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Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

In recent decades, with the large-scale construction and rapid development of half-through arch bridges, as well as the increase of bridge service time, the suspender damage of arch bridge has become increasingly prominent. Therefore, real-time monitoring and regular detection of the health of arch bridge suspenders and timely detection and accurate judgment of the damage location and extent of suspenders are of great engineering significance for evaluating the reliability and residual life of arch bridge structures. By analyzing the main difficulties and existing problems of suspender damage identification, this paper takes the change rate of modal curvature as the damage index, introduces fireworks algorithm into the neural network model, optimizes the optimization process of neural network weight and threshold, and proposes a prediction model based on improved BP neural network by fireworks algorithm. According to the measured data of the damage degree of a long-span arch bridge in daily monitoring and on-site inspection, the proposed prediction method is applied to verify the effectiveness and accuracy in engineering health detection. On this basis, the improved BP neural network by fireworks algorithm is used to predict the suspender damage of a certain long-span half-through arch bridge, which provides an important basis for the actual bridge safety assessment. [ABSTRACT FROM AUTHOR]

During the operation of the power transformer, the fault location often occurs in the winding, and the fault phenomenon caused by insufficient short‐circuit withstand ability of the winding is particularly prominent. Based on this problem, this article proposes an improved symplectic geometry mode decomposition (ISGMD) algorithm to evaluate the short‐circuit withstand ability of winding. The realization method is to use the algorithm to perform time‐frequency decomposition of the vibration signal of the transformer, calculate time‐domain eigenvalues of the symplectic geometry components to constitute the eigenvector, utilize principal component analysis to reduce dimensionality of the data. Finally, apply the obtained data to train and test the extreme learning machine. The correctness of the algorithm is analyzed by simulating vibration signals. At last, the experiment is designed and the evaluation standard is proposed, and the practicability of the intelligent evaluation model based on the method is verified. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

To overcome the lengthy search time, massive space occupation, and overlong planned path of the traditional A* algorithm, this paper integrates the bidirectional search with the intelligent ant colony algorithm to obtain the heuristic function selection factor, and uses the factor to improve the evaluation function of the algorithm. The simulation results show that the improved algorithm achieved better dynamic navigation than the traditional A* algorithm both in search time and distance, featuring shorter path searching time and the algorithm running time. Therefore, the result of this research has effectively reduced the search time and enhanced the dynamic search. [ABSTRACT FROM AUTHOR]

Today, a directional wireless multimedia sensor network is a popular environment for solving the task assignment problem. Achieving long‐term frontal monitoring of moving objects is a crucial challenge for scholars in this field. Utilizing directional sensors equipped with energy harvesting is an effective technique to enhance network performance. In this way, the energy of nodes is no longer limited to batteries and can result in better frontal monitoring. In this method, each sensor categorizes tasks based on its own energy, allowing the determination of task execution nodes through bidding. The present study proposes a distributed algorithm for directional task assignment, EN‐DADA, based on energy harvesting. The task was first classified to determine the candidate node set that could execute the task, and then the task assignment was determined according to the monitoring income of each node in the candidate node set. The comparative analysis confirmed that the proposed method had advantages in terms of task revenue and network lifetime when using the same energy harvesting model. [ABSTRACT FROM AUTHOR]

Failure detection, isolation, and recovery is an essential requirement of any space mission design. Several spacecraft components, especially sensors, are prone to performance deviation due to intrinsic physical effects. For that reason, innovative approaches for the treatment of faults in onboard sensors are necessary. This work introduces the concept of agent-based fault detection and recovery for sensors used in satellite attitude determination and control. Its focuses on the implementation of an algorithm for addressing linear drift bias in gyroscopes. The algorithm was implemented using an agent-based architecture that can be integrated into the satellite's onboard software. Numerical simulations were carried out to show the effectiveness of this scheme in satellite's operations. The proposed algorithm showed a reduction of up to 50% in the stabilization time for the detumbling maneuver, and also an improvement in the pointing accuracy of up to 20% when it was applied in precise payload pointing procedures. The relevance of this contribution is its added value for optimizing the launch and early operation of small satellite missions, as well as, an enabler for innovative satellite functions, for instance, optical downlink communication. [ABSTRACT FROM AUTHOR]

Pertinent to the problems that wind turbine blades operate in complicated conditions, frequent failures and low replacement rate as well as rational inventory need, this paper, we build a fault tree model based on in-depth analysis of the failure causes. As the mechanical vibration of the wind turbine takes place first on the blades, the paper gives a detailed analysis to the Failure mechanism of blade vibration. Therefore the paper puts forward a dynamic prediction model of wind turbine blade failure number based on the grey theory. The relative error between its prediction and the field investigation data is less than 5%, meeting the actual needs of engineering and verifying the effectiveness and applicability of the proposed algorithm. It is of important engineering significance for it to provide a theoretical foundation for the failure analysis, failure research and inventory level of wind turbine blades. [ABSTRACT FROM AUTHOR]

This study presents a cellular lane-changing model where the traffic lanes are discretized into cells and formulates the lane-changing process as a multi-player non-zero-sum non-cooperative game in a connected environment where the real-time surrounding traffic data is shared. In a congested traffic scenario where a large quantity of mandatory lane-changing maneuvers are required simultaneously, the size of the game is extended dynamically (i.e., 2–5 players in this case, and it can be extended more if necessary) based on the traffic situation. Discretionary lane-changing maneuvers are also considered in the decision-making process to maximize the use of the capacity of each traffic lane, i.e., distributing the vehicles to each traffic lane as uniform as possible. Moreover, the competing vehicle on the target lane has more flexible actions (i.e., changing to other lanes) except for accelerating and decelerating actions. Thus, its temporary benefit is sometimes sacrificed by taking these actions to pursue the global benefit so that other vehicles could complete mandatory lane-changing maneuvers. It is a known fact that the space complexity expands exponentially as the game size increases, so a novel decomposition algorithm based on game theory is proposed to reduce the complexity and improve computational efficiency. Finally, a rule-based approach, a classic Nash equilibrium approach and the proposed decomposition algorithm are compared by the critical indicators such as the number of lane-changing vehicles, the maximum incoming queues during the process, the mean of computational time per iteration, etc. The performance shows no significant difference in the efficacy of lane-changing maneuvers among these approaches under the uncongested traffic condition. At the same time, the decomposition algorithm is more efficient in computing time than the classic Nash equilibrium approach. As the traffic gets congested, the game theory-based approaches prove more effective in lane-changing behaviours than the rule-based approach. Meanwhile, the decomposition algorithm outperforms the classic Nash equilibrium approach more significantly in terms of computational time. • A lane-changing model is discretized into cells. • The system is formulated as multi-player games in a connected environment. • A novel decomposition algorithm is proposed to reduce the complexity. • The proposed algorithm and other approaches are examined and compared. • The proposed algorithm shows high efficacy with a short computational time. [ABSTRACT FROM AUTHOR]

In this article, we propose a new method for principal component analysis (PCA), whose main objective is to capture natural 'blocking' structures in the variables. Further, the method, beyond selecting different variables for different components, also encourages the loadings of highly correlated variables to have the same magnitude. These two features often help in interpreting the principal components. To achieve these goals, a fusion penalty is introduced and the resulting optimization problem solved by an alternating block optimization algorithm. The method is applied to a number of simulated and real datasets and it is shown that it achieves the stated objectives. The supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

A two-stage algorithm is presented to determine an analytical model-based rational function by adaptively selecting sampling points. At the first stage, the rational interpolation function is created by using Stoer-Bulirsch algorithm and comparing the fitted model with the different ranks. At the second stage, the fitted model is validated by comparing with the first-principle model at a set of checking points. Some checking points will be added to the sampling series determined in the first stage to improve the fitted model. The new proposed algorithm is more accurate and effective than that using Stoer-Bulirsch interpolation only due to the fact that it appends the validation approach. When a highly oscillating curve is approximated, the validation ensures the fitting precision. With the two-stage MBPE algorithm, some time-consuming electromagnetic computations can be replaced by the interpolation from the fitting functions, which can increase the computational efficiency greatly. This method is used to calculate the samplings of the Green' s functions in a multi- layer horizontal soil to demonstrate its effectiveness. The number of samples used to accurately model such Green' s functions is less than 20 for the typical soil parameters. The computational time can be reduced by more than two orders of magnitude compared with that using the conventional uniform sampling technology. [ABSTRACT FROM AUTHOR]

• In this paper, the optimized GWO-MCSVM assessment model is constructed step by step, and the nonlinear convergence factor is used to replace the conventional convergence factor in the Grey Wolf algorithm and the thought of parameter initialization by tent chaotic mapping, which makes it more suitable for the data analysis of transformers. The improved GWO-MCSVM model ensures the rapidity and reliability of assessment. • The initialization of model parameters based on Tent chaotic map completes the preprocessing of model input data, determines the initial parameter selection of GWO parameter optimization process, and improves the optimization speed of the algorithm. • The parameters of GWO-MCSVM model are optimized through training samples of transformer operation parameters in practical projects, and the GWO-MCSVM model is tested through groups of test samples. Compared with the existing GA-SVM model and PSO-SVM model, it is verified that the model has high accuracy (accuracy rate is more than 90%) in transformer condition assessment, is superior to the existing assessment model, and has bright scalability. The continuous and reliable operation of the transformer is the basis to ensure the normal operation of the power system. Relevant departments collect multi-dimensional and multi-source heterogeneous parameter data during the operation, maintenance and repair of transformers. The effective information contained in the parameter data can directly reflect the current operating status of the transformer. On the basis of support vector machine and grey wolf algorithm, an improved GWO-MCSVM algorithm based on nonlinear convergence factor and Tent chaotic mapping is proposed. The algorithm parameters are optimized through training samples, and the results are evaluated and verified in the algorithm itself, so as to improve the accuracy of the status assessment results. Finally, the accuracy of assessment results of the algorithm proposed in this paper, existing genetic algorithms and particle swarm optimization algorithms are compared by evaluating multiple sets of measured samples. By comparison, the effectiveness of the algorithm proposed in this paper for transformer condition assessment has been verified. [ABSTRACT FROM AUTHOR]

An entry trajectory planning algorithm that generates flyable trajectories satisfying multiple no-fly zones and other path and terminal constraints is presented. The algorithm divides the entry trajectory into initial and glide phases. In the initial phase, the maximum value of heating rate is controlled accurately as the altitude of the quasi-equilibrium glide condition (QEGC) transition point is adjusted with a nominal angle of attack and a parameterized bank angle. In the glide phase, a geometry based planning algorithm that relies on the center positions and radius of the multiple no-fly zones, is proposed to calculate the waypoints of the virtual flight path. The magnitude and reversal point of the bank angle in each sub-phase are searched based on a reduced-order lateral system with the predictor-corrector method. The altitude is designed as an analytical function of energy, and the QEGC is employed to analytically solve the remaining state variables. Finally, a linear quadratic regulator is used to test the realization of the 3D trajectory. The algorithm is tested using the common aero vehicle-H model. The results demonstrate that the algorithm can rapidly generate the entry trajectory with multiple no-fly zone constraints and achieve complex flight missions satisfying all flight constraints. [ABSTRACT FROM AUTHOR]

The traditional multiview distributed video coding scheme using regional unified coding may lead to distortion problem of decoding estimation of the intense motion region. This paper presents a novel multiview distributed video coding algorithm. In the main perspective, gaining the intense motion regions of Wyner-Ziv frame according to the criteria of ROI, the algorithm extracts their DCT low-frequency coefficients for the entropy coding, in order to generate the best temporal side information. For the nonintense motion regions, the algorithm utilizes motion compensation interpolation (MCI) to generate side information. Finally, side information based on fusion of temporal and spatial side information will be gained. Experimental results show that our proposed algorithm can gain more accurate motion estimation in the intense motion region. Quality of decoded image is improved with the same transmit rate; thus, energy consumption of sensor nodes will be decreased ultimately. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm by combining the cheap but slow power method with the fast but expensive Arnoldi procedure periodically. The main feature of our method is that a weighted inner product is introduced when using an Arnoldi procedure to construct a basis for a Krylov subspace. Particularly, in each cycle, the weight matrix is changed adaptively according to the residual information obtained from the power iteration, with the aim of accelerating the computation of PageRank problems. The implementation and the convergence analysis of our new method are discussed in detail. Numerical experiments are reported to show the efficiency and convergence behavior of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a comprehensive review of Uncertainty-Based Multidisciplinary Design Optimization (UMDO) theory and the state of the art in UMDO methods for aerospace vehicles. UMDO has been widely acknowledged as an advanced methodology to address competing objectives of aerospace vehicle design, such as performance, cost, reliability and robustness. However the major challenges of UMDO, namely the computational complexity and organizational complexity caused by both time-consuming disciplinary analysis models and UMDO algorithms, still greatly hamper its application in aerospace engineering. In recent years there is a surge of research in this field aiming at solving these problems. The purpose of this paper is to review these existing approaches systematically, highlight research challenges and opportunities, and help guide future efforts. Firstly, the UMDO theory preliminaries are introduced to clarify the basic UMDO concepts and mathematical formulations, as well as provide a panoramic view of the general UMDO solving process. Then following the UMDO solving process, research progress of each key step is separately surveyed and discussed, specifically including uncertainty modeling, uncertainty propagation and analysis, optimization under uncertainty, and UMDO procedure. Finally some conclusions are given, and future research trends and prospects are discussed. [Copyright &y& Elsevier]

Purpose: Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging factors. The purpose of this study was to evaluate the value of two-dimensional (2D) and three-dimensional (3D) CT texture analysis (CTTA) in predicting LVI in LAC.Methods: A total of 149 LAC patients (50 LVI-present LACs and 99 LVI-absent LACs) were retrospectively enrolled. Clinical data and CT findings were analyzed to select independent clinical predictors. Texture features were extracted from 2D and 3D regions of interest (ROI) in 1.25 mm slice CT images. The 2D and 3D CTTA signatures were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The optimized CTTA signature was selected by comparing the predicting efficacy and clinical usefulness of 2D and 3D CTTA signatures. A CTTA nomogram was developed by integrating the optimized CTTA signature and clinical predictors, and its calibration, discrimination and clinical usefulness were evaluated.Results: Maximum diametre and spiculation were independent clinical predictors. 1125 texture features were extracted from 2D and 3D ROIs and reduced to 11 features to build 2D and 3D CTTA signatures. There was significant difference (P < 0.001) in AUC (area under the curve) between 2D signature (AUC, 0.938) and 3D signature (AUC, 0.753) in the training set. There was no significant difference (P = 0.056) in AUC between 2D signature (AUC, 0.856) and 3D signature (AUC, 0.701) in the test set. Decision curve analysis showed the 2D signature outperformed the 3D signature in terms of clinical usefulness. The 2D CTTA nomogram (AUC, 0.938 and 0.861, in the training and test sets), which incorporated the 2D signature and clinical predictors, showed a similar discrimination capability (P = 1.000 and 0.430, in the training and test sets) and clinical usefulness as the 2D signature, and outperformed the clinical model (AUC, 0.678 and 0.776, in the training and test sets).Conclusions: 2D CTTA signature performs better than 3D CTTA signature. The 2D CTTA nomogram with the 2D signature and clinical predictors incorporated provides the similar performance as the 2D signature for individual LVI prediction in LAC. [ABSTRACT FROM AUTHOR]