Search Results (2,722)

This paper addresses active-mode detection for switched discrete-time Takagi–Sugeno systems from noisy input–output data under candidate-dependent input correction and uncertainty in data-driven observability subspaces. A lifted input–output formulation is developed in which each candidate mode is associated with a mode-dependent forced-response correction and a nominal observability subspace identified offline from representative data. Based on this construction, a practical residual criterion is introduced together with an ideal residual criterion defined by the exact residual projector. An online verifiable sufficient condition is then derived to guarantee consistency between the practical and ideal residual orderings, yielding a conservative but theorem-consistent certification mechanism. To quantify the effect of measurement uncertainty, a component-wise noise-to-signal ratio (NSR) analysis is established, leading to explicit conservative NSR bounds when signal-floor conditions are available offline. These results motivate an adaptive observation-window strategy driven by an explicit online NSR estimate. In addition, an uncertainty-corrected discernibility index based on principal angles between estimated observability subspaces is introduced to assess offline mode separability. Simulations on a switched T–S benchmark show high practical detection accuracy, sound but conservative certification, informative NSR bounds, and stable adaptive-window regulation, including under reviewer-motivated switching stress tests and baseline comparison experiments. Full article

Long-distance belt conveyors exhibit significant nonlinear dynamic characteristics due to factors such as the viscoelasticity of the conveyor belt, startup curves, and material loading, which lead to substantial variations in component loads and belt tension. This complexity poses challenges for dynamic analysis and the study of dynamic properties. Based on the Kelvin–Voigt viscoelastic constitutive relation, this paper establishes a discrete model of the conveyor belt and further develops a nonlinear dynamic model for long-distance belt conveyors. The model is numerically solved using the fourth-order Runge–Kutta method. On this basis, the influence of key parameters—such as integration step size, startup curve, operating time, and belt speed—on the dynamic behavior of the belt conveyor is investigated. The results indicate that increasing the counterweight mass effectively suppresses oscillation in the tensioning device and enhances system stability. Prolonging the startup duration and optimizing belt speed also mitigate load impacts. Compared with conventional methods, a composite transitional startup strategy is proposed, which significantly reduces transient tension peaks in the conveyor belt. This study provides a theoretical basis for optimizing control strategies and structural design of long-distance belt conveyors, thereby improving operational safety and reliability. Full article

This paper investigates a discrete-time fractional-order predator–prey model incorporating a double Allee effect in the prey population, derived from a fractional differential system via the piecewise constant argument method to capture both memory effects and density-dependent constraints. We establish the existence and local stability of all biologically meaningful equilibria and show that the interaction between fractional memory and the double Allee threshold significantly influences the stability of the coexistence state. Through the integration of linear stability analysis and center manifold reduction, we are able to obtain explicit conditions for Neimark–Sacker and period-doubling bifurcations. The system exhibits rich dynamics, including periodic oscillations, quasi-periodicity, and chaos. The double Allee effect plays a key role in shaping system stability. To suppress instability and chaotic behavior, feedback and hybrid control strategies are applied and shown to be effective. Numerical simulations are given to confirm the results obtained by the theoretical analysis and to show the transitions among different dynamical states, in which the fractional-order memory and multiple Allee effects play important roles. Full article

Reliable detection of internal defects in pressure vessel structures remains essential for structural safety and condition-based maintenance. This study presents a low-complexity structural health monitoring framework based on fiber Bragg grating (FBG) sensing and multiresolution wavelet analysis for void detection in curved pressure vessel structures under guided wave excitation. Guided waves are introduced using piezoelectric actuators, while the FBG sensors capture the resulting strain-induced wavelength variations. Due to the limited bandwidth of the optical interrogator, the recorded signals represent the strain envelope response associated with guided wave interaction rather than the resolved ultrasonic carrier waveform. To characterize defect-induced changes, the acquired signals are analyzed using continuous wavelet transform (CWT) for time–frequency interpretation, and discrete wavelet transform (DWT) and wavelet packet transform (WPT) for energy-based multiresolution feature extraction. Experimental results show that void defects lead to consistent redistribution of wavelet-domain energy and increased non-stationarity in the measured strain responses. These trends are further supported by finite-element simulations, which reproduce similar energy redistribution patterns between intact and damaged cases. The proposed framework provides a physically interpretable and computationally efficient approach for defect detection using low-bandwidth FBG sensing, without reliance on high-speed acquisition or data-intensive learning models. The results demonstrate the feasibility of using energy-based multiresolution analysis of FBG strain signals for practical and scalable structural health monitoring of pressure vessel systems. Full article

Intermodal container terminals play a critical role in modern logistics systems, where operational efficiency and energy consumption are strongly influenced by container stacking strategies. Inefficient yard organization leads to increased reshuffling operations, which negatively affect handling time and resource utilization. Despite extensive research, the relationship between operational performance and energy consumption remains insufficiently explored under dynamic terminal conditions. This study applies a discrete-event simulation framework to evaluate the impact of alternative container stacking strategies on both operational efficiency and energy consumption. The model represents container arrivals, storage decisions, retrieval processes, and reshuffling operations over a multi-day simulation horizon. Three stacking strategies—FIFO, balanced distribution, and departure-time clustering—are analysed under identical and dynamically evolving conditions using performance indicators related to reshuffling intensity, handling efficiency, and energy consumption. The results show that stacking strategies significantly affect terminal performance, but their effectiveness depends on the structure of container flows. While FIFO achieves the lowest reshuffling intensity and energy consumption under high-load conditions, departure-time clustering improves performance in outbound-dominated scenarios. The findings also reveal a structural discrepancy between operational and energy-related performance, as non-productive movements account for a higher share of operations than of total energy consumption. The study demonstrates that container stacking should be treated as a multi-criteria decision problem, where minimizing reshuffles does not directly correspond to minimizing energy consumption. The proposed simulation-based framework provides a consistent environment for evaluating trade-offs between operational and energy-related performance under controlled dynamic conditions. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

Unmanned aerial vehicle (UAV) imaging systems are increasingly deployed in surveillance, infrastructure monitoring, and smart-city applications, where the integrity of captured visual data is critical. Recent advances in generative models enable highly realistic deepfake manipulations that can compromise aerial sensor streams, particularly under real-world degradations such as motion blur, sensor noise, and compression artifacts. This paper introduces DeepLayer-ID, a degradation-aware multi-domain forensic framework specifically designed for UAV sensing environments. The proposed architecture decomposes forensic evidence into complementary spatial, frequency, and residual domains. A discrete wavelet transform module captures sub-band energy inconsistencies, while high-pass residual filtering isolates sensor pattern anomalies. A lightweight transformer-based fusion mechanism adaptively integrates cross-domain representations to enhance robustness under heterogeneous acquisition conditions. To emulate operational UAV pipelines, we construct a balanced dataset of 1096 aerial frames derived from the VisDrone2019-DET validation subset, incorporating synthetic manipulations and physics-consistent degradations. The experimental results show that DeepLayer-ID achieves 97.8% accuracy and 0.991 AUC, outperforming ResNet-50 (90.9%, 0.942 AUC), XceptionNet (92.4%, 0.957 AUC), and Noiseprint CNN (93.1%, 0.964 AUC). Notably, the model maintains real-time feasibility, with only 5.4 M parameters and 9.8 ms inference latency. These findings demonstrate that structured multi-domain signal decomposition combined with attention-guided fusion provides a robust and computationally efficient solution for deepfake detection in degraded UAV sensing systems. Full article

A coordinated optimization method for emergency repair scheduling and system operation restoration is proposed to address large-scale transmission line outages caused by extreme weather events such as ice and snow disasters. First, an active outage scenario for transmission lines is constructed based on the Jones icing thickness model and an exponential failure probability model, while incorporating the spatial distribution characteristics of ice disasters. Subsequently, a bi-level optimization model with repair resource constraints is developed. The upper-level model determines the transmission line repair schedule with the objective of minimizing the total repair time while taking system power supply restoration efficiency into account. Based on the completion times of line repairs, the lower-level model optimizes the system restoration process by considering power flow constraints, generator start-up processes, and load restoration characteristics. To address the challenges posed by discrete operational states and strongly coupled bi-level constraints that are difficult to solve using conventional approaches, a logic-based Integer L-shaped coordinated solution method is proposed. Finally, the effectiveness of the proposed method is validated through case studies based on the IEEE New England 10-unit 39-bus system. The results demonstrate that the proposed method can significantly improve system load restoration levels while maintaining high repair efficiency. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

In deep geothermal engineering, concrete slabs are prone to thermal cracking. The aggregates and pores are the core influencing factors for this failure behavior. However, existing research methods are unable to accurately capture the microscopic evolution process of thermal cracking and cannot clarify the intrinsic mechanism of how the characteristics of aggregates and pores affect the initiation and propagation of cracks. This limitation restricts the in-depth understanding of the laws of concrete thermal cracking. To address this deficiency, this study employs the discrete element method (DEM) and combines the particle flow program PFC2D to construct a microscopic model of concrete disks. By setting reasonable temperature parameters and thermal load boundaries, a numerical simulation system matching the actual deep geothermal high-temperature environment is established. Three sets of quantitative variables were designed: aggregate particle size (0.003, 0.004, 0.005, 0.006), aggregate volume fraction (0.35, 0.40, 0.45, 0.50), and porosity (0.11, 0.12, 0.13, 0.14). Through controlled variable simulations, the influence laws of each variable on the formation, propagation path, and time evolution of concrete thermal cracks were explored. The quantitative research results show that an increase in aggregate particle size significantly accelerates the generation and propagation of cracks. When the particle size is 0.006, the number of cracks is the highest and the propagation rate is the fastest. The aggregate volume fraction is negatively correlated with the final number of cracks, and 0.50 is the optimal fraction, at which the number of cracks is the smallest. A decrease in the fraction will lead to intensified stress concentration in the cement paste and a sudden increase in the number of cracks. An increase in porosity significantly disrupts the material continuity. When the porosity is 0.14, the bifurcation and connection of cracks are the most significant, while a low porosity of 0.11 can effectively inhibit the overall development process of thermal cracks. In addition, compared with traditional experimental methods and continuous medium numerical simulation techniques, the discrete element method has unique advantages in revealing the internal mechanism of concrete thermal cracking at the microscopic level. It can achieve real-time tracking of the evolution of discrete micro-cracks and the internal stress distribution characteristics. This study enriches the microscopic theoretical system of concrete thermal cracking and provides reliable quantitative references and technical support for the design of thermal crack resistance of concrete in deep geothermal engineering and the optimization of material composition. Full article

This study presents the development and implementation of a web-based geospatial platform for the quantitative assessment of land use and land cover change (LULCC) based on multispectral satellite images. The system operationalizes the Rao spectral diversity metric (Rao’s Q) to detect and quantify LULCC resulting from different environmental agents. The platform supports single-band (classic mode) or multi-band (multidimensional mode) processing. Its main functionalities include the interactive de-limitation of areas of interest (AOI) and calendar-based temporal selection, allowing analyses to be performed at discrete time points or at defined intervals. Among the tools available in the application are the automated calculation of Rao’s Q surfaces and maps of change between pairs of dates. Additionally, the platform allows the selection of several spectral indices, with the aim of supporting ecosystem monitoring and the characterization of the Earth’s surface. In the use case demonstration (Reykjanes Peninsula volcanic eruption of February 2024), the Rao’s Q method applied to Sentinel-2 SWIR imagery demonstrated strong performance in lava flow detection, with the multidimensional approach (bands 11 + 12) achieving the most balanced results (OA = 83.0%, PA = 84.0%, UA = 82.4%), while band 11 alone yielded the highest precision (UA = 97.4%). By integrating spatiotemporal analysis, spectral diversity metrics, and spectral indices into an accessible and extensible framework, the platform constitutes a robust tool for monitoring LULCC and assessing environmental impacts. Full article

In the aerospace industry, thin-web gears are preferred for achieving high power-density transmission. However, thin-webbed structures always lead to out-of-plane resonance during the transmission process, which commonly happens in helical gears, manifesting as severe vibration at a specific rotational speed. To address this, a shaft–web–ring dynamic model is proposed. The shaft, gear web, and gear ring are modelled based on the Timoshenko straight beam, Mindlin plate, and Timoshenko bent beam theory. Simultaneously, the potential energy caused by the time-varying meshing stiffness is coupled to the gear ring. The kinetic and potential energies of each discretized finite element of the components are derived based on elastic deformation theory, and the governing equations of each element are obtained using Hamilton’s principle. The model is verified through a modal experiment. The comparison with traditional rotor-gear models has demonstrated the significance of gear body flexibility in helical gears with thin webs. The effects of the web thickness and helix angle on dynamic response are studied, revealing that gear web elasticity and an appropriately high helix angle can effectively reduce vibrations at the support bearing, prevent excessive vibrations, and contribute to vibration and noise reduction in the transmission system. Full article

The multi-finger robotic hand offers great potential for precise control due to its high degrees of freedom. Yet, manipulating objects forms a closed-chain kinematic system, which compounds the dimensionality and computational complexity of trajectory tracking. To tackle this challenge, and inspired by the widespread application of the zeroing neurodynamics (ZND) in robotic control, this study proposes a novel direct-discrete robust neurodynamics (DDRN) algorithm. The proposed algorithm advances the ZND methodology by employing a direct discretization design strategy. This strategy is crucial for two reasons. First, it fits naturally with the discrete-time nature of digital systems, enabling practical implementation. Second, it enhances precision by avoiding the integration errors inherent in continuous-to-discrete transformations. By simultaneously integrating this direct discretization with explicit noise suppression mechanisms, the DDRN algorithm efficiently solves the high-dimensional tracking problem formulated as a constrained time-varying quadratic programming (CTVQP) problem. Theoretical analyses demonstrate that under various noise environments, the steady-state residuals (SSRs) achieve global convergence, guaranteeing the algorithm’s strong robustness and high accuracy. Furthermore, comprehensive numerical simulations substantiate its superior performance. Practically, this DDRN algorithm enables more reliable and precise real-time control of dexterous robotic hands, with potential benefits for advanced manufacturing, prosthetic hands, and automated assembly where accurate trajectory tracking under sensor noise is critical. Full article

In HPC systems and hyper-scale data centers, the adoption of high-performance NVMe SSDs and high-speed networks has shifted storage bottlenecks to the network stack. Under high-concurrency workloads, frequent interrupt processing exhausts CPU resources while protocol-level control–data dependencies in the NVMe over TCP write path introduce additional serialization penalties. Existing optimizations either require specialized hardware, dedicate CPU cores to user-space polling, or apply semantically blind batching that delays time-sensitive control messages. We present SENS, a Semantic-aware NVMe over TCP Scheduler embedded within the NVMe over TCP driver of the Linux kernel. SENS combines two mechanisms: (1) PDU vectorization, which aggregates discrete Protocol Data Units into memory vectors before network transmission, amortizing per-I/O system call overhead and reducing soft-interrupt frequency; and (2) instruction-aware dispatch, which detects control PDUs such as R2T and triggers an early flush of the aggregation window, mitigating the serialization penalty on the write path. A prototype evaluation with physical NVMe SSDs and 100 GbE networks shows that SENS saturates the SSD throughput ceiling using 4–5 CPU cores, halving the host-side core budget compared to the native TCP driver. With a RAMDisk backend that removes storage-media constraints, SENS sustains up to 2.5× higher concurrent IOPS. These results show that exposing storage-protocol semantics to the batching layer improves the scalability of NVMe over TCP without additional hardware. Full article

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article