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With the development of the times, rumors spread rapidly on the Internet. Firstly, this paper establishes a reaction-diffusion system with Allee effect to describe the rumor spreading process and derives the necessary conditions for the emergence of Turing bifurcation. Next, a parameter identification approach utilizing optimal control theory is shown. Ultimately, the impact of the magnitude of the certain parameters in the objective function on parameter identification is examined through numerous parameter identifications in continuous space and various complex networks. Additionally, the convergence rates and error magnitudes of different algorithms for parameter identification are studied across different spatial structures. • The necessary conditions for the emergence of Turing bifurcation are investigated. • This paper applies optimal control theory to achieve parameter identification. • The effects of the three algorithms and topologies on parameter identification are studied. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly popular especially for scientific research and business fields where large volume of datasets need to be used, but it involves data security and privacy concerns. This paper mitigates such concerns by tracking and logging the history of shared data (i.e., provenance records) while preserving data privacy. This is a challenging problem in the data sharing scenario in this paper because the environment is decentralized and internal logs are not accessible publicly due to privacy concerns. We present ProvNet, a decentralized data sharing platform that can detect malicious users and provide secure provenance records using the newly proposed networked blockchain without disclosing raw data contents. Valid sharing records are collected and stored in the blocknet and misbehavior is detected with the stored provenance records according to our accountable protocols. We give a proof-of-concept implementation, and evaluation results show that the overhead is acceptable. • A networked blockchain, blocknet, stores data sharing records bi-directionally. • Using MinHash to check datasets' similarities, thus protect data ownership. • Chameleon Hash is leveraged to add future pointers without changing previous hashes. • A provenance graph built from blocknet, provides users' trackability on shared data. • Prototye performance is acceptable, an indexing system designed for optimization. [ABSTRACT FROM AUTHOR]

• Formulate a non-cooperative game with both UEs and MECs as players. • Prove the existence of and convergence to a Nash equilibrium. • Develop a set of algorithms to find the Nash equilibrium. • Demonstrate numerical examples of non-cooperative games with and without MECs' participation. In a newly emerged fog computing environment, various user equipments (UE) enhance their computing power and extend their battery lifetime by computation offloading to mobile edge cloud (MEC) servers. Such an environment is distributed and competitive in nature. In this paper, we take a game theoretical approach to computation offloading optimization in a fog computing environment. Such an approach captures and characterizes the nature of a competitive environment. The main contributions of the paper can be summarized as follows. First, we formulate a non-cooperative game with both UEs and MECs as players. Each UE attempts to minimize the execution time of its tasks with an energy constraint. Each MEC attempts to minimize the product of its power consumption for computation and execution time for allocated tasks. Second, we develop a heuristic algorithm for a UE to determine its "heuristically" best response to the current situation, an algorithm for an MEC to determine its best response to the current situation, and an iterative algorithm to find the Nash equilibrium. Third, we prove that our iterative algorithm converges to a Nash equilibrium. We demonstrate numerical examples of our non-cooperative games with and without MECs' participation. We observe that our iterative algorithm always quickly converges to a Nash equilibrium. The uniqueness of our non-cooperative games is that the strategy set of a player can be discrete and the payoff function of a player can be obtained by a heuristic algorithm for combinatorial optimization. To the best of the author's knowledge, there has been no such investigation of non-cooperative games based on combinatorial optimization for computation offloading optimization in a fog computing environment. [ABSTRACT FROM AUTHOR]

The paper presents a new method of optimization by the Layered Algorithm (LA). The proposed algorithm reduces the initial area to the sub-area containing the optimum. The proposed technique is based on the classification of the optimized function values (data sampled by sensors). The classification method uses a two-dimensional three-state Cellular Automata (CA). The CA classifies all area points ascribed to the CA cells based on their values. Specification of the categorization layers to the data gives a possibility to identify the different levels areas. Consequently, after analysis, a sub-area containing the optimum can be designated. In this paper, the proposed algorithm is applied to find the location of the airborne contaminant source by analyzing the concentration of released substances reported by mobile sensors distributed over the domain of interest. The Gaussian dispersion model simulation of the contaminant dispersion in the urbanized area is applied to generate the data used to verify the efficiency of the proposed Layered Algorithm. The LA successfully estimates the sub-area of the considered domain where the contamination source is located, taking to account data from sensors solely. • Optimization method reducing the initial area to the sub-area containing the optimum. • Verification of the Layered Algorithm (LA) with Selected Benchmark Functions. • Analysis of the LA efficiency to localize the airborne contaminant source. • LA results assessment by the measures: classification and accuracy error, relevance. [ABSTRACT FROM AUTHOR]

In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

Federated Learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the cloud, Federated Learning coordinates a group of devices to train a shared model in a distributed manner with the training data located on the devices. However, unbalanced data distribution and heterogeneous hardware configurations across different devices badly hurts the performance of collaborative model and severely impacts the overall training progress. Thus, a framework that can well balance the model accuracy and the training progress is urgently required. In this paper, we propose PAGroup, a privacy-aware grouping framework for high-performance Federated Learning. PAGroup intelligently divides the participating clients into different groups through carefully analyzing the privacy requirement of the training data and the solid social relationship of the participating clients. After that, PAGroup conducts data shaping and capability-ware training in order to improve the model performance while accelerating the overall training process. We evaluate the performance of PAGroup with both simulation and hardware testbed. The evaluation results show that PAGroup improves model accuracy up to 21%. Meanwhile, it decreases 81% communication overhead, 29% computation cost and 84% wall-clock time at best comparing with the baselines. • Specific private data that required by a learned model can be exchanged daily in certain social community. • PAGroup can share private date within task-specific groups organized according to strongly connected social community. • Training devices in a group can be viewed as a "super" client to compatibility to existing optimization algorithms. • The impact of local data scale, diversity, and label distribution to the model performance has been theoretically proved. [ABSTRACT FROM AUTHOR]

The second-order cone (SOC) is a class of simple convex cones and optimizing over them can be done more efficiently than with semidefinite programming. It is interesting both in theory and in practice to investigate which convex cones admit a representation using SOCs, given that they have a strong expressive ability. In this paper, we prove constructively that the cone of sums of nonnegative circuits (SONC) admits a SOC representation. Based on this, we give a new algorithm for unconstrained polynomial optimization via SOC programming. We also provide a hybrid numeric-symbolic scheme which combines the numerical procedure with a rounding-projection algorithm to obtain exact nonnegativity certificates. Numerical experiments demonstrate the efficiency of our algorithm for polynomials with fairly large degree and number of variables. [ABSTRACT FROM AUTHOR]

In this paper we show the first polynomial-time algorithm for the problem of minimizing submodular functions on the product of diamonds of finite size. This submodular function minimization problem is reduced to the membership problem for an associated polyhedron, which is equivalent to the optimization problem over the polyhedron, based on the ellipsoid method. The latter optimization problem is a generalization of the weighted fractional matroid matching problem. We give a combinatorial polynomial-time algorithm for this optimization problem by extending the result by Gijswijt and Pap (2013) [9]. [ABSTRACT FROM AUTHOR]

The study of greedy approximation in the context of convex optimization is becoming a promising research direction as greedy algorithms are actively being employed to construct sparse minimizers for convex functions with respect to given sets of elements. In this paper we propose a unified way of analyzing a certain kind of greedy-type algorithms for the minimization of convex functions on Banach spaces. Specifically, we define the class of Weak Biorthogonal Greedy Algorithms for convex optimization that contains a wide range of greedy algorithms. We analyze the introduced class of algorithms and establish the properties of convergence, rate of convergence, and numerical stability, which is understood in the sense that the steps of the algorithm are allowed to be performed not precisely but with controlled computational inaccuracies. We show that the following well-known algorithms for convex optimization — the Weak Chebyshev Greedy Algorithm (co) and the Weak Greedy Algorithm with Free Relaxation (co) — belong to this class, and introduce a new algorithm — the Rescaled Weak Relaxed Greedy Algorithm (co). Presented numerical experiments demonstrate the practical performance of the aforementioned greedy algorithms in the setting of convex minimization as compared to optimization with regularization, which is the conventional approach of constructing sparse minimizers. [ABSTRACT FROM AUTHOR]

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction. [ABSTRACT FROM AUTHOR]

Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of collocation points. In this paper, we present a novel and lightweight algorithm to conduct AD for physics-informed operator learning, which we call the trick of Zero Coordinate Shift (ZCS). Instead of making all sampled coordinates as leaf variables, ZCS introduces only one scalar-valued leaf variable for each spatial or temporal dimension, simplifying the wanted derivatives from "many-roots-many-leaves" to "one-root-many-leaves" whereby reverse-mode AD becomes directly utilisable. It has led to an outstanding performance leap by avoiding the duplication of the computational graph along the dimension of functions (physical parameters). ZCS is easy to implement with current deep learning libraries; our own implementation is achieved by extending the DeepXDE package. We carry out a comprehensive benchmark analysis and several case studies, training physics-informed DeepONets to solve partial differential equations (PDEs) without data. The results show that ZCS has persistently reduced GPU memory consumption and wall time for training by an order of magnitude, and such reduction factor scales with the number of functions. As a low-level optimisation technique, ZCS imposes no restrictions on data, physics (PDE) or network architecture and does not compromise training results from any aspect. • We present a novel algorithm to conduct automatic differentiation w.r.t. coordinates for physics-informed operator learning. • Our algorithm can reduce GPU memory and wall time for training physics-informed DeepONets by an order of magnitude. • Our algorithm neither affects training results nor imposes any restrictions on data, physics (PDE) or network architecture. [ABSTRACT FROM AUTHOR]

For agricultural tractors fixed ballast cannot provide high-efficiency traction under time-varying resistance in the field. Combining the characteristics of electric tractors with high-weight battery packs, this paper develops an intelligent ballast control system including a battery position adjustment (BPA) mechanism and an active ballasting control method. A tractor traction performance prediction model was developed to predict traction performance parameters and load-transfer in real time. The movement of the battery pack enables load-transfer based on the BPA. To ensure the lowest sliding rate of optimal tractive efficiency, an active ballasting control method based on a particle swarm optimisation algorithm was proposed that enabled control of the optimal battery position. The results of MATLAB/Simulink simulation indicate that the traction resistance after grading and pre-treatment not only reduced the frequency of change in the resistance signal but also retain the original trend. The results of a hardware in the loop test showed that the average tractive efficiency in the mode of active ballasting mode higher than that of the no ballasting mode (increased by 6.6%) and fixed ballasting mode (increased by 4.7%). The mean wheel slip in active ballasting mode was lower than that of the no ballasting mode (decreased by 10.3%) and the fixed ballasting mode (decreased by 4.9%). In addition, the front axle dynamic load distribution ratio in active ballasting mode was always greater than 0.2, which ensured the operational stability of the tractor. This study provides theoretical support and technical reference for optimising the traction performance of electric tractors. • An intelligent ballast control system with active load-transfer is developed. • The concept of batteries instead of ballasts. • Battery position adjustment mechanism (BPA) is designed. • Active ballasting control method based on traction performance prediction model. • The maximum average tractive efficiency is increased by 6.6%. [ABSTRACT FROM AUTHOR]

• The placement of data and the scheduling of request are studied in edge caching. • Network unreliability and limited storage and bandwidth resources are considered. • Two algorithms with different computation complexity and cache efficiency are developed. • The experiments are conducted to evaluate the performance of proposed caching strategies. Edge caching is a prevailing media delivery technology where data is hosted at the edge nodes with computing and storage capability in close proximity to users, in order to expand the backhaul network capacity and enhance users' quality of experience (QoE). The existing work in this area often neglects the fact that large-scale distributed cache networks are not particularly reliable and many edge nodes are prone to failure. In this paper we investigate and develop a novel, cooperative caching mechanism for content placement and request routing. We aim to minimize the content access delay and achieve the optimization in polynomial time, taking into account failures in an unreliable network environment with limited edge storage and bandwidth. We introduce two optimization algorithms: 1) a primal-dual algorithm that is based on the Lagrangian dual decomposition and subgradient method, and 2) a greedy-based approximation algorithm with a proven approximation ratio. Numerical results show that the proposed algorithms outperform other comparative approaches in synthetic and real network environments, and the approximation algorithm is particularly suitable for networking scenarios with sparse node connectivity and resources in short supply. [ABSTRACT FROM AUTHOR]

Finite element (FE) model of updating is the process of calibrating model parameters to improve the accuracy of numerical prediction. This goal is usually achieved by solving an optimization problem with the objective function measuring the misfit between simulated responses and experimental data. The sensitivity method is the predominate class of algorithms to FE model updating problems, and has opened a wide range of applications. However, this method often suffers from large errors due to linearization. To address this challenge, this paper proposes to utilize the unscented Kalman inversion (UKI) method to solve the FE model updating problems in a derivative-free manner. As an iterative optimization method, the UKI determines the new iterate using function values at a set of sample points rather than the derivative information. Implementation details, such as handling constraints during the optimization process, are presented in the paper. To validate the proposed UKI, model updating of a pedestrian bridge is conducted using the simulated and experimental data. Both validation examples show that the UKI is a competitive method for solving FE model updating problems. • The UKI method solves FE model updating problems in a derivative-free manner. • The UKI takes the effect of noises into account in a natural way. • Method is proposed to incorporate constraints during iterative process of the UKI. • The UKI is compared with the Levenberg–Marquardt algorithm. • The UKI is applied to updating a pedestrian bridge model using field test data. [ABSTRACT FROM AUTHOR]

Fourier analysis based optimization techniques have been developed for numerical schemes on structured grids and result in significant performance improvements. However, there lacks an effective optimization technique for numerical schemes on general unstructured grids, due to the mesh geometry complexity that makes Fourier transformation infeasible. To address this issue, an optimization framework based on machine learning is developed in this paper for numerical schemes on unstructured grids. The optimization of a compact high-order variational reconstruction on triangular grids is performed to illustrate the framework. An artificial neural network (ANN) is used to predict the optimal values of the derivative weights on cell interfaces that are the free parameters of the variational reconstruction, given the local geometry as input. The ANN is trained by minimizing the solution errors of a linear advection equation on a set of generated compact stencils, with a set of sampled sine waves as initial conditions. The developed optimization framework is applicable to general numerical schemes on unstructured grids as the training does not require explicit computation of dispersion or dissipation. Numerical results for inviscid flow problems show that the optimized variational finite volume scheme is remarkably more accurate and efficient than its non-optimized counterpart, demonstrating the effectiveness of the machine learning optimization. • A machine learning optimization framework for numerical schemes on unstructured grids. • A feedforward neural network to predict optimal parameter values of numerical schemes. • Training of the network by minimizing solution errors of a linear advection equation. • A machine learning optimized compact high-order variational reconstruction. • Generalization of the optimized variational reconstruction to nonlinear problems. [ABSTRACT FROM AUTHOR]

The ship lock system of the Three Gorges Hub(TGH) stands out as the most intricate navigation system for ships passing through dams, which is composed of the the world's largest ship lock and ship lift. It operates with features like multi-dam configurations, multi-chambers, multi-operation modes and batch processing. This paper addresses the complex challenge of optimizing the lock schedule, ship entry timings, and ship placements within the lock chamber. By analyzing navigational rules, operational characteristics, and existing problems of TGH, this research introduces an intelligent scheduling system framework. With the incorporation of multiple objectives and constraints, a mixed-integer nonlinear programming(MINLP) model is formulated. Subsequently, a hybrid intelligent algorithm based on simulated annealing and heuristic search is constructed to achieve the optimization of the lock schedule. The algorithm efficacy is validated using the real-world data from TGH. [ABSTRACT FROM AUTHOR]

Software systems rely on events for logging, coordination, handling unusual situations, and more. Monitoring event streams from systems that provide services can ensure the service complies with policies, regulations, and other business rules. Notably, monitoring to detect violations of rules as early as possible is much desired as, for example, the service may reclaim more resources from erring enactments. The primary goal of this paper is to develop techniques for detecting violations of time constraints as early as possible. We formalize a model for events and a language to specify constraints on event timing and data. We develop algorithms to detect violations of individual rules, then use a chase process to detect violations of acyclic sets of rules. We also present optimization techniques to reduce monitoring overhead. Finally, we implement and evaluate our algorithms through experiments to demonstrate our approach is feasible and beneficial. [ABSTRACT FROM AUTHOR]

The prediction of air pollution plays an important role in reducing the emission of air pollutants and guiding people to carry out early warning and control, so it attracts many scholars to conduct modeling and research on it. However, most of the current researches fail to quantify the uncertainty in prediction and only use traditional fuzzy information granulation to process data, resulting in the loss of much detail information. Therefore, this paper proposes a hybrid model based on decomposition and granular fuzzy information to solve these problems. The trend item and the Granulation fluctuation item are respectively predicted and the results are combined to obtain the change trend and fluctuation range of the sequence. This paper selects PM 2.5 concentrations of 3 cities. The experimental results show that the evaluation index of the prediction model is significantly lower than other benchmark models, and a variety of statistical methods are used to further verify the effectiveness of the prediction model. • Quantify the uncertainty of data to obtain a more complete physical meaning. • Fuzzy theory strategies more scientifically measure data characteristics. • The combination of trend term and fluctuation term process data more rationally. • The modified optimization algorithm expands the application scope of the model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method based on maximizing a periodogram-type function for parameter estimation of a two-dimensional (2-D) mono-component chirp signal model. The obtained estimators are called approximate least squares estimators (ALSEs). We also put forward a sequential algorithm for parameter estimation of a more general version of the model with multiple number of components. The main focus of the paper has been on developing the large-sample properties of the proposed estimators. Derived theoretical results show that the proposed estimators are strongly consistent and asymptotically normally distributed. Moreover, it is shown that the derived asymptotic distribution is identical to that of the traditional least squares estimators (LSEs). The numerical performance of the ALSEs and sequential ALSEs is demonstrated through extensive simulation studies. The results are positioned parallel to that obtained using the least squares method and 2-D multilag High Order Ambiguity function (2D-ml-HAF) for a comparative study. These simulations make use of the true values as the initial guesses in the optimization algorithm as using a fine grid search for this purpose is practically infeasible. We also provide a practical alternative for modeling of real world signals in an another set of simulation experiments. This involves a combination of two methods- 2D-ml-HAF estimators and genetic algorithm (GA). The results show that the proposed combination is a reasonable scheme to find initial guesses and provides satisfactory performance for varying signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

The optimal control of thermally convective flows is usually modeled by an optimization problem with constraints of Boussinesq equations that consist of the Navier-Stokes equation and an advection-diffusion equation. This optimal control problem is challenging from both theoretical analysis and algorithmic design perspectives. For example, the nonlinearity and coupling of fluid flows and energy transports prevent direct applications of gradient type algorithms in practice. In this paper, we propose an efficient numerical method to solve this problem based on the operator splitting and optimization techniques. In particular, we employ the Marchuk-Yanenko method leveraged by the L 2 -projection for the time discretization of the Boussinesq equations so that the Boussinesq equations are decomposed into some easier linear equations without any difficulty in deriving the corresponding adjoint system. Consequently, at each iteration, four easy linear advection-diffusion equations and two degenerated Stokes equations at each time step are needed to be solved for computing a gradient. Then, we apply the Bercovier-Pironneau finite element method for space discretization, and design a BFGS type algorithm for solving the fully discretized optimal control problem. We look into the structure of the problem, and design a meticulous strategy to seek step sizes for the BFGS efficiently. Efficiency of the numerical approach is promisingly validated by the results of some preliminary numerical experiments. [ABSTRACT FROM AUTHOR]

When dealing with optimization problems, the introduction of uncertainty will greatly increase the difficulty of solving the problem. The traditional reliability-based design optimization (RBDO) algorithm coupling uncertainty parameters and design variables, and solve optimization with optimization algorithms. The traditional RBDO scheme is easy to understand and realize, but it will greatly increase the calculation time, and it often does not get ideal results when solving some problems with high time complexity. The Kriging-based decoupled non-probability reliability-based design optimization (K-BRBDO) scheme proposed in this paper decouples uncertainty parameters and design variables, establishes a decoupled optimization system based on Kriging model, and proposes a new adaptive learning strategy which involves two stages of enrichment to improve the accuracy of the surrogate model in the region of interest. Finally, the effectiveness and accuracy of the optimization scheme are verified by two numerical examples and an engineering example. [ABSTRACT FROM AUTHOR]

Full-field imaging of guided ultrasonic waves is an emerging Nondestructive Testing (NDT) technique for the inspection of thin-walled structures. One of the most effective full-field processing techniques is the Local Wavenumber Estimation (LWE) which can be applied to detect various types of damage in both homogeneous and multilayer specimens. In this paper, we discuss the possibilities for optimizing the efficiency of the LWE algorithm to facilitate its practical applications. We propose several different signal processing approaches to optimize the algorithm's computational efficiency without sacrificing the damage imaging quality. We evaluate the influence of these improvements by measuring the computation time necessary to perform particular processing steps of the LWE process. We also provide the implementation guidelines for the proposed optimization techniques. The proposed improvements allow for a considerable reduction in the processing time of the LWE with respect to the baseline implementation of the algorithm, ranging from a factor of 10 to a factor of 24, depending on the input dataset. [ABSTRACT FROM AUTHOR]

The saliency prediction precision has improved rapidly with the development of deep learning technology, but the inference speed is slow due to the continuous deepening of networks. Hence, this paper proposes a fast saliency prediction model. Concretely, the siamese network backbone based on tailored EfficientNetV2 accelerates the inference speed while maintaining high performance. The shared parameters strategy further curbs parameter growth. Furthermore, we add multi-channel activation maps to optimize the fine features considering different channels and low-level visual features, which improves the interpretability of the model. Extensive experiments show that the proposed model achieves competitive performance on the standard benchmark datasets, and prove the effectiveness of our method in striking a balance between prediction accuracy and inference speed. Moreover, the small model size allows our method to be applied in edge devices. The code is available at: https://github.com/lscumt/fast-fixation-prediction. • We propose a fast saliency prediction model. It provides a new effective method to solve the problems of high computation and slow inference speed caused by the deep network structure commonly used in current fixation prediction models. • We propose a lightweight siamese network structure, and design a multi-channels activation optimization module to enhance the salience expression of target regions, finally achieving a good balance between prediction accuracy and inference speed. • We release the code for public to facilitate the communications in visual saliency community. The code is available at: https://github.com/lscumt/fast-fixationprediction. [ABSTRACT FROM AUTHOR]

• Balancing the optical powers of signals with the combination of FWM and ASE noise is one of the main challenges of optical system design and OSNR optimization of signals. • The input power values that maximize the OSNR value of the channel with the lowest OSNR value among the channels with the same power were determined. • The input power values that maximized the smallest OSNR value were obtained by both PSO with fixed inertia weight and PSO with linearly decreasing inertia weight. • The values acquired by the three methods were compared and remarkable results were obtained. In this paper, a new application of the particle swarm optimization (PSO) algorithm has been carried out in the field of the optical fiber (OF) communication system. In this theoretical work, the most suitable input channel powers to reduce the disruptive effects of four-wave mixing (FWM) occurring in OF and amplified spontaneous emission (ASE) noise from optical amplifiers are determined with the help of two different particle swarm optimization (PSO) in terms of inertia weight. The figure of merit is the optical signal-to-noise ratio (OSNR) of each channel at the output end of OF. Each of the input channel powers is optimized to maximize the OSNR of the channel with the lowest OSNR. The values obtained by both optimization methods are well good compared to those obtained with equal powers allocation (EPA). The best improvement in OSNR with PSO is obtained as 1.105846 dB (2.762778 %). Moreover, the performances of both algorithms and EPA are also comparatively evaluated in the study. [ABSTRACT FROM AUTHOR]

The lumped parameter modeling of a defective rolling element bearing involves the evaluation of several quantities whose values are characterized by a high degree of uncertainty. In this regard, the goal of this paper is to propose a procedure to estimate these unknown values by employing a multi-objective optimization technique. The objective functions to be minimized are global indicators that take into account the discrepancy between signal features computed on the basis of a numerical model and experimental data. The former are obtained by means of a lumped parameter model of a self-aligning ball bearing, while the latter are the outcomes of an extensive experimental study on a dedicated test bench. The values of the unknown parameters are constrained within fixed limits dictated by their physical properties. Furthermore, dependencies between the involved quantities are taken into account by introducing additional analytical relationships based on the physics of the problem. The values estimated through the proposed procedure allow to obtain a good accordance with the experimental results for a variety of test conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, air pollution occurs frequently, which has a huge negative impact on human health and urban ecosystem. Air quality index (AQI) can directly reflect the degree of air pollution. Accurate AQI trend prediction can provide reliable information for the prevention and control of air pollution, but traditional forecasting methods have limited performance. To this end, a dual-scale ensemble learning framework is proposed for the complex AQI time series prediction. First, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and sample entropy (SE) are used to decompose and reconstruct AQI series to reduce the difficulty of direct modeling. Then, according to the characteristics of high and low frequencies, the high-frequency components are predicted by the long short-term memory neural network (LSTM), and the low-frequency items are predicted by the regularized extreme learning machine (RELM). At the same time, the improved whale optimization algorithm (WOA) is used to optimize the hyper-parameters of RELM and LSTM models. Finally, the hybrid prediction model proposed in this paper predicts the AQI of four cities in China. This work effectively improves the prediction accuracy of AQI, which is of great significance to the sustainable development of the cities. • A dual-scale ensemble framework is proposed for air quality index prediction. • CEEMDAN technology is adopted to obtain multiple intrinsic mode functions. • An improved WOA algorithm based on dimension learning-based hunting is proposed. • Long short-term memory network is used to predict high-frequency components. • Regularized extremum learning machine is used to predict low-frequency components. [ABSTRACT FROM AUTHOR]

• The acoustic holography for any desired images is realized by introducing wave phase shifts. • The metamaterial-based hologram composed of helical units is invented. • A new optimization algorithm has been proposed for seek the phased arrays at the hologram plane. • The desired acoustic images with a high contrast ratio have been achieved numerically and experimentally. Holography is a technique to generate arbitrarily complex acoustic fields, fundamental to applications such as acoustic imaging, particle manipulation, and energy deposition. In this paper, we develop a new approach for designing acoustic holograms with high-resolution sound images against background noise interference. The metasurface-based holograms are composed of phased arrays, such as helical units, to adjust the propagating sound phase from 0 to 2π flexibly. The problem aims to seek the optimal phase distribution at the output of the metasurface, so that sounds focus a prescribed image at the projection plane. The projection plane is divided into the target and background pixels according to the desired resolution of images. The optimization problem is established to maximize the sound intensity of the target pixels and minimize that of the background pixels simultaneously. The optimality criterion is adopted to iteratively update the phase distribution in arrays based on the derived sensitivity. Finally, the effectiveness of the proposed approach is validated numerically and experimentally. [ABSTRACT FROM AUTHOR]

In multibody system models, body flexibility is introduced when the structure compliance is not negligible for an accurate description of the system behaviour. Flexible multibody systems provide a more detailed description of system kinematics and dynamics, introducing the effects of the body dynamics deformations. The influence of component higher frequency harmonics is often undesired during system working conditions. Therefore, in the design of a flexible multibody system the components and assembly modal properties are optimised to avoid resonances in the operating condition frequency ranges. However, in the system optimisation phase or when the system is upgraded to a new release, the least modifications would be ideally performed to optimise the system for its working conditions. In this context, there are no straightforward methods available to recognise which component mode-shapes require structural optimisation to avoid undesired dynamics behaviours. Based on an assembly-to-components modal approach, this paper proposes a method to detect the component mode-shapes responsible for undesired dynamics in the assembled multibody systems; the method is suitable for both numerical model or experimental applications. The inputs to the detection method are the components, sub-structures and assembly mode-shapes, together with the main frequencies exciting the system in operating conditions, to which operative deformations are associated. The procedure reveals the critical components and their mode-shapes requiring modifications. The detection method is conceived especially for systems requiring fast and continuous optimisation, e.g. high-performance racing vehicles, which are at least annually upgraded, or new improved releases of industrial complex machines. Firstly, the proposed method is numerically applied to the flexible multibody model of a slider–crank mechanism, detecting high-frequency vibrations in the piston stroke. Then, it is applied to a race motorcycle chassis frame to detect the critical component mode-shapes in realistic working conditions. Independent application of the procedure to numerical and experimental data of the motorcycle chassis pinpoints the same component mode-shapes as critical, on which optimisation efforts would be required. [Display omitted] [ABSTRACT FROM AUTHOR]

• Proposal of the new optimization methodology for squeal reduction in a brake system. • Applying the slip-dependent stability analysis combining with the model-based design. • Targeting maximum squeal instability reduction during entire braking scenario. • Effectiveness verified by comparing the average maximum power of each design. This paper proposes an improved disc brake system optimization method for squeal instability reduction using slip-dependent eigenvalue results. Although complex eigenvalue analysis is widely used for minimizing brake squeal instability, conventional optimization approaches still have the limitation of not being able to reflect slip rate-varying squeal instability characteristics. While relative angular velocity between the pad and disc declines due to braking, disc brake system instability gradually increases up to a specific peak velocity point and decreases until the vehicle stops, which means a maximum instability point exists during the braking process. Therefore, instability optimization should target the prevention of a maximum value during a braking scenario. The proposed optimization formulation is conducted considering maximum instability during full braking. To obtain braking time profiles, a model-based design method is employed and utilized instead of full finite element transient dynamic analysis to reduce computational cost. Kriging surrogate modeling is also used for solving the optimization problem and better express the multi-variable squeal problem. The proposed optimal design method produces minimal squeal instability during the full vehicle braking time range. The effectiveness of the proposed disc brake optimal design is demonstrated via acceleration power value comparison of the structure acceleration with that derived by conventional optimization approach. [ABSTRACT FROM AUTHOR]

• A simple modification to the passive RDSHI to separately control the reference beam and loop feedback power. • Equal power split for the reference beam confirmed best to enhance the detectable beat order number. • Influence of the loop coupler's coefficient upon maximal beat notes studied experimentally. • Larger number of beats and better performance confirmed for the improved RDSHI scheme. This paper presents an improved scheme of a passive recirculating delayed self-heterodyne interferometer (RDSHI) for laser linewidth measurement. The proposed scheme includes two additional fiber couplers in a regular RDSHI to independently control the reference beam and the loop signal for best system performance. The analysis and experimental results have confirmed that an initial equal power split for the reference beam is best suited to enhance the maximal number of detectable beat notes. The output coupling coefficient of the fiber loop has also been experimentally studied for system optimization. The experimental results have confirmed that the proposed modification to a basic RDSHI can achieve more detectable beat notes (14 vs. 12) and finer linewidth resolution (∼14% improvement) than a basic RDSHI in our test setup of 2-km-long delay line. [ABSTRACT FROM AUTHOR]

Having a specific understanding of the actual ingredient composition of products helps to calculate additional nutritional information, such as containing fatty and amino acids, minerals and vitamins, as well as to determine its environmental impacts. Unfortunately, producers rarely provide information on how much of each ingredient is in a product. Food manufacturers are, however, required to declare their products in terms of a label comprising an ingredient list (in descending order) and Big7 nutrient values. In this paper, we propose an automated approach for estimating ingredient contents in food products. First, we parse product labels to extract declared ingredients. Next, we exert mathematical formulations on the assumption that the weighted sum of Big7 ingredients as available from food compositional tables should resemble the product's declared overall Big7 composition. We apply mathematical optimization techniques to find the best fitting ingredient composition estimate. We apply the proposed method to a dataset of 1804 food products spanning 11 product categories. We find that 76% of these products could be analyzed by our approach, and a composition within the prescribed nutrient tolerances could be calculated, using 20% of the allowed tolerances per Big7 ingredient on average. The remaining 24% of the food products could still be estimated when relaxing one or multiple nutrient tolerances. A study with known ingredient compositions shows that estimates are within a 0.9% difference of products' actual recipes. Hence, the automated approach presented here allows for further analysis of large product quantities and provides possibilities for more intensive nutritional and ecological evaluations of food. • Automated approach for estimating ingredient contents in food products. • Considers regulatorily prescribed tolerances. • Large-scale evaluation with real and diverse food products spanning 11 categories. • Estimates are within a 0.9% difference of products' actual recipes. • Facilitates a variety of follow-up studies regarding nutrition and environmental footprint. [ABSTRACT FROM AUTHOR]

• A nonlinear ensemble RUL prediction method is proposed. • An online joint replacement-order model is formulated and optimized. • Experimental study of milling cutter life tests is carried out for demonstration. Remaining useful life (RUL) prediction and maintenance optimization are two critical and sequentially connected modules in the prognostics and health management of machines. Due to the advantages of obtaining more accurate RUL prediction results and the effectiveness of addressing replacement scheduling and spare parts provision dynamically, ensemble RUL prediction and online joint replacement-order optimization are paid specific attention to. Despite substantial works on those two aspects, there are still two limitations that compromise their performances in practical applications: 1) Existing ensemble RUL prediction methods neglected the nonlinear relationships among individual prediction models. 2) No online joint optimization model that utilizes ensemble RUL information is available. Faced with these two limitations, this paper first proposes a nonlinear ensemble RUL prediction method, which takes nonlinear relationships among models into consideration. Furthermore, an online joint replacement-order model is formulated using the ensemble RUL prediction results, and an iterated local search-based optimization algorithm is utilized for dynamically finding the near-optimal joint policies. Through the experimental study of milling cutter life tests, the proposed nonlinear ensemble RUL prediction method is verified with higher accuracy, and the joint optimization model utilizing the ensemble RUL results is shown to provide more effective joint policies. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear response variability in turbine bladed disks coupled with under-platform dampers (UPDs) is both numerically and experimentally investigated in detail. Non-uniqueness uncertainty of contact forces, which leads to different static force equilibria under the same nominal conditions, is shown as the main source of multiple responses. In the experiments, a previously designed test rig consisting of one blade and two UPDs is used to reveal the inherent kinematics of the friction force uncertainty. A large variability range is achieved in different tests while keeping all user-controlled inputs identical. On the computational side, the boundaries of the variability range are predicted by utilizing a recently developed numerical method. The loss factor of the system is exploited as an objective function to be minimized through an optimization algorithm. To compare the results, several cases with different excitation levels and pre-loads are studied. It is shown in most of the cases that the method is well-suited for the computation of nonlinear response boundaries. Experimentally measured variability range of the dynamic responses and contact forces is computationally obtained with a satisfactory level of accuracy. A slight deviation is also reported in few cases, particularly for highly nonlinear ones where the numerical model is not fully capable of replicating the non-ideal conditions achieved during the tests. [ABSTRACT FROM AUTHOR]

• Optimal design of Tuned Liquid Column Damper Inerter for single degree of freedom structures is studied. • A straightforward numerical approach is developed for its optimization. • An analytical solution for the optimal design of TLCDI parameters is derived. • Analyses are performed using random base excitations and real earthquake records. In this paper, the use of a novel passive control device defined as Tuned Liquid Column Damper Inerter (TLCDI) is studied to control the seismic response of structural systems. The TLCDI, recently introduced as an enhanced version of the conventional Tuned Liquid Column Damper, may achieve improved seismic performances by exploiting the mass amplification effect of the so-called inerter device. For this purpose, an optimization procedure for the design of the TLCDI based on a statistical linearization technique and the minimization of the structural displacement variance is proposed. Notably, by assuming a white noise base excitation and considering some additional approximations, pertinent closed-form expressions for the optimal TLCDI parameters are provided. The reliability of the proposed analytical solutions is proved by a comparison with numerical results obtained by a more computationally demanding iterative optimization technique on the original damped system. Finally, the efficiency of the control performance of TLCDI-controlled structures is examined using real recorded seismic signals as external excitation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a speech pseudonymization framework that utilizes cascaded and superposition-based voice modification modules. With increasing opportunities to use spoken dialogue systems nowadays, research regarding protecting the privacy of speaker information encapsulated in speech data is attracting attention. Pseudonymization, which is one method for voice privacy protection, aims to keep the intelligibility of speech while simultaneously suppressing speaker-specific information. One motivation of our framework is to achieve a reliable pseudonymization performance with light computation. To do this, we utilize the advantages of both machine learning-based and signal processing-based approaches. The advantages are (1) using signal processing-based methods parameterized with few hyperparameters and (2) using machine learning-based optimization to optimize all hyperparameters on the basis of black-box systems consisting of automatic speaker verification and automatic speech recognition. Our method of cascading signal processing modules, which are jointly optimized in a data-driven manner, can pseudonymize speech in a lightweight manner. Additionally, we discuss irreversible pseudonymization approaches and propose a superposition approach, yet another pseudonymization method that is more irreversible than the cascade method in terms of estimating the adequate parameters to recover the original signal. From the experimental results conducted under the VoicePrivacy 2020 protocols, we can demonstrate that (1) our cascade method succeeds in deteriorating the speaker recognition rate by over 24% while simultaneously improving the speech recognition rate by approximately 8% compared with a signal processing-based baseline system of VoicePrivacy 2020 and that (2) our superposition method works comparable to our cascade method in terms of pseudonymization performance. [Display omitted] • Lightweight and irreversible speech pseudonymization for protecting voice privacy. • Cascade or superposition of signal processing-based voice modification modules. • Parameter optimization of hyperparameters in a machine learning-based manner. • Advantages of signal processing and machine learning lead to effective performance. • The importance of irreversibility along with the proposal of an irreversible method. [ABSTRACT FROM AUTHOR]

Recently, reinforcement learning (RL) has been applied to task-oriented dialogue systems by using latent actions to solve shortcomings of supervised learning (SL). In this paper, we propose a multi-domain task-oriented dialogue system, called D ialogue System with O ptimizing a R ecurrent A ction Policy using Efficient Context (DORA), that uses SL, with subsequently applied RL to optimize dialogue systems using a recurrent dialogue policy. This dialogue policy recurrently generates explicit system actions as a both word-level and high-level policy. As a result, DORA is clearly optimized during both SL and RL steps by using an explicit system action policy that considers an efficient context instead of the entire dialogue history. The system actions are both interpretable and controllable, whereas the latent actions are not. DORA improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on MultiWOZ 2.1. • Recurrent action policy optimizes well the dialogue system with both supervised learning and reinforcement learning. • Explicit system actions enable effective reward shaping for dialogue policy optimization. • Optimized dialogue policy can be controlled by post-processing of system actions. • Dialogue system operates as a hybrid system by system action control. • Efficient input context can replace the entire dialogue history. [ABSTRACT FROM AUTHOR]

• Introduction of two novel concepts to improve accuracy of CFD-driven turbulence models. • Simultaneous training of multiple closure models captures coupling effects. • Multi-objective optimization enables informed model selection after training. • Trained RANS closures achieve excellent predictions for flow with highly coupled momentum and thermal fields. This paper introduces two novel concepts in data-driven turbulence modeling that enable the simultaneous development of multiple closure models and the training towards multiple objectives. The concepts extend the evolutionary framework by Weatheritt and Sandberg (2016) [1] , which derives interpretable and implementation-ready expressions from high-fidelity simulation data. By assigning a shared fitness value to the evolved closure models and utilizing the CFD-driven training approach by Zhao et al. (2020) [2] , the multi-expression training concept introduced here is able to account for the coupling between the trained models, i.e. Reynolds stress anisotropy, turbulent heat flux and turbulence production correction models. As a second concept, a multi-objective optimization algorithm is applied to the framework. The extension yields a diverse set of candidate models and allows a trade-off between the training objectives after analyzing the training results. In this study, the novel concepts are applied to a benchmark periodic hills case and a vertical natural convection flow. The predictions of mean flow quantities are improved compared to decoupled training strategies with distinct and robust improvements for strongly coupled momentum and thermal fields. The coupled training of closure models and the balancing of multiple training objectives are considered important capabilities on the path towards generalized data-driven turbulence models. [ABSTRACT FROM AUTHOR]

• An efficient milling parameter optimization method was presented. • Combining the off-line estimation and real-time monitoring strategy are proposed. • Random vector search based numerical optimization algorithm is built up. • Cutting force coefficient is estimated in real-time by spindle current monitoring. • Experiments in titanium alloy Ti5Al5Mo5VCrFe machining have been carried out. High efficiency milling is one of the main goals of CNC machining. Although roughing operation has low requirement for machining accuracy, the milling parameter selection usually need to consider the cutting force, milling stability, spindle torque and power. Besides, the tool wear in difficult-to-cut material machining is another very prominent problem. Therefore, how to achieve the high efficient milling parameters under the multi-constraint condition has been a severe challenge in the field of CNC machining. In this paper, a milling parameter optimization method for efficient rough machining is proposed by combining the off-line optimization and real-time monitoring. Firstly, a mathematical model of the optimization problem is established, which takes the machining efficiency as the objective, the spindle speed, radial and axial depth of cuts as the variables, and considers the multi-constraint, such as the basic parameter feasible region, cutting force, stability, spindle torque and power. Secondly, the quantitative relationship between the spindle three-phase current and milling force is established to realize the real-time estimation of cutting force coefficients. Furthermore, a numerical optimization method based on the random vector search is proposed, and the overall optimization procedure is given. Finally, a series of experimental verifications on the titanium alloy Ti5Al5Mo5VCrFe machining has been carried out, and the case study results show that the proposed parameter optimization method can greatly improve the machining efficiency, which proves the validity of the method. [ABSTRACT FROM AUTHOR]

• A control scheme of the camera's vibration isolator with time-varying stiffness is proposed. • A systematic methodology to optimize the vibration isolator stiffness control is presented. • Reinforcement learning optimization can learn a general variable stiffness policy. • This policy can satisfy the conflictive requirements on both image quality and efficiency. With the increasing demand for high-resolution remote sensing images, both the image quality and the image acquisition efficiency should be concerned for an agile remote sensing satellite. Effective vibration isolation is essential to ensure image quality since the attitude vibration could cause line-of-sight jitter and image blur. To improve the image acquisition efficiency, the capability of performing agile attitude maneuvers is another indispensable requirement of the satellite. These requirements mean an extreme level of attitude stability and vibration isolation from onboard disturbances. However, they have conflicting demands on the connection stiffness between the camera and the satellite bus, i.e., high on the former but low on the latter. By varying its stiffness, this paper develops a dynamic stiffness control scheme for the camera's vibration isolator, whose variable stiffness policies are learned from a reinforcement learning methodology with a feature of low online complexity. The proposed policy optimization algorithm for stiffness control has such merits as simultaneously optimizing both vibration isolation and attitude stabilization. Furthermore, it possesses an adaptive ability to deal with pervasive onboard disturbances. Simulation experiments demonstrate that the learned variable stiffness policies could strike a favorable balance between the agility and the image quality. [ABSTRACT FROM AUTHOR]

• The combined fronthaul network improves the quality of service by the edge computing. • The correction factor meets the physical constraints in network planning. • The performance of the network can be greatly improved by the proposed algorithm. The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay. [ABSTRACT FROM AUTHOR]

• Data-driven interval prediction models with guaranteed reliability bounds. • A new soft-constrained IPM to trade reliability for predictive accuracy. • An comparison of Scenario theoretic epistemic bounds on IPMs error probability. • Formal reliability intervals hold distribution-free and non-asymptotically. • The bounds quantify the level of epistemic uncertainty and are tighter for simpler predictors designs. Interval Predictor Models (IPMs) offer a non-probabilistic, interval-valued, characterization of the uncertainty affecting random data generating processes. IPMs are constructed directly from data, with no assumptions on the distributions of the uncertain factors driving the process, and are therefore exempt from the subjectivity induced by such a practice. The reliability of an IPM defines the probability of correct predictions for future samples and, in practice, its true value is always unknown due to finite samples sizes and limited understanding of the process. This paper proposes an overview of scenario optimization programs for the identification of IPMs. Traditional IPM identification methods are compared with a new scheme which softens the scenario constraints and exploits a trade-off between reliability and accuracy. The new methods allows prescribing predictors that achieve higher accuracy for a quantifiable reduction in the reliability. Scenario optimization theory is the mathematical tool used to prescribe formal epistemic bounds on the predictors reliability. A review of relevant theorems and bounds is proposed in this work. Scenario-based reliability bounds hold distribution-free, non asymptotically, and quantify the uncertainty affecting the model's ability to correctly predict future data. The applicability of the new approach is tested on three examples: i) on the modelling of a trigonometric function affected by a noise term, ii) on the identification of a black-box system-controller dynamic response model and, iii) on the modelling of the vibration response of a car suspension arm crossed by a crack of unknown length. The points of strength and limitations of the new IPM are discussed based on the accuracy, computational cost, and width of the resulting epistemic bounds. [ABSTRACT FROM AUTHOR]

Multimedia event detection is the task of detecting a specific event of interest in an user-generated video on websites. The most fundamental challenge facing this task lies in the enormously varying quality of the video as well as the high-level semantic abstraction of event inherently. In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance. Instead of treating the instances equally, we associate each instance with a reliability variable to indicate its importance and then select reliable instances for training. To measure the reliability of the varying instances precisely, we propose a visual-semantic guided loss by exploiting low-level feature from visual information together with instance-event similarity based high-level semantic feature. Motivated by curriculum learning, we introduce a negative elastic-net regularization term to start training the classifier with instances of high reliability and gradually taking the instances with relatively low reliability into consideration. An alternative optimization algorithm is developed to solve the proposed challenging non-convex non-smooth problem. Experimental results on standard datasets, i.e., TRECVID MEDTest 2013 and TRECVID MEDTest 2014, demonstrate the effectiveness and superiority of the proposed method to the baseline algorithms. • A visual-semantic guided loss is proposed to measure reliability of instance for event detection. • Training begins with high-reliability instances and gradually added instances of low reliability. • Promising experimental results show the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a parallel orbital-updating based optimization method for electronic structure calculations. With our method, the solution of the minimization problem for the Kohn-Sham energy functional with respect to N orbitals is replaced by the solution of N independent minimization problems for the energy functional with respect to one orbital and the orthogonalization of the N updated orbitals. This new method allows a two-level parallelization. This feature makes our approach has a great advantage in large scale parallel computing. The numerical experiments show that our new method is reliable and efficient. Hence, our new method has a great potential for large scale electronic structure calculations on modern supercomputers. • Propose a parallel orbital-updating based optimization approach for KS model. • The new approach has a feature of two-level parallelization. • Provide two practical parallel orbital-updating based optimization algorithms. • Numerical experiments show that these new algorithms are reliable and efficient. [ABSTRACT FROM AUTHOR]

• Damage quantification is proposed considering data-based uncertainties in model optimization. • Statistical properties of MAC are derived to evaluate mode shape match between data and model in the optimization. • An objective function and a stopping criterion are developed incorporating the variance estimates of modal parameters. • Damage localization is used to reduce the optimization parameter space. • The resulting framework is applied to quantify damage of a simulated beam experiment. Damage localization and quantification constitute different aspects of structural damage diagnosis, which are of particular interest in the Structural Health Monitoring field. Therein, a classical solution is model updating, where the parameters of a finite element model of the possibly damaged structure are optimized to match with the corresponding parameters estimated from its vibration responses. To avoid ill-posedness of the classical finite element updating problem, damage localization and quantification can be treated separately. First, the information about regions or clusters of possibly damaged elements in the structure is obtained by a damage localization method. Then, this information is used to reduce the number of parameters for damage quantification. A framework combining the advantages of methods for damage localization with model optimization is proposed in this paper. For the exploration of the clustered physical model space, a stochastic optimization algorithm is coupled with the evaluation of the statistical properties of the MAC and frequency differences between the numerical model and the estimated modes for an adequate treatment of the data-based uncertainties. Herein, the development of the statistical properties of the MAC estimate is an important step, which is based on a recent quadratic framework that is adapted to the context of the inner product between an estimated mode shape and a numerical mode shape. This statistical information is used in the formulation of the objective function as well as in a data-driven stopping criterion for the optimization search. The proposed framework is validated on numerical simulations of a beam model, where damage at multiple locations is quantified up to the clustering precision. [ABSTRACT FROM AUTHOR]

Matrix regression uses matrix data as input and directly selects the features from matrix data by employing several couples of left and right regression matrices. However, the existing matrix regression methods do not consider the relationship between different classes of data and cannot get discriminant left/right regression matrix, which results in poor classifications. In this paper, a margin-based discriminant embedding sparse matrix regression (MDESMR) model for image supervised feature selection is proposed. For each matrix data, a margin is first defined as the difference between two types of distances determined by the left/right regression matrix. Maximizing the average margin for all training matrix data can get the nonlinear discriminant embedding. Thus, a nonlinear embedding and its linear approximation can be obtained simultaneously. An alternative iterative optimization algorithm for solving the proposed model is also designed and the corresponding closed-form solutions in each iteration are found. Some experiments on several datasets demonstrate the superiority of our method. • Our model preserves the spatial information of elements in the original image data. • The margin of each matrix data is defined by using left/right regression matrices. • Maximizing the average margin can obtain the non-linear discriminant embedding. • Discriminative features are selected by the row sparsity of transformation matrix. • An iterative optimization algorithm is designed to solve the proposed model. • The effectiveness and superiority of our method are verified on several datasets. [ABSTRACT FROM AUTHOR]