E. Xia

This paper considers the parameter vector estimation of target motion analysis (TMA) using the angle of arrival (AOA) in pseudolinear framework. Firstly, we review the pseudolinear estimator (PLE), the weighted instrumental variable estimator (WIVE) and the selective angle measurement (SAM) WIVE that uses the SAM scheme to improve the correlation between the instrumental variable matrix and the measurement one. Based on the SAM-WIVE, a SAM-penalized WIVE (SAM PWIVE) is presented where penalty terms are incorporated to reduce the mean square errors (MSEs) of the estimator. Then the SAM-PWIVE is revised into the SAM-bias compensated PWIVE (SAM-BCPWIVE) for bias correction. The optimal penalty matrix and penalty factor of the SAM-PWIVE are closely related to the true motion parameter vector and the uniform measurement noise variance. Further investigation shows that the bias compensation does not deviate the MSE optimality of the SAM-BCPWIVE from the SAM-PWIVE provided the best penalty factor. Although the penalty matrix can be handled as the prior covariance of the unknown zero-mean vector from the Bayesian perspective, the result of the SAM-WIVE timing its transpose is used to mimic the optimal penalty to dodge extra optimization effort. Finally extensive simulation experiments are completed to verify the performance superiorities of the proposed estimators.

Sign language recognition technology serves as a crucial bridge, fostering meaningful connections between deaf individuals and hearing individuals. This technological innovation plays a substantial role in promoting social inclusivity. Conventional sign language recognition methodologies that rely on static images are inadequate for capturing the dynamic characteristics and temporal information inherent in sign language. This limitation restricts their practical applicability in real-world scenarios. The proposed framework, called SSTA-ResT, integrates ResNet, soft spatiotemporal attention, and Transformer encoders to achieve this objective. The framework utilizes ResNet to extract robust spatial feature representations, employs the lightweight SSTA module for dual-path complementary representation enhancement to strengthen spatiotemporal associations, and leverages the Transformer encoder to capture long-range temporal dependencies. Experimental results on the LSA64 Argentine Sign Language (ASL) dataset demonstrate that the proposed method achieves an accuracy of 96.25%, a precision of 97.18%, and an F1 score of 0.9671. These results surpass the performance of existing methods across all metrics while maintaining a relatively low model parameter count of 11.66 M. This demonstrates the framework’s effectiveness and practicality for sign language video recognition tasks.

In this paper, a revisit is paid on the D-criterion based sensor path optimization in bearing-only target motion analysis (TMA). For a nearly constant velocity (CV) target motion model, the four-by-four Fisher information matrix (FIM) of the target starting location and velocity estimate vector in the D-criterion is brought under investigation. Firstly we analyze the structure of the FIM determinant increment regarding to the nextway point and develop its expression. Next the relation between maximizing the FIM determinant increment with respect to the nextway point and other criteria in references is studied. It is revealed that the FIM determinant increment is equal to the two-by-two Bayesian FIM determinant increment regarding to next target location estimate and serves as an upper bound of the majority of other criteria. Traditional numerical optimization of the increment cost function with respect to the nextway point is very time consuming. In this paper, the increment cost function is relaxed by eigenvalue decomposition into its lower bound. The candidate optimizers of the relaxed cost are analytically solved on different constraint boundaries and compared for a quick suboptimal solution. Finally, simulation results on target tracking cases verify the theoretical findings mentioned above, and more importantly, the advantages of the proposed method over other planning strategies.

Fatigue load modeling is crucial for optimizing and assessing the lifespan of floating wind turbines. This study addresses the complex characteristics of fatigue loads on floating wind turbines under the combined effects of wind and waves. We propose a fatigue load modeling approach based on Vine copula theory and machine learning. Firstly, we establish an optimal joint probability distribution model using Vine copula theory for the four-dimensional random variables (wind speed, wave height, wave period, and wind direction), with model fit assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Root Mean Square Error (RMSE). Secondly, representative wind and wave load conditions are determined using Monte Carlo sampling based on the established joint probability distribution model. Thirdly, fatigue load simulations are performed using the high-fidelity simulator OpenFAST to compute Damage Equivalent Load (DEL) values for critical components (blade root and tower base). Finally, utilizing measured wind and wave data from the Lianyungang Ocean Observatory in the East China Sea, simulation tests are conducted. We apply five commonly used machine learning models (Kriging, MLP, SVR, BNN, and RF) to develop DEL models for blade root and tower base. The results indicate that the RF model exhibits the smallest prediction error, not exceeding 3.9%, and demonstrates high accuracy, particularly in predicting flapwise fatigue loads at the blade root, achieving prediction accuracies of up to 99.97%. These findings underscore the effectiveness of our approach in accurately predicting fatigue loads under real-world conditions, which is essential for enhancing the reliability and efficiency of floating wind turbines.

Machine learning has been applied in continuous-variable quantum key distribution (CVQKD) systems to address the growing threat of quantum hacking attacks. However, the use of machine learning algorithms for detecting these attacks has uncovered a vulnerability to adversarial disturbances that can compromise security. By subtly perturbing the detection networks used in CVQKD, significant misclassifications can occur. To address this issue, we utilize an adversarial sample defense method based on non-negative matrix factorization (NMF), considering the nonlinearity and high-dimensional nature of CVQKD data. Specifically, we employ the Kernel Robust Manifold Non-negative Matrix Factorization (KRMNMF) algorithm to reconstruct input samples, reducing the impact of adversarial perturbations. Firstly, we extract attack features against CVQKD by considering the adversary known as Eve. Then, we design an Artificial Neural Network (ANN) detection model to identify these attacks. Next, we introduce adversarial perturbations into the data generated by Eve. Finally, we use the KRMNMF decomposition to extract features from CVQKD data and mitigate the influence of adversarial perturbations through reconstruction. Experimental results demonstrate that the application of KRMNMF can effectively defend against adversarial attacks to a certain extent. The accuracy of KRMNMF surpasses the commonly used Comdefend method by 32.2% and the JPEG method by 30.8%. Moreover, it exhibits an improvement of 20.8% compared to NMF and outperforms other NMF-related algorithms in terms of classification accuracy. Moreover, it can complement other defense strategies, thus enhancing the overall defensive capabilities of CVQKD systems.

The capacity for autonomous functionality serves as the fundamental ability and driving force for the cross-generational upgrading of unmanned aerial vehicles (UAVs). With the disruptive transformation of artificial intelligence technology, autonomous trajectory planning based on intelligent algorithms has emerged as a key technique for enhancing UAVs’ capacity for autonomous behavior, thus holding significant research value. To address the challenges of UAV trajectory planning in complex 3D environments, this paper proposes a multi-UAV cooperative trajectory-planning method based on a Modified Cheetah Optimization (MCO) algorithm. Firstly, a spatiotemporal cooperative trajectory planning model is established, incorporating UAV-cooperative constraints and performance constraints. Evaluation criteria, including fuel consumption, altitude, and threat distribution field cost functions, are introduced. Then, based on its parent Cheetah Optimization (CO) algorithm, the MCO algorithm incorporates a logistic chaotic mapping strategy and an adaptive search agent strategy, thereby improving the home-returning mechanism. Finally, extensive simulation experiments are conducted using a considerably large test dataset containing functions with the following four characteristics: unimodal, multimodal, separable, and inseparable. Meanwhile, a strategy for dimensionality reduction searching is employed to solve the problem of autonomous trajectory planning in real-world scenarios. The results of a conducted simulation demonstrate that the MCO algorithm outperforms several other related algorithms, showcasing smaller trajectory costs, a faster convergence speed, and stabler performance. The proposed algorithm exhibits a certain degree of correctness, effectiveness, and advancement in solving the problem of multi-UAV cooperative trajectory planning.