-tasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.

This paper obtained the "Best of Session (VAS-7) Award" © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Unmanned aerial vehicles (UAV) specifically drones have been used for surveillance, shipping and delivery, wildlife monitoring, disaster management etc. The increase on the number of drones in the airspace worldwide will lead necessarily to full autonomous drones. Given the expected huge number of drones, if they were operated by human pilots, the possibility to collide with each other could be too high. In this paper, deep reinforcement learning (DRL) architecture is proposed to make drones behave autonomously inside a suburb neighborhood environment. The environment in the simulator has plenty of obstacles such as trees, cables, parked cars and houses. In addition, there are also another drones, acting as moving obstacles, inside the environment while the learner drone has a goal to achieve. In this way the drone can be trained to detect stationary and moving obstacles inside the neighborhood and so the drones can be used safely in a public area in the future. The drone has a front camera and it can capture continuously depth images. Every depth image is part of the state used in DRL architecture. Also, another part of the state is the distance to the geo-fence (a virtual barrier on the environment) which is added as a scalar value. The agent will be rewarded negatively when it tries to overpass the geo-fence limits. In addition, angle to goal and elevation angle between the goal and the drone will be used as information to be added to the state. It is considered that these scalar values will improve the DRL performance and also the reward obtained. The drone is trained using Q-Network and its convergence and final reward are evaluated. The states containing image and several scalars are processed by a neural network that joints the two state parts into a unique flow. This neural network is named as Joint Neural Network (JNN) [1]. The training and test results show that the agent can successfully learn to avoid any obstacle in the environment. The results for three scenarios are very promising and the learner drone reaches the destination with a success rate 100% in first two tests and with a success rate 98% in the last test, this one with a total of three drones. This work has been funded by the Ministry of Science,Innovation and Universities of Spain under grant numberTRA2016-77012-R

With the further advancement of the research on marine environmental science, more and more attention has been paid to modern data monitoring technology. In order to obtain the current water quality information quickly, this paper combines the advantages of Canopy algorithm and FFCM algorithm, and proposes an improved Canopy-FFCM clustering algorithm. The algorithm firstly uses Canopy algorithm to quickly obtain the best clustering number, and then iterates through FFCM algorithm. Multiple simulations of the sample were performed by using the FCM algorithm, the FFCM algorithm, and the improved Canopy-FFCM algorithm, the experimental results show that compared with the traditional algorithm, the improved algorithm effectively reduces the time required for the analysis process and has good application value. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

We design an adaptive controller for a 2 × 2 linear hyperbolic PDE with uncertain boundary parameters where measurements are taken at both boundaries, while only one boundary is actuated. The uncertainty, which appears in the un-actuated boundary, is in a bilinear form, which allows us to use bilinear adaptive laws for which parameter estimate convergence is guaranteed under a simple, a priori verifiable, persistence of excitation criterion. The control objective is boundary set-point regulation where the set-point is dependent on one of the unknown parameters. Stability is proved in the L 2 -sense and all signals in the closed loop are shown to be bounded. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

We extend recent results regarding disturbance rejection control of n+m linear hyperbolic partial differential equations (PDEs) in a number of ways: 1) We allow spatially varying coefficients; 2) The disturbance is allowed to enter in the interior of the domain, and; 3) Rejection is achieved in minimum time. Additionally, we solve a tracking problem, where the tracking objective is achieved in finite, minimum time. We use a recently derived Fredholm transformation technique in addition to infinite-dimensional backstepping in our design. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Disturbances originating in one control loop of a large industrial plant can propagate far from the source, giving rise to plant-wide oscillations. The underlying interactions among the different control loops make it hard to identify the origin of such large scale disturbances. This paper studies the application of the convergent cross mapping (CCM) based technique to isolate the source of a plant-wide disturbance. The proposed scheme exploits the cause and effect relationships among the affected variables to find the source of disturbance. The states of the causative factors are estimated from the effect variable and the directionality of information flow is established using the correlation between the original and estimated signal. The method is applied to the industrial case study and is shown to be effective in isolating the disturbance origin. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Using atomic force microscopy (AFM) for studying soft, biological material has become increasingly popular in recent years. New approaches allow the use of recursive least squares estimation to identify the viscoelastic properties of a sample in AFM. As long as the regressor vector is persistently exciting (PE), exponential convergence of the parameters to be identified can be guaranteed. However, even exponential convergence can be slow. In this article, upper bounds on the parameter convergence is found, completely determined by the PE properties and least squares update law parameters. Furthermore, for a parameter vector which is piecewise constant at regular intervals, the time interval necessary for the error to converge to any specified upper limit is determined. For a soft sample in AFM, the viscoelastic properties can be spatially inhomogeneous. These properties can be spatially resolved by periodically tapping at discrete points along the sample. The results of this article then allows us to determine the time interval necessary at each tap in order to guarantee convergence to any specified fraction of the step-change in the parameters. Simulation results are presented, demonstrating the applicability of the approach. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper presents sufficient conditions for the existence of an equilibrium point of multi-terminal HVDC (MT-HVDC) network for offshore applications. Newton's method is used both as a computational tool and as the basis for proving the existence and uniqueness of the equilibrium. A simple but generalized dynamic model of the MT-HVDC grid for offshore wind farm applications is presented which is non-linear due to the constant power loads with droop regulation. The classic Kantorovitch theorem is used to define the requirements for the existence of the equilibrium and the super-convergence of Newton's method starting from voltages close to 1pu. Finding this equilibrium is equivalent to the power flow in power systems applications. Computational results corroborate the requirements for equilibrium as well as the convergence of the algorithm in a realistic MT-HVDC grid. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The Internet data traffic constant growth caused by the popularization of cloud services, mobile and social networks is requiring changes at the underlying networks in order to enable scalability whereas supporting a highly dynamic connectivity. Cost-effective and energy efficient solutions for flexible network subsystems are also required in order to provide future sustainable networks. In this paper, we present the design of a scalable cost-effective CWDM ROADM that can easily be upgraded to work with C/DWDM channel enabling future Metro-Access Networks convergence.

This work suggests an integration procedure for a ESS (Energy Storage System) control algorithm into optimization control strategies minimizing cost functions for a microgrid system. This approach is based on a modification of the optimization strategy for adding absorption and flotation stages after each bulk charge to preserve the battery lifetime. These stages are computed out of the optimization program to reduce both computation complexity and convergence problems. Simulation results have confirmed the feasibility of this procedure at expenses of only a slight cost function increase, which can be assumed to preserve the battery lifetime.

This paper focuses on the small-signal stability analysis of systems modelled as differential-algebraic equations and with inclusions of delays in both differential equations and algebraic constraints. The paper considers the general case for which the characteristic equation of the system is a series of infinite terms corresponding to an infinite number of delays. The expression of such a series and the conditions for its convergence are first derived analytically. Then, the effect on small-signal stability analysis is evaluated numerically through a Chebyshev discretization of the characteristic equations. Numerical appraisals focus on hybrid control systems recast into delay algebraic-differential equations as well as a benchmark dynamic power system model with inclusion of long transmission lines. European Commission

We address the problem of deploying a swarm of autonomous mobile robots toward building an ad hoc network of robotic sensors with spatial uniform density. For the purpose, each of the robots configures themselves into an area with geographical constraints through local interactions with two adjacent neighboring robots. The basic idea underlying this work is that robots can be thought of as liquid particles that change their positions conforming to the shape of the container they occupy. The main challenge is how to cope with the accuracy limitations of sensors and possible holes in the configuration. Considering such realistic conditions, the convergence of the proposed method is proved using Lyapunovpsilas theorem. The proposed method is verified to be effective through the simulation for the secure deployments of robotic sensor network.

Evolutionary algorithms (EAs) have shown to be promising in solving many-objective optimization problems (MaOPs), where the performance of these algorithms heavily depends on whether solutions that can accelerate convergence toward the Pareto front and maintaining a high degree of diversity will be selected from a set of nondominated solutions. In this paper, we propose a knee point-driven EA to solve MaOPs. Our basic idea is that knee points are naturally most preferred among nondominated solutions if no explicit user preferences are given. A bias toward the knee points in the nondominated solutions in the current population is shown to be an approximation of a bias toward a large hypervolume, thereby enhancing the convergence performance in many-objective optimization. In addition, as at most one solution will be identified as a knee point inside the neighborhood of each solution in the nondominated front, no additional diversity maintenance mechanisms need to be introduced in the proposed algorithm, considerably reducing the computational complexity compared to many existing multiobjective EAs for many-objective optimization. Experimental results on 16 test problems demonstrate the competitiveness of the proposed algorithm in terms of both solution quality and computational efficiency.

Consider a problem of forward error-correction for the additive white Gaussian noise (AWGN) channel. For finite blocklength codes, the backoff from the channel capacity is inversely proportional to the square root of the blocklength. In this paper, it is shown that the codes achieving this tradeoff must necessarily have peak-to-average power ratio (PAPR) proportional to logarithm of the blocklength. This is extended to codes approaching capacity slower, and to PAPR measured at the output of an orthogonal frequency division multiplexing modulator. As a by-product, the convergence of (Smith's) amplitude-constrained AWGN capacity to Shannon's classical formula is characterized in the regime of large amplitudes. This converse-type result builds upon recent contributions in the study of empirical output distributions of good channel codes., National Science Foundation (U.S.). Center for Science of Information, National Science Foundation (U.S.). Science and Technology Center (Grant CCF-0939370), National Science Foundation (U.S.) (CAREER Award CCF-12-53205)

This paper presents a study of the scaling with frequency (computational complexity) of preconditioned iterative solution, using the Multilevel Fast Multipole Method, of a class of radiation and scattering problems that exhibits particularly slow convergence: problems involving electrically large, open and concave geometries. A comparison is presented between a well-known state of the art preconditioner (ILU) and a recently introduced preconditioning method, the Multiscale Compressed Block Decomposition.

Adaptive filtering schemes exhibit a compromise between convergence speed and steady-state mean square error. Trying to overcome this trade-off, convex combination of adaptive filters have been recently developed for system identification achieving better performance than traditional approaches. The purpose of this work is to apply the convex combination strategy to single-channel and multichannel active noise control systems. In these systems it is necessary to take into account the secondary path between the adaptive filter output and the error sensor and the possible unavailability of the disturbance signal, which depends on the filtering scheme considered. Even though this strategy involves a higher computational burden than the classic adaptive filters, it exhibits a good performance in terms of convergence speed and steady-state mean square error., This work was supported in part by the Spanish Ministerio de Ciencia e Innovacion TEC2009-13741, Generalitat Valenciana PROMETEO 2009/0013, Generalitat Valenciana GV/2010/027, and Universitat Politecnica de Valencia PAID-06-09. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Boaz Rafaely.

In this paper we derive stability conditions of local minima and their convergence regions of the Hopfield neural network when the diagonal elements of the coefficient matrix are all nonzero. Then for the traveling salesman problem (TSP) we clarify the ranges of the weight values in the energy function and the range of values of the diagonal elements, so that the feasible solutions become stable and infeasible solutions become unstable. Simulations of the TSP show that the above criteria are valid and, by gradually decreasing diagonal elements, quality of solutions is drastically improved, compared with that of zero diagonal elements. >

The RRT* algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT* is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domain-specific extension heuristics: a distance metric and node extension method. We propose automatically deriving these two heuristics for RRT* by locally linearizing the domain dynamics and applying linear quadratic regulation (LQR). The resulting algorithm, LQR-RRT*, finds optimal plans in domains with complex or underactuated dynamics without requiring domain-specific design choices. We demonstrate its application in domains that are successively torque-limited, underactuated, and in belief space., National Science Foundation (U.S.) (Grant 019868), United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1051), United States. Air Force Office of Scientific Research (Grant AOARD-104135)

The standard Network Utility Maximization (NUM) problem has a static formulation, which fails to capture the temporal dynamics in modern networks. This work considers a dynamic version of the NUM problem by introducing additional constraints, referred to as delivery contracts. Each delivery contract specifies the amount of information that needs to be delivered over a certain time interval for a particular source and is motivated by applications such as video streaming or webpage loading. The existing distributed algorithms for the Network Utility Maximization problems are either only applicable for the static version of the problem or rely on dual decomposition and first-order (gradient or subgradient) methods, which are slow in convergence. In this work, we develop a distributed Newton-type algorithm for the dynamic problem, which is implemented in the primal space and involves computing the dual variables at each primal step. We propose a novel distributed iterative approach for calculating the dual variables with finite termination based on matrix splitting techniques. It can be shown that if the error level in the Newton direction (resulting from finite termination of dual iterations) is below a certain threshold, then the algorithm achieves local quadratic convergence rate to an error neighborhood of the optimal solution in the primal space. Simulation results demonstrate significant convergence rate improvement of our algorithm, relative to the existing first-order methods based on dual decomposition., National Science Foundation (U.S.) (Grant DMI-0545910), United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (R6756-G2), United States. Office of Naval Research. Multidisciplinary University Research Initiative (N000140810747), United States. Air Force Office of Scientific Research. Complex Networks Project

We consider continuous-time consensus seeking systems whose time-dependent interactions are cut-balanced, in the following sense: if a group of agents influences the remaining ones, the former group is also influenced by the remaining ones by at least a proportional amount. Models involving symmetric interconnections and models in which a weighted average of the agent values is conserved are special cases. We present a result guaranteeing the convergence of every cut-balanced system, and giving a sufficient condition on the evolving interaction topology for the limit values of two agents to be the same. This condition is also necessary up to a zero-measure subset of the initial conditions. Using the fact that our convergence requires no additional condition, we show that it also applies to systems where the agent connectivity and interactions are random, or endogenous, that is, determined by the agent values. We also derive corresponding results for discrete-time systems., National Science Foundation (U.S.) (Grant ECCS-0701623), Belgian American Educational Foundation, inc. (Fellowship), Belgian National Foundation for Scientific Research (Fellowship)

In this paper, we revisit standard results for singularly perturbed systems on the infinite time interval by employing tools from nonlinear contraction theory. This allows us to determine explicit bounds both on the rate of convergence of trajectories to the slow manifold, and on the distance between these trajectories and those of the reduced system. We illustrate the application of the proposed technique to the problem of retroactivity attenuation in biomolecular systems, that is, to the problem of attenuating the effects of output loading due to interconnection to downstream systems. By virtue of the explicit bounds, we can single out the key biochemical parameters to tune in order to enhance retroactivity attenuation. This provides design guidelines for synthetic biology devices that are robust to loading and can function as insulation devices just like insulating amplifiers work in electronics., National Science Foundation (U.S.). Division of Computing and Communication Foundations (NSF-CCF Grant 1058127)

Computational Intelligence Systems (CIS) is one of advanced softwares. CIS has been important position for solving single-objective / reverse / inverse and multi-objective design problems in engineering. The paper hybridise a CIS for optimisation with the concept of Nash-Equilibrium as an optimisation pre-conditioner to accelerate the optimisation process. The hybridised CIS (Hybrid Intelligence System) coupled to the Finite Element Analysis (FEA) tool and one type of Computer Aided Design (CAD) system; GiD is applied to solve an inverse engineering design problem; reconstruction of High Lift Systems (HLS). Numerical results obtained by the hybridised CIS are compared to the results obtained by the riginal CIS. The benefits of using the concept of Nash-Equilibrium are clearly demonstrated in terms of solution accuracy and optimisation efficiency.

Assuming that the output function of neurons is monotonic and differentiable at any interior point in the output range, the condition necessary for a vertex of a hypercube to become a local minimum of the Hopfield neural networks and the form of the convergence region to that minimum are clarified. Based on this, a method for analyzing and suppressing spurious states in the networks is derived. It is shown that all the spurious states of the traveling salesman problem (TSP) for the Hopfield original energy function can be suppressed by the method, and the validity of the method is demonstrated by computer simulations. >

In Wireless Mesh Networks (WMNs) the meshing architecture, consisting of a grid of mesh routers, provides connectivity services to different mesh client nodes. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Thus, finding optimal or near-optimal mesh router nodes placement is crucial to such networks. In this work we propose and evaluate Genetic Algorithms (GAs) for near-optimally solving the problem. In our approach we seek a two-fold optimization, namely, the maximization of the size of the giant component in the network and that of user coverage. The size of the giant component is considered here as a criteria for measuring network connectivity. GAs explore the solution space by means of a population of individuals, which are evaluated, selected, crossed and mutated to reproduce new individuals of better quality. The fitness of individuals is measured with respect to network connectivity and user coverage being the former a primary objective and the later a secondary one. Several genetic operators have been considered in implementing GAs in order to find the configuration that works best for the problem. We have experimentally evaluated the proposed GAs using a benchmark of generated instances varying from small to large size. In order to evaluate the quality of achieved solutions for different possible client distributions, instances have been generated using different distributions of mesh clients (Uniform, Normal, Exponential and Weibull). The experimental results showed the efficiency of the GAs for computing high quality solutions of mesh router nodes placement in WMNs.

In this paper, we present experimental results evaluating the performance of the scalable-TCP, HS-TCP, BIC-TCP, FAST-TCP, and H-TCP proposals in a series of benchmark tests. In summary, we find that both scalable-TCP and FAST-TCP consistently exhibit substantial unfairness, even when competing flows share identical network path characteristics. Scalable-TCP, HS-TCP, FAST-TCP, and BIC-TCP all exhibit much greater RTT unfairness than does standard TCP, to the extent that long RTT flows may be completely starved of bandwidth. Scalable-TCP, HS-TCP, and BIC-TCP all exhibit slow convergence and sustained unfairness following changes in network conditions such as the start-up of a new flow. FAST-TCP exhibits complex convergence behavior.