Search Results (442)

The analysis of transient regimes is important for the reliable monitoring and diagnostics of electromechanical systems, since dynamic changes in load have a significant impact on the electrical and mechanical characteristics of electric motors. In this study, an approach for classification of dynamic operating regimes is proposed using ten synthetically generated load profiles representing linear, nonlinear, periodic, stochastic, and combined loads. An experimental setup was developed for implementing controlled load effects and recording electrical and mechanical parameters of DC and induction electric motors. To determine the most informative characteristics, the ReliefF, SFCPP, and FSNCA methods were applied, followed by dimensionality reduction and classification using statistical and machine-learning approaches. The selected feature vectors were evaluated by stratified 5-fold cross-validation and validated using Wilcoxon, Friedman, and permutation tests. The results obtained show that a limited set of electrical and electromechanical parameters contains sufficient information to reliably distinguish between different dynamic operating modes. The best-performing model achieved classification accuracy above 98%, confirming the effectiveness of the proposed framework. The proposed approach provides a reproducible methodology for generating representative datasets, assessing the informativeness of the features, and supporting intelligent systems for monitoring the condition of electric drives. The methodology is applicable both in laboratory conditions and in the development of digital twins and predictive maintenance systems. Full article

The efficient use of existing road infrastructure has become increasingly important in densely developed urban areas where large-scale roadway expansion is constrained. Variable directional lanes (VDLs) and traffic signal control can reallocate roadway capacity and improve network performance. However, most existing studies optimize lane configurations and signal timing under a fixed route-flow distribution and therefore do not capture the feedback between control decisions and travelers’ route choices. To address this limitation, this study proposes a bi-level framework for coordinating VDLs and traffic signals at multiple intersections. The upper-level model determines the VDL functions and signal-control parameters to minimize total system travel time, while the lower-level static Logit-based stochastic user equilibrium model endogenously redistributes fixed origin–destination (OD) demand among candidate paths. Thus, OD demand remains fixed within each analysis period, whereas the route-flow distribution responds endogenously to the interaction between traffic control and aggregate route-choice responses. A hybrid solution procedure combining the Non-dominated Sorting Genetic Algorithm II and the Method of Successive Averages is used to solve the coupled control–assignment problem. Numerical experiments on a hypothetical network showed that incorporating route-choice feedback improved coordinated VDL–signal control under the tested conditions. In a supplementary comparison with the pre-optimization BPR-based reference scenario, the average route travel time decreased by 7.67–12.84% across the five representative demand periods, including reductions of 12.52% and 12.84% during the morning and evening peak periods, respectively. Microscopic simulation provided an additional numerical consistency check, with average discrepancies of 4.66% before optimization and 4.38% after optimization between the analytical and simulation results. These findings indicate that incorporating aggregate route-choice feedback can support sustainable urban traffic management by reducing travel time and congestion and improving the utilization of existing transportation infrastructure. However, further validation using real-world data and larger-scale networks is required, and environmental benefits should be evaluated explicitly using energy-consumption and emission indicators. Full article

This study provides a computational and experimental assessment of the parametric reliability of friction pairs in brake devices for lifting and transport technology. The computational and experimental method for assessing the parametric reliability of various friction units in lifting and transport equipment, based on operational and experimental research data with their permissible dynamic and thermal load of disc-drum and band-shoe brake devices, was developed to evaluate the influence of coupled dynamic and thermal loads on brake friction pair reliability. The operational parameters of the band-shoe brake, considered as a multi-pair friction system, were classified into four functional groups. The external operational parameters of their materials are determined, and the relationship between dynamic and thermal processes, phenomena and effects is established. A new quantitative indicator, the relative coefficient of parameter (RCP), was introduced to characterize the stochastic variability in friction pair performance and to assess parametric reliability. The meaning of parametric reliability for friction pairs is revealed with the subsequent classification of parameters that fit into the structural diagram of its computational and experimental assessment. Based on reliability conditions, the optimal thickness of the transverse or longitudinal section of the metal friction element has been determined, ensuring it does not exceed the permissible level of thermal stress. Experimental investigations of drilling rig winch brakes demonstrated that the RCP values ranged from 0.008 to 0.051 depending on operating conditions. The proposed probabilistic model enables the optimization of metal friction element dimensions while maintaining thermal stresses below critical levels. Full article

This paper proposes AutoRL, a high-fidelity Robot Operating System 2 (ROS2)–Gazebo simulation pipeline that addresses a critical reproducibility gap in learning-based flight control: existing reinforcement learning (RL) frameworks for unmanned aerial vehicles (UAVs) lack deterministic, step-level coupling between control actions and physics updates. AutoRL enforces a strict one-to-one correspondence between agent actions and physics updates via blocking ROS2 service calls, preserving the Markov property required for stable policy learning and enabling verifiable reproducibility independent of the learning algorithm. A composite reward function jointly optimizes attitude tracking accuracy, oscillation suppression, actuator smoothness, and disturbance robustness. Its modular, service-oriented architecture provides a reusable framework for offline-trained RL research. A proximal policy optimization (PPO) controller trained within AutoRL validates the framework, demonstrating consistent convergence and stable performance across multiple independently seeded runs. Determinism was experimentally verified across two regimes: with Gaussian IMU noise disabled, repeated rollouts produced bit-identical trajectories, while with noise enabled, the measured distribution of trajectory divergence agreed with a reference distribution drawn from the declared sensor model, together confirming that the blocking service call architecture eliminates all non-stochastic sources of nondeterminism between the agent and the physics engine. The trained model is exported in a lightweight form compatible with embedded flight control firmware and remains adaptable across airframe configurations by automatically recomputing the control allocation matrix from configuration files. The total policy network contains only $\mathrm{10,628}$ trainable parameters, and inference was measured on a Cortex-M7 microcontroller at $598.7$ µs per step, $15.0\%$ of the 4 ms control period. Full article

The Global Navigation Satellite System (GNSS) has been widely adopted in navigation applications due to its high accuracy and convenience. However, in urban canyon environments, severe signal blockage caused by buildings and trees introduces substantial non-line-of-sight errors and multipath effects, leading to degraded and highly fluctuating positioning performance. To address this issue, this paper proposes a residual-guided hybrid stochastic modeling framework. The method operates in two stages: first, a pseudo-range correction estimation network takes GNSS parameters containing residuals as input to estimate pseudo-range correction; second, these estimated corrections together with elevation angle and carrier-to-noise ratio are fed into a hybrid stochastic model parameter estimation network to determine model parameters. This design adjusts the pseudo-range observations to reduce the positioning loss without requiring true pseudo-range errors, which are difficult to obtain in real-world scenarios. Meanwhile, the explicit modeling of relationships among pseudo-range correction, elevation angle, and carrier-to-noise ratio renders the stochastic model parameters interpretable. Experiments on public urban GNSS datasets demonstrate that the proposed method achieves competitive positioning performance against both conventional and learning-based baselines. It delivers notable accuracy improvements in light urban canyon environments, particularly on the KLT2 sequence, while maintaining robust and competitive performance in the more challenging TST and Mong Kok scenarios. These results validate the effectiveness of jointly estimating pseudo-range corrections and adaptive observation weights for enhancing positioning accuracy and robustness across diverse urban environments. Full article

Earthquake sequences can affect slope stability before recovery from an earlier event is complete, yet reduced-order models commonly treat successive earthquakes as independent inputs or prescribe cumulative damage through event-based increments. We introduce a bounded, response-generated memory state into a delayed two-block landslide model. The state reduces incremental friction, evolves exclusively through computed dissipative response, and heals continuously between earthquakes. Its critical value is derived independently from the characteristic roots of the delayed mechanical subsystem. Paired aftershock-only and mainshock–aftershock experiments use corrected accelerograms from the 2011 Redcliffs sequence as recorded inputs rather than calibration data. Across 1255 admissible deterministic comparisons, a subcritical mainshock reduced the aftershock activation threshold in every parameter cell; 1235 threshold intervals were strictly separated, while 20 converged to the aftershock-only limit under strong healing. Threshold reduction was almost entirely determined by retained memory (Spearman rank coefficient ρS = 0.9997). Under stochastic forcing, all 33 primary parameter cells showed the same direction of reduction, and 32 paired 95% bootstrap confidence intervals excluded zero. Independent 4096-realisation ensembles yielded reductions of 18.0–26.2% under white-noise and Ornstein–Uhlenbeck forcing. Resetting memory reproduced the aftershock-only response exactly, whereas removing displacement delay eliminated delayed activation. These results identify a causal mechanism by which a non-activating earthquake can transiently lower the activation threshold of a subsequent event, while distinguishing dimensionless model activation from physical landslide failure. Full article

In this paper, we investigate a simulation-based optimization framework that implements discrete-event simulation with evolutionary search methods to optimize stochastic manufacturing systems efficiently. The proposed methodology couples a Tecnomatix Plant Simulation model with a MATLAB R2025b-based optimization environment using a data exchange interface, allowing for the iterative assessment of complex manufacturing systems. The study examines an adaptive replication strategy designed to manage stochastic variability in simulation outcomes. In the proposed method, the required number of simulation runs are determined dynamically based on confidence interval estimation. The stopping criterion is specified using a 95% confidence interval, ensuring adequate statistical accuracy while decreasing excess computational effort. The framework allows multiple performance indicators, such as throughput, congestion levels, and machine failures, which are built into an objective function. The optimization is driven by a (1, λ)-evolution strategy with Gaussian mutation and adaptive step-size control, allowing robust search in noisy objective function. However, thanks to the framework presented, it is also possible to apply other optimization algorithms. A case study of a manufacturing system was built and modeled in Tecnomatix Plant Simulation to validate the proposed methodology. In comparison with the baseline production configuration in one of the simulation runs, the suggested framework reduced the objective function by 43.36%. Benchmark experiments demonstrated that the adaptive replication strategy achieved a solution quality comparable to fixed replication schemes while requiring fewer simulation evaluations on average, thereby reducing the computational effort without compromising statistical reliability. The benchmark comparison showed that the adaptive replication strategy improved the objective value by up to 17.20% compared with fixed-replication strategies while requiring substantially less computational time than the fixed-20 and fixed-30 strategies. The robustness analysis further demonstrates that the adaptive replication strategy produces consistent optimization results across independent runs despite the stochastic nature of both the simulation model and the optimization process. From an industrial perspective, the proposed framework provides a practical decision-support tool for the optimization of manufacturing systems under uncertainty, enabling more reliable parameter tuning with reduced computational effort and facilitating the implementation of digital twin technologies. Full article

This paper proposes and analyzes a symmetry-based shared-pool architecture for service continuity in UAV-assisted wireless sensor networks with aerial base station placement. Unlike the conventional dedicated architecture, in which individual sensor clusters are structurally associated with specific serving UAVs and replacement units provide cluster-centered redundancy, the proposed architecture treats all available UAVs as a common pool of functionally interchangeable service units that can be dynamically assigned to active clusters. The novelty of the approach lies in formalizing this functional interchangeability as permutation symmetry of the underlying stochastic model. Under homogeneous UAV reliability parameters and homogeneous cluster-demand characteristics, relabeling UAVs or non-tagged clusters does not change the system dynamics. This invariance permits the identity-dependent microscopic state space to be reduced to occupation-number macrostates defined by the number of unavailable UAVs and simultaneously active service demands, while symmetric access in overloaded states determines the tagged-cluster service probability. On this basis, an analytical availability model is developed that combines UAV failure–recovery dynamics, limited recovery capacity, stochastic cluster activity, and demand-driven allocation from a shared aerial resource. The proposed architecture is compared with a dedicated-service baseline under both equal-fleet and target-availability criteria. The results show that shared pooling can convert permutation-based functional symmetry into a practical fleet-efficiency gain: under low or moderate demand overlap and adequate recovery capacity, the same service-continuity target can be achieved with fewer UAVs than in the dedicated architecture, whereas the advantage decreases under strong demand overlap or severe pool depletion. The study therefore establishes a direct link between permutation symmetry, stochastic state-space reduction, shared redundancy, and UAV fleet efficiency in service-oriented aerial sensor networks. Full article

This paper investigates how opinion dynamics with memory effects and random communication uncertainties achieve consensus in an inhomogeneous Hegselmann–Krause (H-K) model. The Caputo fractional derivative is introduced to describe the influence of historical opinions, while multiplicative noise captures random disturbances in communication. We establish sufficient conditions under which all followers almost surely converge to the leader’s opinion. Moreover, we characterize the algebraic (power-law) convergence behavior determined by the fractional order. The analysis is based on fractional Lyapunov techniques and fractional Grönwall estimates, which enable the treatment of the nonlinear stochastic system with leadership. The results reveal how memory effects, leadership strength and stochastic perturbations jointly influence consensus formation. Numerical simulations are provided to verify the theoretical results and illustrate the effects of the model parameters on the convergence dynamics. Full article

Air-to-underwater extremely low frequency (ELF) signal recovery requires a receiver that accounts for both cross-medium attenuation and impulsive interference. Seawater weakens the desired electromagnetic waveform while altering the tail behavior that remains observable within the receiver bandwidth. Conventional recursive robust filters normally choose their linearity parameters and output scale from empirical or data-driven rules and therefore do not explicitly incorporate this depth-dependent physical–statistical coupling. This work formulates the propagation-guided scaled recursive weighted myriad (PG-SRWMy) filter as a mathematically structured framework that links a propagation-evolving stochastic process model with robust nonlinear recursive estimation for reference-normalized underwater ELF recovery. The propagation state determines a depth-evolving effective stable-like description through a characteristic exponent and a dispersion parameter, and these stochastic descriptors are transformed into branch-specific initial linearity parameters of the recursive myriad estimator. The same propagation model yields a positive and bounded normalization transform for the reconstruction scale, while first-order sensitivity relations characterize the local effect of environmental-state uncertainty. The original SRWMy sample-wise recursion is retained for data-driven adaptation. Numerical experiments show that PG-SRWMy accelerates bilinear-parameter stabilization, lowers waveform-reconstruction error, and improves end-to-end reference-normalized signal-to-noise ratio (SNR) gain across changes in receiver depth and surface-side impulsiveness. The results support propagation-aware initialization as a mathematically structured and statistically interpretable route to depth-adaptive underwater ELF reception. Full article

Suppose that bids, or offers, to purchase an asset for sale occur according to a non-homogeneous Poisson process. Bid amounts, measured in percentages, are assumed to be independent and identically distributed random variables. Faced with the pressure of having to sell the asset by a certain time period (or else risk getting nothing at all for it), this article analyzes the probability distribution of the obtained return on the asset under a specific type of selling strategy, advancing the initial results found in Karlin’s seminal paper from 1962. In particular, the main result derived is an explicit expression for the raw moments of the return on the asset. Such moments are widely used in probability theory and stochastic modeling, since, in most instances, having all the raw moments fully determines the underlying probability distribution. The special case when bid amounts have a beta distribution with integer shape parameters is examined in depth. Numerical results are presented and discussed for a variety of different selling strategies, time-varying offer rates, and bid amount distributions. Full article

Cooperative jamming task allocation for UAV swarms must jointly consider radar priority, the effectiveness of different jamming modes, and heterogeneous resource limits. This paper establishes a radar threat assessment model from radar operating parameters and evaluates each candidate jamming assignment in the time, frequency, and power domains. Based on these evaluations, a binary integer programming model is formulated to jointly determine radar selection, UAV assignment, and jamming mode under coverage, capacity, and mode availability constraints. To solve the model, an improved multi-population genetic algorithm (IMPGA) is developed using crossover on radar task blocks, mutation at the task level, stochastic feasibility repair, and cooperative evolution among multiple subpopulations. Experimental comparisons are conducted under identical function evaluation budgets, and each representative scenario is evaluated through 100 independent Monte Carlo runs. The results show that the IMPGA reliably obtains exact or near-optimal solutions and provides improved solution quality and consistency, particularly as the problem scale and resource coupling increase. The scalability experiments further demonstrate that the algorithm maintains small optimality gaps in larger instances. Ablation results confirm that the radar task operators and stochastic repair make important contributions to the final solution quality and convergence process. Full article

Accurate modeling and partitioning of forest evapotranspiration (ET) are essential for understanding water cycle processes in forest ecosystems. This study develops an improved three-source ET model by integrating a physically based canopy interception evaporation (Ei) scheme into the Shuttleworth–Wallace–Hu (SWH) model. A Monte Carlo stochastic parameterization scheme was applied to optimize model parameters. The proposed framework disaggregates the total ET flux into three distinct components: vegetation transpiration, soil evaporation, and Ei, thereby reducing uncertainties associated with the original SWH model in humid forest regions. The new model’s performance was assessed using flux observations from two subtropical forest sites and compared to the SWH model. The verification results indicate that the three-source model provided reliable estimates of daily ET. At the QYZ station (2004–2007) and the DHS station (2005–2007), the fitting slopes for simulating daily ET were 0.97 and 1.01, respectively, with corresponding coefficients of determination of 0.92 and 0.81. The root mean square errors (RMSE) for the three-source model were 0.38 mm day⁻¹ and 0.52 mm day⁻¹, respectively, with a reduction of 4.33% and 3.10% in RMSE compared to the SWH model. Additionally, the new model simulated the annual T/ET ratio more accurately, with values closer to site-measured data than the SWH model’s estimates. At both sites, the T/ET ratios simulated by the new model were closer to the observed values than those simulated by the SWH model, indicating an improved representation of ecohydrological processes. Furthermore, environmental analysis revealed that vapor pressure deficit and precipitation primarily govern the T/ET ratio, exerting the strongest positive and negative effects, respectively. Importantly, it requires only one additional precipitation parameter compared to the SWH model, yet achieves higher simulation accuracy and a more realistic representation of hydrological processes. Overall, the three-source model provides an improved framework for estimating ET in humid forest ecosystems. Ultimately, these results offer deeper insights into the coupled water and energy fluxes within forest ecosystems, thereby facilitating more effective water management and guiding sustainable forestry under a shifting climate. Full article

We develop an operator-theoretic framework for extreme events in reflexive financial systems, identifying inverse evolution—the deterministic contraction of the manifold of admissible futures—as the structural mechanism underlying crashes and melt-ups. The interpolation constraint, which forces all analytical continuations to match the terminal empirical price, resolves the apparent continuum of stochastic paths into a discrete, countable spectrum of metastable futures. This countable manifold is stabilized by a spectral regularizer that preserves dimensionality through a “wait-and-adjust” re-categorization logic. Within this unified structure, we distinguish three pathways to collapse: (i) the Black Swan, a crisis of spectral weight; (ii) the projection operator, a rank-reducing projection that restores symmetry by exclusion; and (iii) the reactivation operator, a breakdown of spectral truncation that reactivates suppressed behaviour with large emergent return (Heavy) modes and forces the system into a regime of manifold resumption. Central to all modalities is the emergent return, an effective mass parameter whose sign determines whether collapse manifests as reflexive contraction (crash) or reflexive amplification (melt-up). The resulting dynamics exhibit cross-domain universality. The same operator grammar governs geopolitical choke-points, institutional purges, technological monopolies, retail-driven short squeezes, and other macrosystems in which dimensionality is either forcibly reduced or abruptly restored. Full article

This study aims to clarify the mesoscopic damage evolution mechanisms of concrete subjected to rigid projectile penetration and provide support for the optimal design of high-performance protective structures. Based on the ABAQUS/Explicit finite element framework, a three-phase mesoscopic numerical model of concrete considering aggregate, mortar matrix, and interfacial transition zone (ITZ) is constructed. By combining the random convex polygon algorithm with the background mesh mapping technique, the intrinsic geometric features of stochastic materials such as crushed stone and pebble are accurately characterized. The effects of aggregate geometric characteristics, volume fraction, and projectile motion/geometry parameters (velocity, length–diameter ratio, curvature radius of the warhead CRH) on the damage evolution of the target, penetration depth, and velocity attenuation law are systematically investigated. The results reveal that increased aggregate angularity substantially enlarges both tensile and compressive damage zones and promotes crack bifurcation, which collectively enhances kinetic energy dissipation, reduces penetration depth, and accelerates projectile deceleration. Increasing the aggregate volume fraction can significantly enhance the anti-penetration resistance of the target. A high proportion of aggregate grains effectively enhances the structural toughness by blocking the crack propagation path. Penetration velocity, length–diameter ratio, and CRH are the core elements determining the penetration efficiency, and the increase in their values will lead to a significant increase in penetration depth and induce a change in the damage mode from local failure to large-scale cracking. The mesoscopic model and related conclusions established in this study can provide a theoretical foundation and numerical benchmark for the impact resistance design, optimization, and damage assessment of high-strength concrete protective structures. Full article