Search Results (950)

The sixth generation of mobile networks (6G) represents a paradigmatic shift in the conception of wireless communication systems, where Artificial Intelligence (AI) is not integrated as an additional feature but is conceived as a native and fundamental component of the physical layer (PHY). This paper presents a comprehensive survey of the state of the art in AI-native physical layer for 6G, synthesizing approximately 100 references from the period 1948–2025. The survey systematically covers 5 main PHY components (channel coding, channel estimation, signal detection, beamforming, and semantic communications) and analyzes 8 AI architectural families (autoencoders, CNN, RNN/LSTM, Transformers, GNN, GAN, Diffusion Models, and Foundation Models), addressing theoretical foundations, proposed architectures, learning algorithms, implementation challenges, and future research directions. A rigorous mathematical framework underpinning these developments is presented, including optimization formulations, convergence analysis, and theoretical performance characterization. Published results from the literature demonstrate that AI-native physical layer can improve conventional performance metrics and enable emerging capabilities essential to 6G, such as semantic communications, predictive environmental adaptation, and operation in previously inaccessible computational complexity regimes. However, such gains are conditional on adequate training resources, robust channel-matched data, and careful consideration of known limitations including generalization across channel distributions, sample inefficiency, model interpretability, and hardware implementation constraints—all of which are critically analyzed in this survey. A reproducible proof-of-concept benchmark further confirms that, under severe resource constraints, autoencoder-based codes currently underperform conventional schemes, highlighting the gap between theoretical potential and practical deployment readiness. Full article

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS). Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article

Offshore floating photovoltaic (OFPV) is an important renewable energy technology, and assessing the reliability of mooring systems is of great significance for promoting the large-scale commercial deployment of OFPV. However, owing to the complexity of the system structure, relevant reliability research has not been extensively carried out. With this in view, this work focuses on the systematic reliability analysis of a novel parallel mooring system composed of elastic ropes and anchor chains under the ultimate limit state (ULS), accidental limit state (ALS) and fatigue limit state (FLS), considering both long-term cyclic and extreme environmental conditions. The first-order second moment (FOSM), first-order reliability method (FORM) and Monte Carlo simulation have been employed to calculate the failure probabilities. By applying the series-parallel model to integrate multimode failures, it is confirmed that the failure probability of the entire mooring system is significantly greater than that under any single limit state. The results indicate that anchor chain is the main fatigue-critical component, and the Monte Carlo simulation based on extensive random sampling data is more conservative in reliability estimation than FOSM and FORM which cannot fully capture all distribution characteristics. This work could provide essential theoretical support for the safe design of subsequent OFPV mooring systems. Full article

This paper presents a comprehensive UAV testbed that establishes quantitative baselines for hardware vulnerability diagnosis and cyber–physical security validation by leveraging comparative flight logs from PX4-based Software-In-The-Loop (SITL) simulations and multiple real-world quadrotor missions. The testbed utilizes a unified data pipeline centered on the uORB message bus and ULog format, enabling the extraction of high-resolution telemetry, including raw Inertial Measurement Unit (IMU) data, state-estimation, and actuator control signals. Evaluated across varying environmental conditions, side-by-side time-series and statistical analyses reveal critical sim-to-real discrepancies in sensor fidelity, GPS interference, and onboard resource behavior that are often overlooked in virtual environments. Real-world data exposes hardware-induced noise, mechanical vibrations, and electromagnetic disturbances that significantly impact flight stability and system reliability. By mathematically quantifying these discrepancies (e.g., via variance and probability distribution shifts), the proposed testbed establishes a rigorous baseline for distinguishing natural physical variability from anomalous or adversarial behavior. Ultimately, this work provides a foundational framework for developing robust anomaly detection models and validating the cyber–physical security of autonomous UAV systems in safety-critical environments. Full article

To mitigate the risks of pressure surges and water hammer during accidental pump trips in industrial cooling water systems, accurate boundary modeling of cooling towers is essential. This study employs the Method of Characteristics (MOC) to evaluate four equivalent models for the central riser shaft: Model A (constant level), Model B (two-way surge tank), Model C (dynamic coupling of shaft and distribution channel), and Model D (composite structure). Results indicate that Model A fails to reflect actual hydraulic states, producing an unrealistic pump reverse speed of −253.24 r/min and overly conservative estimates. While Models B, C, and D exhibit similar pressure trends, Model C most accurately captures the physical drainage process, realistically simulating how the shaft level stabilizes at the distribution channel elevation before declining. By accurately reflecting engineering hydraulics, Model C provides the most reliable basis for water hammer safety assessments. It is recommended for optimizing pump valve closure strategies, vacuum breaker installations, and siphon protection designs in power plant systems. Full article

This paper proposes a voltage-sensitivity estimation and droop-allocation framework for virtual power plant (VPP)-based frequency regulation in partially observable distribution feeders. In practical distribution systems, active-power adjustments by distributed energy resources (DERs) for frequency regulation may cause voltage excursions, while full real-time feeder information is often unavailable. To address this issue, an anchor-least-squares (Anchor-LS)-aided sensitivity-estimation method is developed using only point-of-common-coupling (PCC) voltage measurements and feeder-network information. Unlike state-estimation-based, data-driven, or optimization-heavy approaches that typically require wider measurement coverage, large training datasets, or repeated centralized computation, the proposed framework is designed for fast VPP-based frequency regulation under partial observability using only limited PCC measurements and feeder information. The proposed method reconstructs an approximate operating point and derives an operating-point-sensitive PCC voltage-magnitude-sensitivity matrix based on a coupled Z-bus formulation. Based on the estimated sensitivity, a voltage-constrained asymmetric droop-allocation framework is developed for under-frequency and over-frequency events, together with a practical iterative droop-adjustment method that mitigates PCC voltage violations without relying on a full optimization-based dispatch model. The proposed framework is validated through two case studies. In Monte Carlo simulations on the IEEE 33-bus feeder, the proposed sensitivity model reduced the mean RMSE by about 117 times compared with the common-path resistance method and by about 30 times compared with the conventional Z-bus method. In simulations on a practical 115-bus Korean distribution feeder, the proposed method achieved acceptable droop capacities comparable to those of a centralized LP baseline while reducing the mean computation time by about 3.2 times for both under-frequency and over-frequency events. These results confirm the practical usefulness of the proposed framework for fast VPP-based frequency regulation in real distribution networks under partial observability. Full article

In this paper, for leader–follower structure multi-agent systems (MASs), a new fault-tolerant consensus control mechanism which is called the distributed information estimation and centralized control scheme is developed. To begin with, for each follower agent, an unknown input observer (UIO) is designed to obtain the asymptotic convergence state estimation. Then, a fault reconstruction (FR) method is proposed through constructing an interval observer by sensor measurement output. Most importantly, using the leader’s state estimation provided by the local observer, a distributed observer (DO) is designed so that each follower can obtain the leader’s state estimation. Subsequently, for each follower agent, by using its own state estimation and FR, and the leader’s state estimation offered by the DO, a centralized controller is designed. In this way, a DO-based distributed fault-tolerant control protocol is developed, in which the distributed feature is majorly reflected by the DO construction, resulting in the controller being formulated in a centralized way. In addition, under the DO-based distributed fault-tolerant control protocol, MAS consensus can be reached. Finally, two simulation examples are given to show the effectiveness of the proposed methods. Full article

Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article

The stress–strength reliability model is widely applied in various fields such as mechanical engineering, materials science, and aerospace engineering to identify weak links in systems and thereby improve system reliability. This paper analyzes the stress–strength reliability for multi-state systems composed of multi-state components. One of the main contributions is the derivation of a multi-state stress–strength reliability model under combined stresses based on the generalized survival signature theory. In the model analysis, it is assumed that each component of the system is subjected to two different stresses corresponding to two different strengths, and that the stress variables and strength variables are mutually independent and all follow the exponentiated Pareto distribution with the common second shape parameter. Another contribution is the use of maximum likelihood estimation, empirical Bayesian estimation, and weakly informative Bayesian estimation to estimate the variable parameters and the stress–strength reliability under the progressive first-failure censoring scheme. In addition, the asymptotic confidence intervals for the stress–strength reliability model are derived, and the Bayesian credible intervals are constructed based on MCMC sampling. Finally, through MCMC simulation of a three-state consecutive 3-out-of-5: G system, the accuracy of the variable parameters and the stress–strength reliability under the aforementioned point estimation and interval estimation methods is analyzed, and the performance of these estimation methods is compared under different sample sizes. In addition, sensitivity analyses were conducted on the common shape parameter $w$ and the hyperparameters of the weakly informative prior distributions. Furthermore, a real data set is applied to illustrate the proposed procedures. Full article

To address the difficulty of finely classifying complex optical remote sensing images, this paper innovatively proposes a new image classification method based on quantum statistics (QS) inspired by quantum physics. Each pixel in the image is regarded as a fermion, which is one of the fundamental particles in quantum systems. The energy of the energy level where fermions are located is described using the negative logarithm of the distribution that the spectrum of the pixel follows. The Fermi-Dirac distribution, a quantum statistics model used to describe the complex occupation pattern of energy levels by fermions, is employed to characterize the membership relationship between pixels and classes, instead of traditional distance measures and probability measures. Then, the cost function guiding the convergence of classification is defined based on free energy, which is used to describe whether a system is in a state of thermal equilibrium according to energy, temperature, and entropy. To minimize the free energy, the derivative method and the simulated annealing algorithm are adopted to estimate the optimal solution for model parameters. The proposed method can describe complex features more effectively, obtain fine classification results, and overcome the curse of dimensionality in high-dimensional image classification. Finally, the feasibility and effectiveness are verified through qualitative and quantitative analysis of multispectral and hyperspectral image classification experiments. Full article

This study presents a simulation-based framework for battery-degradation-aware routing in electric vehicles by integrating physics-informed battery state estimation with decision-level navigation logic. A hybrid estimation approach combining spatially distributed fiber-optic sensing with complementary Kalman filtering strategies is used to reconstruct core temperature, surface temperature, state-of-charge, and mechanical degradation indicators in real time. These estimated states are supplied directly to an intelligent routing module, enabling charging station selection that is both physically reachable and aware of thermal- and health-related constraints. The results demonstrate that routing decisions informed by battery state estimation consistently avoid high-risk thermal and swelling conditions while maintaining range feasibility. By explicitly incorporating mechanical degradation indicators into the routing logic, the framework addresses a key gap in prior studies where battery swelling and navigation were treated independently. Overall, the findings confirm that estimator-driven, degradation-aware routing can improve operational safety, reduce range anxiety, and support more reliable electric vehicle navigation. The study establishes a simulation-first foundation for future experimental validation, adaptive policy refinement, and broader deployment of battery-degradation-aware decision-making in electric mobility systems. Full article

In recent years, microreactors have attracted increasing attention as next-generation chemical reactors, enabling rapid and highly efficient reactions, while requiring precise control against temperature variations. In this paper, a research platform for a microreactor process close to practical implementation is constructed using a distributed control system (DCS) and wireless communication. By establishing such a research platform, not only the effectiveness of control methods but also discussions on system configuration, including operation and maintenance, can be verified and optimized at an early stage. Moreover, operator-based multi-dimensional nonlinear control strategies have been applied in existing studies of channel temperature control. In contrast, this paper extends such strategies by integrating an operator-based feedback scheme with state estimation via particle filters, which simultaneously accounts for unknown communication delay compensation and the nonlinear characteristics of microreactors. Finally, the feasibility and effectiveness of the proposed research platform are verified through real-world experiments. Full article

Industrial activities represent the primary source of anthropogenic carbon dioxide (CO₂) emissions, and accurate monitoring of industrial CO₂ emissions is critical to mitigating greenhouse gas emissions. Due to the lack of quantifiable and direct measurement technologies, industrial CO₂ emissions are typically calculated based on fuel combustion consumption and emission factors. However, the calculation method is not applicable to the quantification of fugitive emissions of CO₂. This work demonstrates the capability of remotely measuring industrial CO₂ emissions by mobile Differential Absorption Lidar (DIAL) system. The two-dimensional concentration distributions of the CO₂ plume were remotely measured using DIAL system, and the CO₂ emission rate was obtained with wind field information. The DIAL measurements were cross-validated using in-stack CEMS data and emission-factor calculations. Results show that the relative deviations of CO₂ emission rates between DIAL and CEMS range from −5.83% to +2.57% across four tests, all within ±6%, and the coefficient of variation (CV) of 27 valid datasets is 7.24%. In contrast, the emission factor method yields consistently higher estimates, with relative deviations of +4.61% compared to DIAL measurements. Furthermore, the mobile DIAL system was deployed in three industrial scenarios with different emission intensities: a natural gas-fired industrial park, a photovoltaic glass manufacturing plant (low-emission steady-state), and a coal-fired power plant (high-emission dynamic), demonstrating its preliminary adaptability under different operating conditions. This study indicates the feasibility and potential reliability of the mobile DIAL system for high spatio-temporal resolution remote measurement of industrial CO₂ emissions. Full article

An ever-increasing demand for higher photon generation rates in quantum light sources often leads to the generation of multiple photon pairs, making quantum secure imaging, sensing, and communication vulnerable to photon number splitting (PNS) attacks. Here, we investigate the use of weak coherent sources (WCS) and heralded single-photon sources (HSPS) in conjunction with quantum key distribution protocols to mitigate these risks. Our initial observation shows that the BB84 protocol using HSPS has an advantage in secured information transfer over the WCS. We then extend our comparative study between WCS and HSPS to high dimensional protocols and conduct a rigorous analysis to estimate a benchmark for quantum advantage in secure bit rate thresholds for secure information transfer. When combined with high-dimensional states (hybrid encoding), the two-state non-orthogonal encoding protocol offers an increased resistance to PNS and unambiguous state discrimination attacks. These findings suggest that integrating high dimensional encoding would strengthen the security and performance of quantum secure imaging, sensing, and communication systems for practical and resilient implementations at shorter distances. Full article