Search Results (450)

We suggest Josephson interferometry as a quantitative probe of spin–orbit-driven phenomena in superconducting heterostructures. Two distinct mechanisms are analyzed: (i) intrinsic helical superconductivity, producing asymmetric Fraunhofer patterns with lobe deformations and field-reversal asymmetry, and (ii) emergent interfacial magnetism in ferromagnet–superconductor hybrids, where Rashba spin–orbit coupling generates spontaneous fields that rigidly shift the interference fringes. The predicted signatures—flux-shifted interference minima, anisotropic critical current suppression, and angle-dependent pattern distortions—provide direct experimental access to finite-momentum pairing and interface-localized fields via standard Josephson current measurements. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

Path planning for inspection and maintenance robots in nuclear power plants often suffers from limited adaptability, high computational cost, and unstable convergence in obstacle-dense confined environments. To address these issues, this paper proposes an improved Bi-RRT–APF path optimization framework for complex industrial scenarios. The method integrates (1) a hybrid sampling strategy combining random, goal-biased, and potential-field-guided sampling to enhance global exploration and convergence efficiency; (2) a potential-field-guided perturbation and stagnation detection mechanism to improve escape capability from local minima; and (3) a dynamic target switching and constrained segmented connection strategy to improve path feasibility and safety. A digital twin-based simulation platform is further developed to validate the engineering applicability of the proposed approach. Simulation results demonstrate significant quantitative improvements over baseline methods. Compared with conventional RRT and Bi-RRT, the proposed method reduces iteration count by 65.3% and 43.8%, respectively, and decreases computation time by 76.1% and 48.4%, respectively, while increasing the success rate to 95% (from 82% and 93%) and improving path smoothness (reduced from 5.3 and 3.3 to 2.9). Compared with advanced variants (Quad-RRT and KB-RRT*), the method further reduces computation time by 25.2% and 10.3% and iteration count by 29.3% and 8.4%, respectively. These results indicate that the proposed method achieves a balanced improvement in efficiency, robustness, and path quality. This work provides an efficient and reliable solution for autonomous path planning of robots in complex nuclear power plant environments. Full article

This paper introduces the Quantum-Inspired Impulsive Continuous Hopfield Network (Q-ICHN), a novel hybrid control framework designed to handle non-smooth, high-energy perturbations in nonlinear dynamical systems. Standard Continuous Hopfield Networks (CHNs) rely on sigmoidal activation functions that are prone to gradient saturation, which leads to an insufficient corrective response when the system undergoes large deviations from equilibrium. To overcome this shortcoming, the proposed Q-ICHN adopts a wave-packet-based activation function grounded in the stationary Schrödinger equation, yielding a non-monotonic and oscillatory activation profile that sustains effective compensatory dynamics across a broad range of states. Furthermore, the proposed framework incorporates Madelung’s quantum potential into the control architecture, thereby enabling a fundamental reshaping of the system’s energy landscape. Specifically, this induces a tunneling-like mechanism that allows the system to circumvent local minima and rapidly recover from impulsive disturbances, manifested as a sharpened attractor structure in the phase-space domain. Together, these properties yield enhanced convergence behavior and improved robustness over traditional neural control approaches. To rigorously assess its merits, the performance of the Q-ICHN is evaluated through a large-scale benchmark involving 20 established control methods, including Sliding Mode Control (SMC), Model Predictive Control (MPC), and Backstepping. The experimental results obtained across 20 heterogeneous scenarios demonstrate that the proposed model achieves a 48% reduction in Mean Squared Error (MSE) relative to the classical ICHN. In addition, the Q-ICHN exhibits improved smoothness, reflected in a 30% reduction in jerk with respect to high-gain robust controllers, and enhanced reliability, validated by superior spectral purity and a 34% reduction in integrated variance under stochastic perturbations. Collectively, these results underscore the potential of quantum-inspired activation mechanisms to favorably balance control responsiveness and harmonic stability, providing a robust framework for handling both continuous dynamics and impulsive effects. Full article

Ocean surface currents (OSCs) are central to upper ocean dynamics and air–sea exchange, yet their retrieval from spaceborne synthetic aperture radar (SAR) is limited by wave-induced bias (WB). WB arises from the inherent motion of the scattering facets and from long-wave hydrodynamic and tilt modulations, and is therefore jointly controlled by sea state and radar viewing geometry. This study develops an observation geometry optimization framework. Five years of hourly ERA5 wind and wave reanalysis data over the Kuroshio are used as a representative ensemble of sea states to drive the KaDOP model, and an exhaustive grid search over line-of-sight (LOS) azimuth (0–360°) and incidence angle (20–60°) is performed to identify, for each location and season, the viewing geometry that minimizes the time-mean WB. These local optima are then summarized as mission-level metrics, including the minimum achievable WB, the coverage meeting prescribed WB thresholds, and the spatial coherence of the preferred LOS azimuth and incidence angle. Finally, the theoretical minima are compared with the fixed left-looking geometry of the Luojia-2 (LJ-2) satellite along a 213 km × 6 km observation corridor and with Gaofen-3 (GF-3) viewing geometries at four representative locations in the Kuroshio. Across these validation cases, the optimized geometry reduces mean absolute WB by about 20–60% for LJ-2 and 20–80% for GF-3, providing quantitative constraints for future SAR mission design targeting OSCs. Full article

This paper presents a closed-form tracking and collision avoidance framework for multi-agent differential drive robots. Existing reactive methods often rely on purely geometric proximity, leading to conservative detours and local minima. A state-dependent adaptive avoidance strategy is developed to dynamically modulate repulsive forces using the time-derivative of fractional barrier risk functions, alleviating unnecessary evasive maneuvers. Within a convergence vector field (CVF) architecture, an active safety-corrected tracking mechanism orthogonally strips hazardous velocity projections from the spatial error. This mitigates the inherent conflict between target tracking and obstacle repulsion. A matrix projection-based Lyapunov approach demonstrates the finite-time convergence of the vehicle orientation, bounded tracking errors, and collision-free properties of the closed-loop system, with effectiveness further validated through simulations. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

Real-time motion planning in cluttered and dynamically evolving environments remains challenging due to the need to ensure rapid convergence, collision avoidance, computational efficiency, and robustness against local minima under frequent changes. Although sampling-based planners such as RRTX* and ABIT* provide strong theoretical guarantees, their practical deployment in dense dynamic scenarios is often limited by high sampling overhead and computational latency. This paper proposes a Fast Converging Adaptive Algorithm (FCAA), a deterministic sampling-based framework integrating adaptive sampling density, temperature-controlled exploration, and dynamic step-size regulation within a unified heating and annealing mechanism. The temperature parameter governs both the spatial sampling band and incremental expansion radius, enabling controlled transitions between goal-directed expansion and stochastic exploration when stagnation occurs. The algorithm is evaluated using a two-stage protocol comprising intrinsic validation and benchmarking. Across 36 environments with obstacle densities ranging from 3% to 20% and velocities between −30 and +30 m/s, FCAA achieved a 100% success rate within the defined experimental design while maintaining path quality comparable to or better than RRTX* and ABIT*. Unlike the reference planners, which typically required tens of thousands of samples and seconds of computation, FCAA operated with substantially reduced sampling effort, typically tens of nodes, and planning times from 0.1 to 320 ms depending on scenario complexity. Within the simulation framework, the results indicate that the proposed temperature-regulated strategy enables fast and computationally efficient motion planning under dynamic constraints, making FCAA suitable for time-critical robotic navigation scenarios. Full article

Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architecture integrating deep representation learning with an improved Vortex Artificial Potential Field (APF). At the decision layer, a Convolutional Neural Network (CNN) encodes the environment as a fixed-dimensional tensor and generates global waypoints with constant-time inference, independent of obstacle count. At the control layer, a Vortex APF resolves the Goal Non-Reachable with Obstacles Nearby (GNRON) problem and limit-cycle oscillations through tangential rotational potentials, achieving significant improvement in trajectory smoothness compared to traditional APF methods. A closed-loop replanning mechanism further ensures robust performance under execution drift. Experiments across varying obstacle densities demonstrate that the combined system achieves high navigation success rates in dense environments with substantially reduced computation time compared to sampling-based planners such as Rapidly exploring Random Tree star (RRT*), while maintaining superior trajectory quality. This architecture provides a computationally efficient solution for resource-constrained UAV platforms operating in GPS-denied or obstacle-rich environments such as warehouses, forests, and disaster sites. Full article

The increasing penetration of renewable energy sources in power systems has intensified the need for accurate modelling of generation units under transient conditions. Despite the widespread adoption of the IEC 61400-27 generic wind turbine models, their parametrization remains a critical challenge. Classical optimization-based approaches are time-consuming, prone to convergence to local minima in the high-dimensional non-convex parameter space and require substantial expert knowledge. To address this gap, this paper proposes a machine learning- and deep learning-based methodology for estimating the key mechanical parameters of Type III wind turbines. A synthetic database of 10,000 active power responses was generated using DIgSILENT PowerFactory via its Python Application Programming Interface, covering a wide range of voltage dip conditions and mechanical parameter combinations. A comparative analysis of eight machine learning and deep learning algorithms for this task is performed. Validation is performed on both the synthetic dataset and two real manufacturer-validated wind turbine models. The results demonstrate that the proposed methodology enables fast and accurate identification of the mechanical parameters of wind turbines, maintaining reliable estimation performance even in the presence of measurement noise, thereby supporting its applicability in power system stability studies. Full article

Proton exchange membrane fuel cells (PEMFCs) represent a promising renewable energy technology that converts chemical energy from hydrogen and oxygen into electrical energy. Accurate mathematical modeling and precise parameter identification are essential for optimizing PEMFC performance and control. This study proposes a novel hybrid meta-heuristic algorithm, the mutated puma optimizer (Mu-PO), which integrates a mutation operator from differential evolution to enhance the exploration and exploitation capabilities of the conventional puma optimizer, enabling it to escape local minima and reach global optima in fewer iterations. A sum of squared error (SSE)-based objective function is formulated to minimize the discrepancy between estimated and experimental voltages. The proposed method identifies seven unknown parameters for three commercial PEMFC models (250 W, SR-12, and NedStack PS6), achieving SSE values of 0.6419, 1.0566, and 2.0791, respectively. Notably, Mu-PO attains these low SSE values in fewer than 50 iterations for all models, demonstrating rapid convergence. Comparative analysis using statistical indicators (minimum, mean, maximum, and standard deviation of SSE) confirms that Mu-PO outperforms well-established optimization algorithms in terms of convergence speed, stability, and accuracy. Furthermore, validation under dynamic operating conditions, including variations in pressure and temperature, demonstrates consistent and reliable parameter identification, highlighting the robustness and practical applicability of the proposed approach for PEMFC modeling and optimization. Full article

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance. Full article