The present work introduces an adaptive entangled Hamiltonian switching (AEHS) control framework for resilient grid–electric vehicle (EV) synchronization under multiple-mode disturbances in cyber–physical vehicle-to-grid (V2G)/grid-to-vehicle (G2V) infrastructures, incorporating several key innovations. First, a quantum-inspired fault modeling approach is developed to represent overcurrent, overvoltage, and phase desynchronization faults as perturbations in the system’s Hamiltonian space. This formulation enables both deterministic and probabilistic fault dynamics to be captured through fidelity-based state correlation, allowing precise quantification of grid–EV interactions under uncertainty. Second, a quantum-inspired predictive resilience function learning mechanism is proposed, where resilience is learned from the fidelity evolution between consecutive quantum states. This predictive mechanism enables the controller to anticipate fault propagation and adjust control weights preemptively, achieving proactive fault mitigation. Third, an entaglement-based Softmax adaptive mode transition strategy is introduced to ensure smooth switching among Hamiltonian operational modes. Each mode’s fault evidence score combines state residuals and fidelity deviation, and adaptive probabilities are computed via an entanglement-Softmax normalization, guaranteeing stable and chatter-free transitions. Together, these elements form the AEHS control framework, which dynamically entangles grid and EV phase dynamics for enhanced synchronization, robustness, and energy efficiency. The novelty lies in introducing an entanglement-driven adaptive switching framework that transcends traditional fault-tolerant control by encoding disturbances, adaptation, and resilience into a quantum-inspired Hamiltonian control architecture—paving the way for intelligent, self-organizing energy systems. Simulation results confirm that AEHS significantly outperforms classical PID and SMC controllers in synchronization accuracy, disturbance resilience, and adaptive learning capability, marking a substantial advancement in quantum-inspired control for grid-connected EV systems.
Emotion recognition from musical voice signals is inherently challenging due to complex temporal and spectral variations across emotions. Hence, anovel “recurrent graph fuzzy SHapley label propagation neural network (RGF-SLP-NN)” is proposed. Traditional feature extraction models fail to capture emotion-specific dynamics, like fast pitch shifts in angry or excited and gradual tonal shifts in calm or serene. Afuzzy pattern multihead wavelet extractor (FPMWE) overcomes this by adaptively capturing features across multiple time scales. Feature selection faces challenges with overlapping acoustic traits, such as pitch in excited and rhythmic bursts in angry. The self-adaptive colony additive explanations (SCAes) module dynamically weights features using ant colony heuristics and SHapley additive explanation (SHAP). Valence–arousal (V–A) mapping is hindered by formant trajectory overlaps in similar emotions, such as calm and melancholy. The entropy-based edge adaptive inference system (EB-EAIS) models formant-emotion relationships more precisely by emphasizing emotionally salient transitions. Atlast, traditional classifiers overlook contextual structure, weakening classification robustness across blended or ambiguous emotional states. The multilayer genetic label perceptron algorithm (MLGLPA) refines label separation through relational learning in graph topology. This integrated approach ensures robust, context-sensitive emotion recognition. Together, these recurrent neural network (RNN)-graph neural network (GNN) modules form a robust pipeline for high-confidence emotion classification, accurately recognizing minute, overlapping, and acoustically irregular emotional states.
The rapid growth of electric vehicles (EVs) and distributed renewable energy sources is transforming conventional power systems into highly dynamic smart grid environments requiring secure, scalable, and low-latency coordination mechanisms. The existing EV scheduling frameworks suffer from communication bottlenecks, limited transparency, and inadequate peak demand management, particularly under high transaction volumes and decentralized energy trading conditions. This work aims to develop a secure and scalable EV scheduling framework by integrating blockchain technology with reconfigurable intelligent surface (RIS)-assisted communication to enhance reliability, economic efficiency, and grid stability. Amultiobjective optimization model incorporating electricity cost, battery degradation cost, peak deviation penalty, and state-of-charge (SOC) constraints is formulated and implemented in MATLAB/Simulink. The framework further models RIS-enhanced signal-to-noise ratio (SNR), blockchain transaction latency, and throughput constraints to ensure real-time feasibility. The simulation results demonstrate 34.6% peak reduction, 93.8% grid utilization, SOC violation limited to 0.8%, and daily energy cost reduced to ₹11 248, while RIS-assisted communication improves average SNR to 29.5dB and limits transaction latency to 185ms. The scientific novelty lies in the unified cyber–physical–economic integration of RIS-enhanced communication with blockchain-secured EV scheduling under a scalable O(N) optimization structure. The proposed framework provides practical value by enabling secure peer-to-peer energy trading, resilient grid operation, and economically optimized large-scale EV coordination suitable for the next-generation smart city infrastructures. The specific novel contribution of this work is the joint formulation in which the RIS phase shift design which sets the achievable channel capacity $C(t)$ is co-optimized with the blockchain latency constraint $T_{block}(t)\le T_{max}$ inside a single multiobjective problem, coupling the physical (RIS), cyber (blockchain), and economic (cost) layers. Unlike prior work that treats EV scheduling, blockchain security, and communication reliability as independent modules, noexisting scheme optimizes the RIS phase matrix $\Phi $ under an explicit per-block latency budget tied directly to the EV-scheduling cost objective.
The power loss reduction in distribution networks is commonly achieved through reconfiguration of feeders (RF), placement of capacitors (PC), and allocation of distributed energy sources (ADES). Although these strategies have been widely studied, most existing approaches consider only peak-load conditions, despite the significant influence of load variability on network operation and planning decisions. This article introduces an MILP framework for a coordinated optimization of RF, PC, and ADES that incorporates hourly load profiles together with distributed energy sources investment and operating costs, capacitor installation costs, and switching operation expenses. The model is applied to six benchmark distribution systems and a large-scale 119-bus network to investigate the effects of load variability on optimal network configuration, component placement, and overall system cost. The results show that accounting for hourly load variations can substantially alter the optimal feeder topology, and the locations and sizes of capacitors and distributed energy sources while reducing annual operating costs compared with peak-load-based planning. However, these improvements are achieved at the expense of increased computational effort, particularly for larger networks. The findings highlight the tradeoff between modeling accuracy and computational complexity and provide practical guidance for distribution system planning under realistic loading conditions.
An all-electric ship (AES) generally employs integrated electric propulsion and coordinated onboard power supply to achieve economically efficient and environmentally sustainable maritime transportation. This article aims to evaluate the impact of wind and sea loadings on AES voyage performance and economic efficiency using a deterministic single-stage optimization model. The proposed multiobjective model optimizes total operating cost (TOC) and voyage unmet distance while satisfying navigation, propulsion, and electrical operating constraints. Wind and wave loads are modeled using analytical and semi-empirical wind-based resistance models to assess the resulting changes in propulsion power demand and attainable sailing speed. The optimization method is formulated as a mixed-integer linear programming (MILP) problem and solved using the MATLAB built-in intlinprog solver. Tradeoffs between conflicting objectives are analyzed through the Pareto front of the obtained nondominated solutions. The simulation results demonstrate that wind and wave loads significantly influence AES operation by increasing ship resistance, which in turn affects propulsion power demand.
