Search Results (1,099)

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions. Full article

Electrochemical Impedance Spectroscopy (EIS) is a widely used technique for characterizing the electrode–electrolyte interface. EIS analysis can be very complex and tedious. In this work, a fitting algorithm written in C is implemented on OriginPro software to avoid the data import/export operation and speed up the analysis. An automated fitting procedure that assigns the initial parameters is implemented for the simplest equivalent circuit. In addition, the possibility of using a custom error function is explored, with results comparable to that of reference software. The developed algorithm is tested on two different case studies. Full article

Pain recognition based on multimodal physiological signals remains a challenge, not only because of the limited training data, but also due to the varying responses of individuals. In this article, we present a randomized modality mixing technique (Modmix) for multimodal data augmentation and a patchwise radial basis function (RBF) network designed to improve robustness in limited and highly heterogeneous data. Modmix generates new samples by randomly swapping modalities between existing data points, creating new data in a very simple but effective way. The RBF patch network divides the input into randomly selected, overlapping patches that capture local similarities between modalities. Each patch network is trained end-to-end using stochastic gradient descent. Moreover, the model’s performance is further improved by using multiple independently trained networks and combining them into a single decision. Experiments with the two different pain datasets X-ITE and BioVid were performed under limited training data conditions, where only approximately 30% of the original datasets were used for training. With both datasets the RBF patch network achieved significant improvements for a subset of subjects, resulting in a similar or even slightly better mean accuracy compared to competing related models such as random forest and support vector machine. Full article

The maritime wireless communication network (MWCN) faces challenges such as limited coverage, inaccurate channel state information (CSI), and the sparse distribution of maritime vessel users. To overcome the above challenges, this paper proposes a low Earth orbit satellite (LEO) MWCN based on rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS). Common data streams transmit broadcast-shared information to all vessel users. Private data streams provide differentiated supplements. The primary optimization objective is to maximize the sum rate. The transmission time is also introduced as a supplementary performance indicator to assess the system’s transmission capability. To overcome the problems of imperfect CSI and the low efficiency of the weighted minimum mean square error (WMMSE) algorithm, a block coordinate descent (BCD) algorithm is proposed based on the deep unfolding method (DU) and momentum-accelerated projection gradient descent (PGD). Numerical results show that DU-WMMSE reduces the number of convergence iterations from 8 to 4, improves the sum rate by 11.06%, and achieves lower transmission time. In addition, active RIS mitigates severe fading more effectively in complex channels. The proposed scheme also exhibits excellent scalability. Full article

Classification techniques, reliant on annotated data for autonomous decision training, have become pivotal tools in diverse domains. These techniques rely on models like Backpropagation Neural Networks (BPNNs). However, BPNNs frequently trap local optima, leading to suboptimal classification accuracy, and its convergence speed is relatively slow, which affects efficiency in complex and non-linear process data classification applications. Existing optimization algorithms struggle to balance global exploration and local exploitation when adjusting BPNNs. Addressing these limitations, this paper proposes a BP classifier based on an Elephant Herding Optimization with Multi-Learning strategy (MLEHO), termed MLEHO-BPC. The proposed MLEHO establishes a triple learning framework. Firstly, a collective learning stage incorporates two different adaptive operators into the original algorithm to strengthen global exploration. Subsequently, a group learning stage is designed, integrating exemplar, deskmate, and random learning methods to enhance convergence efficiency. Finally, a tutorship learning stage, guided by fitness value discrimination, empowers the algorithm to escape local optima. Benchmark function tests confirm MLEHO’s superiority in convergence speed and stability over comparative algorithms. Furthermore, MLEHO replaces traditional gradient descent, reformulating the BPNN’s update mechanism to optimize weights and thresholds. Validated on classification datasets and a Ti6Al4V process classification problem, MLEHO-BPC demonstrates exceptional classification accuracy and robustness against other algorithm classifiers. Full article

Systematic radiometric anomalies, manifesting as non-physical range-direction oscillations, significantly compromise the quality of Miniature Radio Frequency (Mini-RF) S-band SAR imagery and its scientific application in the lunar south polar region. In this study, we analyzed 1262 scenes from the Mini-RF archive in south polar regions. By employing a statistical screening method based on fitting the relationship of backscattering signal and off-nadir angle, 377 scenes (29.9%) were identified as radiometrically anomalous scenes with systematic errors. To correct these errors, a physics-based radiometric correction framework has been proposed by reconstructing the effective antenna gain pattern (AGP) of Mini-RF. Referenced relationship between the backscattering signal and the local incidence angle was established using normal scenes. For each anomalous scene, a simulation-driven gradient descent optimization approach is developed to estimate the offset of the AGP. Subsequently, the derived offset is applied to realign the AGP of the anomalous scene, effectively compensating for the systematic range-direction oscillations and restoring the true backscatter intensity. Using the proposed method, systematic errors in anomalous scenes have been eliminated effectively, reducing the Root Mean Square Error (RMSE) relative to the reference radiometric curve from 2.11 to 1.21 and decreasing the image entropy from 2.83 to 2.29. By eliminating systematic banding artifacts, the proposed method has significantly improved the radiometric fidelity of Mini-RF data. Furthermore, a temporal periodicity was found in the gain offsets, suggesting dynamic instrument distortion driven by variations in the orbital thermal environment. Full article

To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Within the framework of the traditional gradient descent MEA with variable step size, the proposed method introduces soft-thresholding-based sparse reconstruction to make moving targets more prominent and suppress background clutter in the image domain. A joint metric combining image entropy and the Hoyer sparsity measure is then constructed, and a three-point adaptive, variable step-size search is employed to reduce the number of evaluations of the cost function, thereby effectively mitigating clutter interference and significantly accelerating the optimization while maintaining good focusing quality. Simulation and real-data experiments demonstrate that, under complex phase errors and different SNR conditions, the proposed algorithm outperforms the conventional variable-step MEA in terms of image entropy, image sparsity, and runtime, while keeping the phase error estimation accuracy within a small range. These results indicate that the proposed method can achieve satisfactory moving-target focusing performance and exhibits promising engineering applicability. Full article

Backpropagation Neural Networks (BPNNs) are widely used in fault diagnosis and parameter prediction due to their simple structure and strong universal approximation capabilities. However, BPNNs suffer from slow convergence and susceptibility to poor local minima under basic gradient descent settings. To address these issues, this paper proposes a Balanced Grey Wolf Optimizer (BGWO) as an alternative to gradient descent for training BPNNs. This paper proposes a novel stochastic position update formula and a novel nonlinear convergence factor to balance the local exploitation and global exploration of the traditional Grey Wolf Optimizer. After exploration, the optimal convergence coefficient is determined. The test results on the six benchmark functions demonstrate that BGWO achieves better objective function values under fixed iteration settings. Based on BGWO, this paper constructs a training method for BPNN. Finally, three public datasets are used to test the BPNN trained with BGWO (BGWO-BPNN), the BPNN trained with Levenberg–Marquardt, and the traditional BPNN. The relative error and mean absolute percentage error of BPNNs’ prediction results are used for comparison. The Wilcoxon test is also performed. The test results show that, under the experimental settings of this paper, BGWO-BPNN achieves superior predictive performance. This demonstrates certain advantages of BGWO-BPNN. Full article

Eliminating poor scaling of variables of minimized functions is a pressing issue in solving high-dimensional minimization problems where it is impossible to use methods that change the metric of the space with full-scale metric matrices. In this paper, we propose an iterative method for scaling variables using a diagonal metric matrix and apply it to the gradient minimization method and the conjugate gradient method. In conjugate gradient methods, for quadratic functions, the descent directions are orthogonal to the previous gradient differences. In the proposed method, the transformation of diagonal metric matrices is based on the noted property. For the gradient method with a diagonal metric matrix, an estimate for the convergence rate on strongly convex functions with a Lipschitz gradient was obtained. A computational experiment was conducted, and the presented methods were compared with the Hestenes–Stiefel conjugate gradient method. On the given set of test functions, the gradient method with scaling is comparable in convergence rate to the Hestenes–Stiefel conjugate gradient method, while the conjugate gradient method with scaling matrices significantly outperforms the Hestenes–Stiefel conjugate gradient method. The obtained results confirm the acceleration properties of scaling methods in the case of poor scaling of the variables of the function being minimized. This allows us to conclude that the studied methods can be used alongside conjugate gradient methods to solve smooth, high-dimensional optimization problems with a high degree of conditionality. Full article

In pellet production, the uniformity of material distribution directly affects the subsequent roasting effect and the quality of finished products. Aiming at the problems of uneven distribution in traditional shuttle distribution systems, such as material stacking at both ends of the wide belt, insufficient parameter matching leading to uneven distribution, and reliance on manual adjustment which makes it difficult to adapt to dynamic working conditions, this paper proposes an intelligent control method based on Integral Simulation and Gradient Descent optimization (IS-GD). Firstly, this method combines the structure and operating parameters of the distribution equipment and accurately simulates the material distribution law on the wide belt during the reciprocating movement of the shuttle through integral technology. Based on the simulation results, longitudinal and lateral uniformity discriminant functions are constructed, and a phased gradient descent optimization strategy is adopted to dynamically adjust the shuttle belt speed, walking speed, and operating parameters of each stage with the goal of minimizing the uniformity index. Experimental results show that this method achieves a significant improvement in lateral distribution uniformity without affecting the stability of longitudinal distribution. This research provides reliable technical support for intelligent distribution control in pellet production and helps to improve the roasting quality and production efficiency of pellets. Full article

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments. Full article

Under the “Dual Carbon” strategy, high-penetration integration of distributed generators (DG) into distribution networks has triggered bidirectional power flow and reactive power-voltage violations. This phenomenon undermines the accuracy guarantee of conventional relaxation models (represented by second-order cone programming, SOCP), causing solutions to deviate from the AC power flow feasible region. Notably, ensuring solution feasibility becomes particularly crucial in engineering practice. To address this problem, this paper proposes a collaborative optimization framework integrating convex inner approximation (CIA) theory and a solution recovery algorithm. First, a system relaxation model is constructed using CIA, which strictly enforces ACPF constraints while preserving the computational efficiency of convex optimization. Second, aiming at the conservatism drawback introduced by the CIA method, an admissible region correction strategy based on Stochastic Gradient Descent is designed to narrow the dual gap of the solution. Furthermore, a multi-objective optimization framework is established, incorporating voltage security, operational economy, and renewable energy accommodation rate. Finally, simulations on the IEEE 33/69/118-bus systems demonstrate that the proposed method outperforms the traditional SOCP approach in the 24 h sequential optimization, reducing voltage deviation by 22.6%, power loss by 24.7%, and solution time by 45.4%. Compared with the CIA method, it improves the DG utilization rate by 30.5%. The proposed method exhibits superior generality compared to conventional approaches. Within the upper limit range of network penetration (approximately 60%), it addresses the issue of conservative power output of DG, thereby effectively promoting the utilization of renewable energy. Full article

For the first time, this paper introduces Nabla fractional calculus into the distributed Nash equilibrium (NE) seeking problem of aggregative games (AGs) with partial decision information in undirected communication networks, and proposes two novel fractional-order distributed algorithms. In the considered setting, each agent can access to only local information and collaboratively estimates the global aggregate through communication with its neighbors. Both algorithms adopt a backward-difference scheme followed by an implicit fractional-order gradient descent step. One updates local aggregate estimates via fractional-order dynamic tracking and the other uses fractional-order average dynamic consensus protocols. Under standard assumptions, convergence of both algorithms to the NE is rigorously proved using nabla fractional-order Lyapunov stability theory, achieving a Mittag-Leffler convergence rate. The feasibility of the developed schemes is verified via numerical experiments applied to a Nash-Cournot game and the coordination control of flexible robotic arms. Full article

In this paper, we present a rapid gradient descent method for solving low-rank matrix recovery problems. Our method extends the conventional gradient descent framework by exploiting the problem’s unique features to develop an innovative fast gradient computation technique that lowers the computational cost of gradient evaluation. The introduced adaptive step size selection strategy not only eliminates the need for the heavy calculations usually involved in finding the descent direction but also guarantees a consistent decrease in the objective function at every iteration. Additionally, we offer a proof confirming the algorithm’s convergence. Numerical experiments are provided to show the efficiency of the proposed algorithm. Full article

Establishing precise and reliable quadrotor dynamics model is crucial for safe and stable tracking control in obstacle environments. However, obtaining such models is challenging, as it requires precise inertia identification and accounting for complex aerodynamic effects, which handcrafted models struggle to do. To address this, this paper proposes a safety-critical control framework built on a Hamiltonian neural differential model (HDM). The HDM formulates the quadrotor dynamics under a Hamiltonian structure over the $\mathit{SE}\left(3\right)$ manifold, with explicitly optimizable inertia parameters and a neural network-approximated control input matrix. This yields a neural ordinary differential equation (ODE) that is solved numerically for state prediction, while all parameters are trained jointly from data via gradient descent. Unlike black-box models, the HDM incorporates physical priors—such as $\mathit{SE}\left(3\right)$ constraints and energy conservation—ensuring a physically plausible and interpretable dynamics representation. Furthermore, the HDM is reformulated into a control-affine form, enabling controller synthesis via control Lyapunov functions (CLFs) for stability and exponential control barrier functions (ECBFs) for rigorous safety guarantees. Simulations validate the framework’s effectiveness in achieving safe and stable tracking control. Full article