Terms Of Convergence Behavior Research Articles

Selective Harmonic Elimination in a Multilevel Inverter Using Multi-Criteria Search Enhanced Firefly Algorithm

This research paper proposes the new multi-criteria search based enhanced firefly algorithm for solving selective harmonic elimination in a multilevel inverter. This new enhanced firefly utilizes adaptive nature of social and cognitive components to find the global optima. To see the effectiveness of the proposed algorithm and for the evaluation of results, a three phase nine level cascaded multilevel inverter is used. It is compared with existing meta-heuristic algorithms namely particle swarm optimization and firefly algorithm to validate its effectiveness. Crucial parameters for optimization, including population size and number of iterations, are kept same for comparison. For comparison, total harmonic distortion and convergence behaviour of algorithms against various modulation index values are considered. Moreover, results have clearly indicated that the proposed algorithm has surpassed particle swarm optimization and firefly algorithms in terms of convergence behaviour by attaining lower fitness value in lesser number of iterations. Finally, the experimental validation of selective harmonic elimination in multi-level inverter is also performed and analyzed.

High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation

With the rapid development of the Internet of Things (IoT), federated learning (FL) has been widely used to obtain insights from collected data while preserving data privacy. Differential privacy (DP) is an additive noise scheme that has been widely studied as a privacy-preserving approach on FL. However, privacy protection under DP usually comes at the cost of model accuracy for the underlying FL process. In this paper, we propose a novel low-cost (for both communication and computational overhead) adaptive noise perturbation/masking scheme to protect FL clients’ privacy without degrading the global model accuracy. In particular, we set the magnitude of the additive noise to adaptively change with the magnitude of the local model updates. Then, a direction-based filtering scheme is used to accelerate the convergence of the FL model. A maximum tolerable noise bound for local clients is derived using the central limit theorem (CLT). The designed noise maximizes privacy protection for clients while preserving the accuracy and convergence rate of the FL model , as a result of the noise cancelling out and forming a more concentrated distribution after the aggregation operation on the server. We theoretically prove that FL with the proposed noise perturbation scheme retains the same accuracy and convergence rate (O(1/T) for convex loss functions and O(1/T) for non-convex loss functions) as that of non-private FL with SGD. We also evaluate the performance of the proposed scheme in terms of convergence behavior, computational efficiency, and privacy protection against state-of-the-art privacy inference attacks on real-world datasets. Experimental results show that FL with our proposed perturbation scheme outperforms DP in the accuracy and convergence rate of the FL model in both client dropout and non-client dropout scenarios. Compared with DP, our proposed scheme does not incur additional computational and communication overhead. Our approach provides DP-comparable or better effectiveness in defending against privacy attacks under the same global model accuracy.

Comparison of cell- and vertex-centered finite-volume schemes for flow in fractured porous media

Flow in fractured porous media is of high relevance in a variety of geotechnical applications, given the fact that they ubiquitously occur in nature and that they can have a substantial impact on the hydraulic properties of rock. As a response to this, an active field of research has developed over the past decades, focusing on the development of mathematical models and numerical methods to describe and simulate the flow processes in fractured rock. In this work, we present a comparison of different finite volume-based numerical schemes for the simulation of flow in fractured porous media by means of numerical experiments. Two novel vertex-centered approaches are presented and compared to well-established numerical schemes in terms of convergence behavior and their performance on benchmark studies taken from the literature. The new schemes show to produce results that are in good agreement with those of established methods while leading to less degrees of freedom on unstructured simplex grids.

Nearest Kronecker Product Decomposition Based Linear-in-The-Parameters Nonlinear Filters

A linear-in-the-parameters nonlinear filter consists of a functional expansion block, which expands the input signal to a higher dimensional space nonlinearly, followed by an adaptive weight network. The number of weights to be updated depends on the type and order of the functional expansion used. When applied to a nonlinear system identification task, as the degree of the nonlinearity of the system is usually not known a priori, linear-in-the-parameters nonlinear filters are required to update a large number of coefficients to effectively model the nonlinear system. However, all the weights of the nonlinear filter may not contribute significantly to the identified model. We show via simulation experiments that, the weight vector of a linear-in-the-parameters nonlinear filter usually exhibits a low-rank nature. To take advantage of this observation, this paper proposes a class of linear-in-the-parameters nonlinear filters based on the nearest Kronecker product decomposition. The performance of the proposed filters is superior in terms of convergence behaviour as well as tracking ability in comparison to their traditional linear-in-the-parameters nonlinear filter counterparts, when tested for nonlinear system identification. Furthermore, the proposed nearest Kronecker product decomposition-based linear-in-the-parameters nonlinear filters has been shown to provide improved noise mitigation capabilities in a nonlinear active noise control scenario.

A hybrid testing method based on adaptive feed-forward filters

Hybrid testing is a technique that allows tests on components of complex systems with realistic boundary conditions. This can be achieved by coupling test rigs with simulation models of the overall system. Applications of hybrid testing to problems involving complex flexible structures demand careful controller design which may be prone to changes in system dynamics or model inaccuracies. Adaptive feed-forward filters offer a robust approach to coupling experimental and virtual subcomponents. In this paper, a framework based on adaptive feed-forward filters with harmonic basis functions is presented. Within the framework, a Least-Means-Squares adaption law and a Recursive-Least-Square adaption law using QR-factorization are proposed. The methods permit an automated work-flow of identification and control, and allow the coupling of structures with multiple degree-of-freedom interfaces. Questions related to measurement and actuation of interfaces are discussed. The methods are successfully applied to a double-clamped beam with an interface composed of two degrees of freedom. The results show the agreement of coupled test and reference system in the frequency domain as well as the ability of the methods to couple systems with arbitrary harmonic excitations. The experiments, furthermore, include a comparison of the different adaption laws in terms of convergence behavior.

Computational issues in fitting joint frailty models for recurrent events with an associated terminal event

Background and objective: Joint frailty regression models are intended for the analysis of recurrent event times in the presence of informative drop-outs. They have been proposed for clinical trials to estimate the effect of some treatment on the rate of recurrent heart failure hospitalisations in the presence of drop-outs due to cardiovascular death. Whereas a R-software-package for fitting joint frailty models is available, some technical issues have to be solved in order to use SASⓇ11SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. software, which is required in the regulatory environment of clinical trials.Methods: First, we demonstrate how to solve these issues by deriving proper likelihood-decompositions, in particular for the case of non-normally distributed random terms. Second, we perform a simulation study to evaluate the accuracy of different software-implementations (in SAS and R) in terms of convergence behavior, bias of model parameter estimates and coverage probabilities of confidence intervals. Therefore we developed SAS macros that facilitate the analysis and simulation of joint frailty data. These are provided as supplementary material along with comprehensive manuals.Results: Whereas estimates for regression coefficients are unbiased irrespective of the software, the bias of the remaining (nuisance) parameter estimates strongly depends on the software: SAS is shown to be much more efficient in avoiding bias compared to R. However, even in SAS a careful choice of the implementation is required to get reliable results, in particular for the joint gamma frailty model. By far the best performance is reached with a SAS-implementation that makes use of the probability integral transformation method.Conclusions: We have shown, that getting reliable results from joint frailty models is not straightforward and users should be aware about the computational options between and within software packages. Based on our simulation study, we elaborate recommendations on these options. In addition, our provided SAS macros may encourage statistical practitioners to apply these models in clinical trials with recurrent event data and potentially informative drop-outs.

An optimization based output power regulation in small modular reactors

Efficient controller tuning for novel designs of nuclear reactors is an ongoing research issue. An offline optimal PI regulator tuning has been accordingly addressed herein for the power control of a sodium-cooled small modular reactor. A dynamic model has been developed employing a 1-Fuel/2-Coolant (1F/2C) layout for the core and a permanent magnet dc motor as the control rod governor. A state space representation is characterized and the pertaining stability region is delineated in the (kP,kI) plane for the plant linearized at the nominal operating point. Upon selection of an appropriate search scope within this zone, Differential Evolution (DE) and Firefly optimization algorithms are conducted in parallel to minimize certain error-based performance measures for the closed loop system. The step response for the closed loop nonlinear plant is resorted in this regard which would yield optimal PI pairs for each performance index. The two optimization schemes are thus compared in terms of convergence behavior and it can be figured out that the DE approach outperforms the Firefly scheme in this work. A less rippled convergence regime steadily stabilized at the optimal point is inspected in the former strategy. The latter method though quickly situates at the proximity of the optimal location, experiences persistent fluctuations therein which calls for a deeper search space comprising more samples.

A Riemannian trust‐region method for low‐rank tensor completion

SummaryThe goal of tensor completion is to fill in missing entries of a partially known tensor (possibly including some noise) under a low‐rank constraint. This may be formulated as a least‐squares problem. The set of tensors of a given multilinear rank is known to admit a Riemannian manifold structure; thus, methods of Riemannian optimization are applicable. In our work, we derive the Riemannian Hessian of an objective function on the low‐rank tensor manifolds using the Weingarten map, a concept from differential geometry. We discuss the convergence properties of Riemannian trust‐region methods based on the exact Hessian and standard approximations, both theoretically and numerically. We compare our approach with Riemannian tensor completion methods from recent literature, both in terms of convergence behavior and computational complexity. Our examples include the completion of randomly generated data with and without noise and the recovery of multilinear data from survey statistics.

Multichannel Acoustic Echo Cancellation in the Wave Domain With Increased Robustness to Nonuniqueness

Acoustic echo cancellation (AEC) is significantly more challenging in the case of multichannel reproduction compared to the single-channel case. This is mainly due to the so-called nonuniqueness problem, which becomes especially severe for massive multichannel reproduction systems like, e.g., wave field synthesis systems or higher-order ambisonics, where the different loudspeaker signals are often strongly correlated. In this paper, a multichannel wave-domain AEC structure is proposed that maintains robustness without altering the loudspeaker signals, as is commonly required to preserve the reproduction quality. The proposed method exploits the dominance of certain couplings in the wave-domain representation of the loudspeaker-enclosure-microphone system. In this representation, preferring a strong weight of the dominant couplings in the identified system improves the system identification performance. A modification of the well-known generalized frequency-domain adaptive filtering algorithm is then presented to implement the proposed approach, including a self-contained description of all necessary components. An experimental evaluation compares the proposed method to competing approaches and shows that the proposed method compares well in terms of convergence behavior while avoiding the performance degradation, which is intrinsic to other approaches.

Finite element simulation of articular contact mechanics with quadratic tetrahedral elements

Although it is easier to generate finite element discretizations with tetrahedral elements, trilinear hexahedral (HEX8) elements are more often used in simulations of articular contact mechanics. This is due to numerical shortcomings of linear tetrahedral (TET4) elements, limited availability of quadratic tetrahedron elements in combination with effective contact algorithms, and the perceived increased computational expense of quadratic finite elements. In this study we implemented both ten-node (TET10) and fifteen-node (TET15) quadratic tetrahedral elements in FEBio (www.febio.org) and compared their accuracy, robustness in terms of convergence behavior and computational cost for simulations relevant to articular contact mechanics. Suitable volume integration and surface integration rules were determined by comparing the results of several benchmark contact problems. The results demonstrated that the surface integration rule used to evaluate the contact integrals for quadratic elements affected both convergence behavior and accuracy of predicted stresses. The computational expense and robustness of both quadratic tetrahedral formulations compared favorably to the HEX8 models. Of note, the TET15 element demonstrated superior convergence behavior and lower computational cost than both the TET10 and HEX8 elements for meshes with similar numbers of degrees of freedom in the contact problems that we examined. Finally, the excellent accuracy and relative efficiency of these quadratic tetrahedral elements was illustrated by comparing their predictions with those for a HEX8 mesh for simulation of articular contact in a fully validated model of the hip. These results demonstrate that TET10 and TET15 elements provide viable alternatives to HEX8 elements for simulation of articular contact mechanics.

The Study and Simulation of Adaptive Algorithms in Echo Cancellation of an Acoustic Signal

Echo signal is the delayed form of an electrical or acoustic signal and it occurs when it returns to its source, in other words when acoustic signal finds its way from sending route through receiving one. One of the important parameters in discussing echo is delay time. In practical applications, if round trip time exceeds 30 milliseconds and echo power also exceeds 30 decibels, echo cancellation should be done. Today given the developments in utilizing communication system and transmitting acoustic information, echo cancellation becomes very important. There are different algorithms for cancelling echo of acoustic signals and each of them has both advantages and disadvantages. Adaptive filters are appropriate for echo cancellation. In such filters, minimization of the computational complexity and quick convergence of adapting is done within frequency domain owing to long impact response. In this study, different adaptive algorithms such as LMS, NLMS, VSLMS, VSNLMS and RLS have been suggested which can be used for echo cancellation and finally, a combination of them as an optimal algorithm was simulated for echo cancellation. In this paper, the stages of determining filter coefficients and the level of computational work in terms of convergence behavior, simulation results and other methods’ results were compared and it was found that using NLMS and MAX-E algorithms would offer best results in different situations. Innovative aspects of this paper include using adaptive algorithms in their real time and we can minimize the computational work of these algorithms and maximize the convergence speed by selecting accurate filter coefficients and the window used in computations. Also, we can use it in current applications and even in sound conversations on some communication networks like internet. Other aspects include using adaptive algorithms in implementing echo cancellation that have better function and convergence compared to blind methods of echo cancellation and they contribute to quality improvement of sent signals in conversations.

Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles

State of charge (SOC) estimation is essential to battery management systems in electric vehicles (EVs) to ensure the safe operations of batteries and providing drivers with the remaining range of the EVs. A number of estimation algorithms have been developed to get an accurate SOC value because the SOC cannot be directly measured with sensors and is closely related to various factors, such as ambient temperature, current rate and battery aging. In this paper, two model-based adaptive algorithms, including the adaptive unscented Kalman filter (AUKF) and adaptive slide mode observer (ASMO) are applied and compared in terms of convergence behavior, tracking accuracy, computational cost and estimation robustness against parameter uncertainties of the battery model in SOC estimation. Two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the two algorithms. Comparison results show that the AUKF has merits in convergence ability and tracking accuracy with an accurate battery model, while the ASMO has lower computational cost and better estimation robustness against parameter uncertainties of the battery model.

Modified Differential Evolution algorithm for multi-objective VAR management

Reactive power or VAR management is one of the most crucial tasks required for proper operation and control of a power system. Reactive power management is carried out to reduce losses and to improve voltage profile in a power system, by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks or FACTS devices. VAR management provides better system security, improved power transfer capability and overall system operation. VAR management is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, the VAR management problem is formulated as a nonlinear constrained multi-objective optimization problem with equality and inequality constraints for minimization of real power losses and voltage deviation simultaneously. This multi-objective problem is solved using Differential Evolution (DE), which is a population based search algorithm. For avoiding the time and the effort in tuning the parameters of DE algorithm, a modified DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-objective VAR management problem. Weighing factor method has been employed for finding Pareto optimal set for VAR management problem. Fuzzy membership function is used to obtain the best compromise solution out of the available Pareto-optimal solutions. Effectiveness of the proposed modified DE algorithm based approach has been demonstrated on IEEE 30-bus system and is found to be superior to classical DE and its variants Self-adaptive Differential Evolution (SaDE) and Ensemble of Mutation and Crossover Strategies and Parameters in Differential Evolution (EPSDE) in terms of convergence behavior and solution quality.

APPLICATION OF PARTICLE SWARM OPTIMIZATION FOR ENHANCED CYCLIC STEAM STIMULATION IN A OFFSHORE HEAVY OIL RESERVOIR

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and L\'evy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able to improve the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The L\'evy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigated in this study, it is found that the temperature of the injection steam, CO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate have less impact on the objective function.

Application of Particle Swarm Optimization for Enhanced Cyclic Steam Stimulation in a Offshore Heavy Oil Reservoir

Three different variations of PSO algorithms, i.e. Canonical, Gaussian Bare-bone and Levy Bare-bone PSO, are tested to optimize the ultimate oil recovery of a large heavy oil reservoir. The performance of these algorithms was compared in terms of convergence behaviour and the final optimization results. It is found that, in general, all three types of PSO methods are able toimprove the objective function. The best objective function is found by using the Canonical PSO, while the other two methods give similar results. The Gaussian Bare-bone PSO may picks positions that are far away from the optimal solution. The Levy Bare-bone PSO has similar convergence behaviour as the Canonical PSO. For the specific optimization problem investigatedin this study, it is found that the temperature of the injection sCO2 composition in the injection gas, and the gas injection rates have bigger impact on the objective function, while steam injection rate and the liquid production rate ha ve less impact on the objective function.

Trust Region-Based Optimization for Maximum Mutual Information Estimation of HMMs in Speech Recognition

In this paper, we have proposed two novel optimization methods for discriminative training (DT) of hidden Markov models (HMMs) in speech recognition based on an efficient global optimization algorithm used to solve the so-called trust region (TR) problem, where a quadratic function is minimized under a spherical constraint. In the first method, maximum mutual information estimation (MMIE) of Gaussian mixture HMMs is formulated as a standard TR problem so that the efficient global optimization method can be used in each iteration to maximize the auxiliary function of discriminative training for speech recognition. In the second method, we propose to construct a new auxiliary function for DT of HMMs by adding a quadratic penalty term. The new auxiliary function is constructed to serve as first-order approximation as well as lower bound of the original discriminative objective function within a locality constraint. Due to the lower-bound property, the found optimal point of the new auxiliary function is guaranteed to improve the original discriminative objective function until it converges to a local optimum or stationary point of the objective function. Both TR-based optimization methods have been investigated on two standard large-vocabulary continuous speech recognition tasks, using the WSJ0 and Switchboard databases. Experimental results have shown that the proposed TR methods outperform the conventional EBW method in terms of convergence behavior as well as recognition performance.

Joint energy and reserve dispatch in a multi-area competitive market using time-varying differential evolution

In a deregulated electricity industry, competitive bidding for energy and reserve is increasingly gaining importance and playing a decisive role in maintaining system reliability. The transmission capacity limits have a significant impact on market cost, therefore these limits should also be considered while optimizing the joint dispatch. In this paper, a detailed comparison of classical differential evolution (DE) mutation strategies is carried out to study the role of tuning parameters. For avoiding the time and labor lost in tuning DE parameters, an improved DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-product, multi-area market dispatch problem with physical and operational constraints. The proposed approach effectively handles the complex constraints like reserve requirements, zonal power balance constraints, area spinning reserve constraints, tie-line constraints and reserve and capacity coupling constraints. The efficiency and accuracy of the proposed algorithm is tested on two different test systems and is found to be better than classical DE in terms of convergence behavior and solution quality.

A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems

We present a new multiobjective evolutionary algorithm (MOEA), called fast Pareto genetic algorithm (FastPGA), for the simultaneous optimization of multiple objectives where each solution evaluation is computationally- and/or financially-expensive. This is often the case when there are time or resource constraints involved in finding a solution. FastPGA utilizes a new ranking strategy that utilizes more information about Pareto dominance among solutions and niching relations. New genetic operators are employed to enhance the proposed algorithm's performance in terms of convergence behavior and computational effort as rapid convergence is of utmost concern and highly desired when solving expensive multiobjective optimization problems (MOPs). Computational results for a number of test problems indicate that FastPGA is a promising approach. FastPGA yields similar performance to that of the improved nondominated sorting genetic algorithm (NSGA-II), a widely-accepted benchmark in the MOEA research community. However, FastPGA outperforms NSGA-II when only a small number of solution evaluations are permitted, as would be the case when solving expensive MOPs.

Globally Optimal Training of Unit Boundaries in Unit Selection Text-to-Speech Synthesis

The level of quality that can be achieved by modern concatenative text-to-speech synthesis heavily depends on a judicious composition of the unit inventory used in the unit selection process. Unit boundary optimization, in particular, can make a huge difference in the users' perception of the concatenated acoustic waveform. This paper considers the iterative refinement of unit boundaries based on a data-driven feature extraction framework separately optimized for each boundary region. This guarantees a globally optimal cut point between any two matching units in the underlying inventory. The associated boundary training procedure is objectively characterized, first in terms of convergence behavior, and then by comparing the distributions in inter-unit discontinuity obtained before and after training. Experimental results underscore the viability of this approach for unit boundary optimization. Listening evidence also qualitatively exemplifies a noticeable reduction in the perception of discontinuity between concatenated acoustic units

Adaptive joint beamforming and B‐MMSE detection for CDMA signal reception under multipath interference

AbstractThe combination of antenna array beamforming with multiuser detection can effectively improve the detection efficiency of a wireless system under multipath interference, especially in a fast‐fading channel. This paper studies the performance of an adaptive beamformer incorporated with a block‐wise minimum mean square error(B‐MMSE) detector, which works on a unique signal frame characterized by training sequence preamble and data blocks segmented by zero‐bits. Both beam‐former weights updating and B‐MMSE detection are carried out by either least mean square (LMS) or recursive least square (RLS) algorithm. The comparison of the two adaptive algorithms applied to both beamformer and B‐MMSE detector will be made in terms of convergence behaviour and estimation mean square error. Various multipath patterns are considered to test the receiver's responding rapidity to changing multipath interference. The performance of the adaptive B‐MMSE detector is also compared with that of non‐adaptive version (i.e. through direct matrix inversion). The final performance in error probability simulation reveals that the RLS/B‐MMSE scheme outperforms non‐adaptive B‐MMSE by 1–5 dB, depending on the multipath channel delay profiles of concern. The obtained results also suggest that adaptive beamformer should use RLS algorithm for its fast and robust convergence property; while the B‐MMSE filter can choose either LMS or RLS algorithm depending on antenna array size, multipath severity and implementation complexity. Copyright © 2004 John Wiley & Sons, Ltd.