Search Results (499)

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. Full article

This paper investigates the tracking control problem for a class of networked nonlinear systems in a non-ideal communication environment, where both internal communication constraints (delays and packet dropouts) and external random deception attacks are taken into account. From a symmetry perspective, the backward and forward channels constitute a paired sensing–actuation structure, and channel-dependent imperfections may destroy their functional coordination. To compensate for the resulting sensing–actuation mismatch, a data-driven sliding-mode predictive tracking control scheme is developed without relying on an explicit system model. First, an equivalent dynamic linearization is adopted to represent the input–output behavior using a data-dependent incremental model. Then, using delayed measurements together with historical input–output data, an online estimator is constructed to update the pseudo partial derivative (PPD). Based on the estimated PPD, a multi-step predictor is further designed to generate the predicted outputs, and a data-driven sliding-mode predictive tracking controller is proposed by imposing a discrete reaching law on the predicted outputs. Rigorous analysis is provided to ensure the stability of the closed-loop system and to guarantee that the tracking error remains bounded, together with an explicit bound that reveals the influence of the delay horizon, estimation mismatch, and attack amplitudes. Finally, numerical simulations under square-wave and sinusoidal references validate the effectiveness and robustness of the proposed approach. Full article

Uncertain dynamic games have recently emerged as a rigorous and versatile framework for the analysis and synthesis of multi-agent decision-making processes in complex, stochastic, and dynamically evolving environments. By integrating foundational concepts from dynamic game theory with neural network-based function approximation techniques, these methodologies facilitate the development of adaptive, data-driven strategies for agents whose interactions unfold over time and are subject to both state and control constraints. Notwithstanding these advances, practical implementations are invariably influenced by model inaccuracies, exogenous disturbances, and parametric uncertainties, all of which may substantially impair system performance and jeopardize stability if left unmitigated. In this context, the present study examines dynamic game formulations defined on perturbed and uncertain system models, explicitly incorporating state and control constraints, with the objective of ensuring robustness and reliability in both competitive and cooperative settings. We consider a broad class of nonlinear dynamic games characterized by system dynamics affected by unknown disturbances and uncertain parameters. Within this framework, Dynamic Neural Networks (DNNs) are employed to approximate feasible solutions to the associated robust control problem, thereby enabling the characterization of $\epsilon$-Nash equilibria through learning mechanisms driven by worst-case trajectory realizations. A comprehensive theoretical analysis is developed to elucidate the effects of perturbations and uncertainties on equilibrium existence, convergence behavior, and closed-loop stability properties. Furthermore, sufficient conditions are established under which the neural learning dynamics ensure boundedness and convergence to approximate Nash or saddle-point equilibria, despite the presence of modeling imperfections. The proposed framework effectively synthesizes principles from robust control theory and learning-based game-theoretic approaches, yielding formal guarantees that are often absent in purely data-driven methodologies. Finally, numerical simulations conducted on representative dynamic game scenarios substantiate the efficacy of the proposed approach, demonstrating enhanced robustness relative to nominal neural game formulations. These findings contribute to the advancement of dependable dynamic game architectures, with potential applications spanning autonomous systems, robotics, and networked control systems operating under uncertainty. Full article

Supply chain control requires balancing operational performance and financial efficiency when decisions are made using delayed and imperfect demand information. Although fixed heuristics, adaptive policies, and reinforcement learning approaches have been proposed, their relative effectiveness and robustness under temporary informational mismatch remain unclear. This study addresses this gap by developing an integrated simulation–reinforcement learning framework that jointly captures operational and financial dynamics in supply chains, which enables adaptive optimisation of working capital policies under uncertainty. A unified simulation framework is developed for a multi-echelon supply chain that jointly models service levels, backlog, customer retention, and working capital exposure through the cash conversion cycle. Five classes of controllers are evaluated: fixed-threshold heuristics, adaptive threshold policies optimised using stochastic and evolutionary search, and a reinforcement learning controller based on proximal policy optimisation. Performance is assessed under stationary demand and under demand disruptions. The results reveal a clear hierarchy of performance. Fixed heuristics provide transparent and stable baselines but suffer from structural rigidity. Adaptive threshold policies substantially improve coordination, with evolutionary search yielding the strongest performance among structured approaches. The reinforcement learning controller achieves the best overall outcomes by learning a nonlinear state–action mapping that sharply reduces backlog and service shortfalls while maintaining comparable working capital exposure. These gains arise from improved coordination across operational and financial decisions rather than single-metric optimisation. Practically, adaptive heuristics offer robust baselines, while learning-based controllers are most valuable in more volatile environments. Full article

This study proposes a novel hardware-impairment-aware convolutional neural network (CNN)-based hybrid precoding scheme for cell-free massive multiple input multiple output (MIMO) systems operating in the terahertz (THz) band under practical constraints of imperfect channel state information (CSI) and transceiver hardware non-idealities. In a realistic THz simulation environment incorporating molecular absorption, phase noise, channel aging, and power consumption models, the proposed CNN precoder demonstrates significant performance improvements over conventional Zero-Forcing (ZF), Kalman, and Minimum Mean Square Error (MMSE) schemes. Quantitative results show that the CNN achieves spectral efficiency gains of 10.67% over Kalman, 14.67% over MMSE, and 70% over ZF for an eight-user scenario. In addition, the CNN-based precoder provides an SNR gain of 0.8 dB over MMSE and 2 dB over ZF. Complexity analysis indicates that the CNN approach is 17% less complex than ZF, 44% less complex than Kalman, and 60% less complex than MMSE. Further analysis of individual impairment effects reveals that the CNN effectively mitigates the compounded degradation caused by hardware distortions and CSI imperfections, exhibiting only a 25% performance loss compared to an ideal hardware baseline. These results establish the proposed data-driven precoder as a robust, computationally efficient, and high-performance solution for reliable and energy-sustainable ultra-high-throughput THz communication networks. Full article

In vehicular ad hoc networks (VANETs), using vehicle platooning can improve traffic efficiency, reduce driving energy consumption, and ease traffic congestion. However, since land-based stations have limited coverage (about 7% of the Earth’s surface), ensuring low-latency communication is challenging. To address this issue, the introduction of solar-powered unmanned aerial vehicles (UAVs) as aerial base stations provides flexible and extensive communication support for vehicle platooning. Additionally, intelligent connected vehicles (ICVs) adopt non-orthogonal multiple access (NOMA) techniques for uplink transmission to further enhance transmission performance. Motivated by the above, this paper investigates the latency optimization problem of UAV-supported vehicle platooning by jointly considering multi-dimensional resource allocation and imperfect channel state information (CSI) affected by mobility. To solve this problem, we propose an iterative optimization approach with polynomial complexity, where the transmitted power and channel allocation are tackled in turn. Then, an analytical framework is developed to analyze the probability that NOMA is superior to OMA, guiding parameter settings for UAV-supported vehicle platooning. Finally, the simulation results show that the proposed latency optimization scheme can achieve lower total and average latencies on the uplink compared to state-of-the-art works and the benchmark scheme using OMA. Moreover, this paper elucidates the convergence, performance gap, and computational complexity associated with the proposed iterative optimization approach. Furthermore, the probability of NOMA outperforming OMA is quantified through Monte Carlo experiments, which validates the correctness of the developed analytical framework. Full article

This paper presents a Sim2Real policy transfer framework for distributed control in cyber-physical production systems using State-Based Potential Games (SbPGs). While fuzzy inference systems (FISs) or other conventional control policies provide interpretable and stable control policies for manufacturing processes, their direct deployment in real systems is often affected by Sim2Real discrepancies caused by actuator imperfections, sensor uncertainty, and process variability. To address this limitation, we propose a hybrid control architecture in which an optimized rule-based conventional control policy (i.e., FIS used in a non-adaptive, expert-knowledge-driven manner) serves as a baseline controller and SbPG-based policy adaptation refines the control actions online, while keeping the distributed manner, and is proven to converge. To evaluate robustness during Sim2Real deployment, deterministic and stochastic noise injection mechanisms are introduced to emulate systematic actuator biases and random disturbances. The proposed framework is validated on a laboratory-scale distributed production system. Experimental results in both simulation and real-world environments demonstrate that the SbPG-based adaptation compensates for disturbances and maintains production objectives under actuator, sensor, and parameter uncertainties. Compared to standalone FIS control, the proposed approach consistently reduces overflow and power consumption while satisfying production demands under noisy operating conditions. Additional ablation studies further confirm the robustness of the policy transfer strategy and the effectiveness of global and local interpolation mechanisms in the SbPG learning. Full article

Mode coupling in multimode step-index polymer optical fibers (SI POFs) plays a critical role in determining signal integrity and bandwidth performance in optical communication systems. It originates from intrinsic random perturbations that influence power distribution among propagating modes, making accurate prediction of steady-state distributions (SSDs) essential for reliable system design. In this work, we model mode coupling as a stochastic process using the Langevin equation, incorporating simulated Langevin forces to numerically evaluate modal power evolution and steady-state behavior. The proposed approach demonstrates strong agreement with previously reported experimental results, validating its capability to capture energy redistribution mechanisms induced by fiber imperfections. From a telecommunications perspective, the model provides valuable insights into modal dispersion, bandwidth limitations, and signal degradation in SI POF-based links. These results establish a robust and efficient framework for analyzing and optimizing multimode SI POFs, supporting their application in high-speed data transmission and short-reach optical communication networks. Full article

Accurate transmission-line parameters are essential for reliable, efficient, and sustainable smart grid operation, especially under increasing renewable-energy integration and data-driven grid management. However, line aging, temperature variations, and measurement outliers may cause significant deviations between actual and nominal grid models, thereby degrading the state estimation, power-flow analysis, and operational security assessment. To address these challenges, this paper proposes a robust transmission-line parameter estimation method based on a variable-projection framework. The proposed framework decomposes the original high-dimensional, strongly coupled, and non-convex joint estimation problem into two subproblems associated with line-parameter identification and operating-state calibration. An iteratively reweighted least-squares algorithm based on the Huber M-estimator is introduced to dynamically adjust measurement weights and suppress the influence of outliers. The preconditioned conjugate-gradient method is further employed to avoid the explicit inversion of large-scale normal matrices. Simulations on the IEEE 118-bus system demonstrate that the proposed method achieves a higher parameter-estimation accuracy and stronger robustness than conventional weighted least-squares and joint state-parameter estimation methods. In the base case, the proposed method reduces the RMSRE of line reactance to 0.0794%, compared with 0.1558% for WLS and 0.1126% for JSE. Under the representative 5% gross-error case, the proposed method maintains lower RMSREs of 0.9772%, 0.0875%, and 5.8536% for $R_l$, $X_l$, and $B_{sh}$, respectively. Further sensitivity tests under contamination ratios from 1% to 20%, outlier magnitude factors from 1.5 to 5.0, and different outlier-location patterns confirm that the proposed method maintains a more stable estimation accuracy than WLS, conventional JSE, and Huber-JSE without VPM under diverse bad-data conditions. In downstream operational evaluations, it reduces the branch active-power flow RMSE from 1.6842 MW to 0.7215 MW, voltage-magnitude RMSE from 0.00482 p.u. to 0.00216 p.u., and active-power-loss error from 2.4368% to 0.9327% compared with WLS. These quantitative results indicate that the proposed approach can improve the grid model accuracy under imperfect measurements, thereby supporting reliable and sustainable smart-grid operation. Full article

This paper investigates abnormal secondary voltage drops in dry-type transformers operating in isolated offshore power systems. While conventional analyses primarily attribute voltage deviations to load conditions and transformer impedance, this study shows that noticeable voltage drops can also occur under no-load conditions due to the combined effects of voltage unbalance, harmonic distortion, and residual magnetic flux. A comprehensive approach integrating on-site measurements, PSCAD simulations, and laboratory experiments is employed to systematically analyze this phenomenon. The results indicate a coupled electromagnetic effect in which source-side voltage imperfections induce asymmetric core flux distribution, which is associated with reduced secondary voltage. In addition, a relationship between synchronous generator winding pitch and harmonic voltage distortion is observed, suggesting its influence on power quality in isolated grids. Simulation results show that the interaction of these factors can lead to a secondary voltage drop of approximately 4–6 V under no-load conditions, even in the absence of transformer defects. Finally, mitigation strategies based on voltage balancing and harmonic reduction are experimentally validated, restoring the secondary voltage to 1.002 pu. These findings provide practical insights for improving voltage stability and power quality in offshore and other isolated power systems. Full article

To clarify the instability behavior of the columnar microstructure in RF magnetron sputtered TiN coatings under compressive loading, experimental characterization and finite element simulation were combined to investigate the microstructural features, mechanical properties, and linear and nonlinear buckling responses of the coating. TiN coatings were deposited on cemented carbide and Si substrates by RF magnetron sputtering using a 99.9% purity TiN target. The surface and cross-sectional morphologies were characterized by field-emission scanning electron microscopy, and the nanohardness and Young’s modulus were determined by nanoindentation. Based on the experimentally observed morphology and measured mechanical properties, a finite element model of the columnar structure was established in ABAQUS, and the instability responses predicted by solid, shell, and beam element models were comparatively analyzed. The results showed that the as-deposited TiN coating exhibited a dense and uniform surface and a distinct columnar microstructure in cross-section. Linear buckling analysis indicated that the first-order critical buckling loads predicted by different element models were different, among which the solid element model gave a value of 3.43 × 10⁻⁵ N, showing the closest agreement with the theoretical result. Furthermore, nonlinear buckling analysis was performed by introducing an initial geometric imperfection of 4 × 10⁻³ mm based on the first-order buckling mode of the solid element model. The results showed that the columnar structure became unstable at a load of 0.74 × 10⁻⁶ N, accompanied by irreversible deformation. These findings demonstrate that linking experimentally observed TiN columnar microstructures with microstructure-informed instability analysis provides a useful perspective for understanding the local instability behavior and potential failure tendency of sputtered coatings and offers theoretical support for the structural design and reliability evaluation of protective coatings for cutting tools. Full article

This paper presents a comprehensive investigation of the impact of I/Q (In-phase/Quadrature) imbalance on the performance of a six-port receiver operating in the millimeter-wave band, specifically in the 60–65 GHz frequency range. Unlike traditional heterodyne architectures, the six-port junction offers a low-cost and low-power alternative for direct conversion; however, it is highly sensitive to hardware imperfections. This study demonstrates that manufacturing tolerances in passive components, such as 90° hybrid couplers and power dividers, introduce significant amplitude and phase disparities. These imbalances geometrically distort the ideal QPSK constellation, transforming the circular decision boundaries into an elliptical profile. The research methodology employs a robust co-simulation approach in Advanced Design System (ADS), integrating measured S-parameters with mathematical analysis to quantify signal degradation. Performance is evaluated using the Error Vector Magnitude (EVM) metric. The experimental findings reveal that even at the higher end of the spectrum (65 GHz), where the amplitude imbalance reaches 0.7 dB and the phase error is approximately 5°, the six-port QPSK receiver maintains an EVM of 8.7%. This result is comfortably below the 17.5% limit mandated by modern wireless communication standards, such as LTE and 5G. These results confirm the architectural resilience of the six-port receiver, validating its effectiveness as a reliable solution for high-speed, short-range data transmission in future ultra-wideband telecommunication infrastructures. Full article

Inter-turn short-circuit faults (ITSC) in the stator winding of large synchronous machines are among the most critical failures in power systems and may lead to severe insulation damage and unplanned outages. At the same time, such faults, due to their nature in critical industrial scenarios, make it difficult to collect sufficiently rich labeled datasets for data-driven and deep-learning-based diagnostic methods. Training diagnostic models purely on simulated signals often results in a severe domain shift between the digital twin and the physical machine due to nonlinearities, mechanical noise, and measurement imperfections, causing a significant degradation of performance when the model is deployed in practice. This paper proposes a hybrid diagnostic framework that combines a nonlinear physics-based digital twin of a synchronous machine, formulated using an extended Park’s transformation model with a dedicated fault loop, with a Domain-Adversarial Neural Network (DANN) driven by a minimal physics-guided feature vector composed of the 100 Hz and 200 Hz harmonic amplitudes of the excitation current. Simulated data from the digital twin are used as a labeled source domain, whereas test-bench measurements of the excitation current form an unlabeled target domain, enabling unsupervised sim-to-real transfer of the stator fault resistance. The proposed architecture achieves accurate regression of the stator fault-loop resistance on a laboratory machine without any labeled measurements of real faults. Experimental results demonstrate Mean Absolute Error (MAE) below 3% across the investigated fault severity range, significantly outperforming baseline approaches that lack domain adaptation. The industrial significance of this approach lies in its potential to facilitate a transition from reactive to predictive maintenance. By enabling early-stage detection, the framework allows power plant operators to avoid catastrophic failures and significantly reduce exceptionally high costs associated with unplanned outages and cascading grid disturbances. Full article