Search Results (3,807)

The energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) plays an important role in determining both hydrogen economy and the state-of-charge (SOC) regulation and prevention of over-charge/discharge. Existing fuzzy logic-based EMS typically employs a single fuzzy logic controller with fixed SOC thresholds, which struggles to adapt to complex and varying driving conditions. To address this limitation, this paper proposes a dual-fuzzy logic control strategy comprising a primary fuzzy logic controller (FLC1) and a secondary fuzzy logic controller (FLC2), which operate in coordination to achieve refined power distribution through ΔSOC adjustment, defined as the difference between the state of charge (SOC) of a power battery and its ideal SOC (ISOC). A series of systematic simulations are conducted under different ISOC coefficients (0.45, 0.50, 0.55, 0.60, and 0.65), and the results are compared with those obtained from the switch-fuzzy control strategy. The findings indicate that an ISOC of 0.55 yields the optimal comprehensive performance. Building on this, an extremum-seeking control (ESC) algorithm is introduced to perform online adaptive optimization of the ISOC. A comprehensive cost function that integrates hydrogen consumption, power loss, and SOC deviation is constructed to dynamically adjust the ISOC. Simulation verification is conducted on the MATLAB/Simulink R2024b and AVL CRUISE co-simulation platform under the New European Driving Cycle (NEDC). Results demonstrate that the ESC adaptive strategy achieves hydrogen consumption reductions of 10.0%, 34.6%, and 45.7% under initial SOC conditions of 35%, 75%, and 85%, respectively, compared with the switch-fuzzy control. Furthermore, it delivers an additional 1.21% hydrogen saving over the optimal dual-fuzzy control at an initial SOC of 75%. Notably, the ISOC update in the proposed strategy does not require prior knowledge of future driving cycles, and the optimization itself is performed online. However, the ESC hyperparameters (perturbation amplitude, frequency, filter time constants, integrator gain) and the cost function weights are pre-calibrated offline based on typical operating conditions, and the real-time cost evaluation relies on component efficiency signals derived from the vehicle model, providing a practical and intelligent upgrade pathway for fuzzy logic-based EMS in FCHEV applications. Full article

This paper develops a zero-sum game-based practical predefined-time reinforcement learning method for robust tracking control of highly flexible aircraft. First, the disturbed tracking problem of highly flexible aircraft is transformed into a min–max optimal control problem, where the controller minimizes the infinite-horizon performance index and the adversarial disturbance maximizes it. Then, a critic neural network is used to approximate the solution of the Hamilton–Jacobi–Isaacs equation online, and a fractional-power critic-update law is constructed so that the critic weight estimation error converges to a bounded neighborhood within a user-predefined time. The boundedness of closed-loop and practical predefined-time convergence of critic weight estimation error are rigorously proven. Several simulations are conducted to verify the advancement, effectiveness and robustness of this control algorithm. Overall, the results demonstrate that the proposed method provides an effective practical predefined-time learning framework for robust tracking control of highly flexible aircraft. Full article

Detecting disinformation in morphologically rich and under-resourced languages remains a significant challenge in natural language processing. This paper examines the role of lemmatization as a preprocessing strategy for disinformation detection in Croatian. Building on previous work and extending the evaluation to a newly collected dataset of nearly 25,000 social media comments, we systematically compare traditional machine learning classifiers (SVM, Random Forest, and neural networks) and a transformer-based model (croBERT) on both original and lemmatized text. Our findings demonstrate that lemmatization does not produce uniform gains across architectures: while linear models and croBERT display small but measurable improvements from morphological normalization, non-linear models such as RBF SVM and neural networks experience substantial declines in performance. These results indicate that lemmatization interacts differently with model inductive biases and feature extraction mechanisms. Overall, the study provides a detailed empirical assessment of preprocessing choices for low-resource, morphologically complex languages and offers practical guidance for developing disinformation detection systems in Croatian and similar contexts. Full article

In advanced manufacturing, designing multi-pulse composite driving waveforms for piezoelectric inkjet (PIJ) printing presents a constrained, high-dimensional, physical black-box optimization challenge. The feasible jetting region within the 12-dimensional parameter space is highly sparse; furthermore, traditional unconstrained optimization algorithms are prone to triggering nozzle flooding or actuator fatigue damage. To overcome this bottleneck, this paper proposes CA-TuRBO-m, a closed-loop collaborative architecture based on classifier-assisted multi-trust region Bayesian optimization. This architecture reconstructs the deposition morphology features on the substrate into a composite visual feedback source that implicitly incorporates fluid dynamics. Furthermore, it repurposes a Random Forest classifier into a dynamically iterating physical safety topological gating mechanism to actively intercept high-risk parameter combinations. Simultaneously, a multi-trust-region parallel exploration mechanism is introduced to balance global exploration and local exploitation. Experimental results demonstrate that over 200 online physical printing iterations, the proposed architecture reduces the number of invalid prints leading to system failures to an average of 3.8, achieving a high effective sampling rate of 98.1%. Without relying on complex fluid dynamic models, this approach enables precise morphological control over droplets of varying sizes and mitigates printing defects, successfully achieving multi-target adaptive regulation within a limited budget on a single physical platform. Full article

A unified power quality conditioner (UPQC) is developed for the mitigation of grid-voltage and -current distortions under steady-state and dynamic conditions. The proposed configuration integrates series and shunt inverters through a common DC-link capacitor. The capacitor functions as an energy buffer by accommodating the power exchanged between the two inverters. Following an abrupt grid-voltage disturbance or load transition, the DC-link capacitor must instantaneously deliver or absorb power to maintain the power balance between the inverters while sustaining the required compensation. The resulting transient energy exchange can produce pronounced DC-link voltage excursions, with adverse consequences for system stability and compensation accuracy. Rapid and accurate regulation of the DC-link voltage is therefore essential for maintaining the dynamic compensation performance of the UPQC. To improve this regulation, a compensatory wavelet fuzzy neural network incorporating asymmetric membership functions (CWFNN-AMFs) is introduced in place of the conventional proportional–integral (PI) controller. The network architecture and its online learning algorithm are derived in detail. Finally, experimental results under steady-state and dynamic conditions demonstrate that the CWFNN-AMF-controlled UPQC improves both power quality compensation and DC-link voltage regulation, thereby verifying the feasibility and effectiveness of the proposed control framework. Full article

Aimed at addressing the issue of frequent short-duration ICE start–stop events in P2-configuration plug-in hybrid electric vehicles (PHEVs) employing conventional instantaneous optimization-based Equivalent Consumption Minimization Strategy (ECMS)—and the resulting deterioration of vehicle smoothness, NVH performance, fuel economy, and emission performance—this paper proposes a novel energy management strategy, designated ECMS-ISS, which integrates instantaneous optimization with an engine unnecessary start suppression algorithm. A multilayer perceptron (MLP) neural network is first constructed as an online identifier to recognize high-frequency intervals of frequent start–stop events in real time. A dedicated penalty function is then embedded within these identified intervals, with the penalty intensity adaptively adjusted according to the accumulated count of short-duration start–stop events, enabling zoned and targeted intervention without affecting engine torque output during normal operating intervals. Simulation results under NEDC and WLTC driving cycles demonstrate that, compared with the conventional A-ECMS, ECMS-ISS reduces engine start–stop events by 35.48% and 32.31%, respectively, and reduces comprehensive fuel consumption by 2.13% and 5.40%, while significantly decreasing CO, NOx, and HC emissions. Compared with RB-EMS, ECMS-ISS also exhibits superior fuel economy and emission reductions, with the final SOC maintained within a reasonable range throughout. The proposed strategy demonstrates distinct advantages in reconciling multiple objectives, including start–stop rationality, fuel economy, emission performance, and battery health, thereby providing a practical and adaptive solution to the frequent engine start–stop problem in P2-configuration PHEVs. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

With the rapid growth of social media, online news has become increasingly multimodal, combining textual and visual information, posing new challenges for fake news detection. Existing methods often suffer from redundant features, distribution differences across modalities, and insufficient modeling of semantic interactions. To address these issues, this paper proposes an Adaptive Binary Osprey Optimization Algorithm and Cross-modal Disentangled Fusion model (ABOOA-CDF). First, an Adaptive Binary Osprey Optimization Algorithm (ABOOA) is developed for multimodal feature selection by integrating chaotic initialization, adaptive search, and binary mapping strategies to identify informative feature subsets. Then, a Cross-modal Relation Disentanglement Module (CRDM) is introduced to decompose multimodal representations into shared, discrepant, and complementary components, thereby enhancing semantic relationship modeling. Furthermore, an Adaptive Semantic Fusion Module (ASFM) dynamically learns fusion weights to generate discriminative multimodal representations. Experimental results demonstrate that ABOOA-CDF effectively improves detection performance. Compared with MFO and OOA, the proposed method achieves Accuracy improvements of 1.02 and 2.66 percentage points, respectively, verifying its effectiveness in feature optimization, cross-modal relation modeling, and semantic fusion. Full article

Smart charging stations require mobile charging robots to respond to dynamically arriving charging requests with heterogeneous priorities, varying travel costs, and uneven workloads while maintaining online scheduling feasibility. Conventional single-layer approaches often optimize task assignment or route ordering separately, which limits their ability to coordinate allocation quality, route efficiency, and workload regulation under real-time constraints. This study proposes a hierarchical improved consensus-based bundle algorithm–ant colony optimization (ICBBA-ACO) framework for dynamic multi-robot task allocation. The upper ICBBA layer combines deterministic task clustering, intra-cluster greedy bundling, conflict resolution, and feedback-guided workload-aware reassignment, while the lower ACO layer refines the visiting order of unstarted tasks under fixed ownership using the same normalized four-objective scheduling cost. Complete decision time is evaluated separately against a $200\mathrm{ms}$ online requirement, and estimated motion energy is retained only as a distance-derived auxiliary indicator. In a five-method comparison over 100 paired scenarios, ICBBA-ACO achieves a mean composite objective of $J=0.663052$, a mean decision time of $33.07\mathrm{ms}$, and 100% deadline compliance. GA-MRTA obtains a lower unconstrained mean objective of $J=0.615790$, but requires approximately $2199.30\mathrm{ms}$ on average and satisfies the $200\mathrm{ms}$ requirement in only $8.89\%$ of the evaluated updates. Thus, ICBBA-ACO provides the lowest mean objective among the compared methods that maintain full deadline compliance, demonstrating a favorable quality–runtime trade-off within the tested operating range. ROS-based engineering verification further completes all 15 repeated trials and all 48 verification tasks with no recorded invariant violations. Full article

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

This paper presents a novel fractional-order hybrid observer framework for robust sensorless control of brushless DC (BLDC) motor drives in unmanned aerial vehicle (UAV) propulsion systems, addressing the fundamental limitations of conventional integer-order observers through the lens of fractional calculus. The proposed architecture synergistically integrates high-frequency square-wave signal injection for zero/low-speed operation with an adaptive fractional-order extended Kalman filter (AFEKF) augmented by online stator resistance and flux linkage estimation, capitalizing on the memory and hereditary properties inherent to fractional-order systems. A minimum-order current observer enables accurate three-phase current reconstruction using a single DC-link sensor, substantially reducing hardware complexity and cost. The complete algorithm is implemented on an STM32H7 microcontroller and experimentally validated on a 1.5 kW drone propulsion testbench and in-flight platform. Results demonstrate reliable startup under 50% rated load, stable operation from standstill to 5000 RPM on the UAV motor (and validated up to 22,000 RPM on a high-speed test motor, <4° electrical position error at 5 kRPM, and strong robustness against 35% stator resistance variation. In-flight tests confirm improved thrust smoothness and hover stability compared to conventional sensorless strategies. The proposed fractional-order architecture offers a practical, resilient, and computationally feasible solution for next-generation autonomous aerial systems, establishing a new paradigm for observer design in electric propulsion. Full article

Generative AI shopping assistants are becoming a primary touchpoint for online consumers, yet they often produce convincing but inaccurate content, a phenomenon known as AI hallucination that poses an under-studied risk to consumer trust in e-commerce. This study explains that phenomenon by building and testing a moderated mediation model grounded in Expectation Violation Theory, Epistemic Vigilance Theory, and Algorithmic Trust Repair Theory. A sequential, exploratory mixed-methods design was used: a qualitative phase identified the dimensions and configurational pathways of consumer trust withdrawal using the Gioia methodology and fuzzy-set Qualitative Comparative Analysis, and a subsequent large-scale quantitative phase tested and refined the resulting model across a multi-country European sample using partial least squares structural equation modeling and Necessary Condition Analysis. The results show that exposure to hallucinations triggers expectation violation, activating epistemic vigilance and reducing perceived AI competence; this sequence drives trust recalibration, reflected in lower continued-use and purchase intentions and greater negative word-of-mouth. AI literacy, prior trust, and transparency cues significantly moderate these relationships, and structural trust repair mechanisms, namely retrieval-augmented generation and uncertainty disclosure, prove more effective than purely communicative repair strategies. Theoretically, this study advances a dynamic account of trust recalibration in AI-mediated commerce; practically, it offers concrete guidance for platform design and regulatory policy under the EU AI Act. Full article

Rapid cooperative detection of stationary targets by multiple unmanned aerial vehicles (UAVs) is important in time-critical missions such as search and rescue. However, the online coordination of probabilistic inference, distributed communication, and detection–motion decisions under local information remain challenging when targets exhibit spatial correlations that existing methods typically neglect. To address this challenge, we develop a distributed event-driven Bayesian search framework for stationary, spatially correlated targets at unknown locations. The framework couples three components. A pairwise spatial model and a distance-dependent Neyman–Pearson detector yield a Bayesian belief update whose unclipped product form is order-invariant to event-processing sequence. A distributed selective flooding algorithm propagates only positive detection events, achieving finite-time event-set consensus over connected graphs while avoiding full-map exchange. A decoupled detection–motion planner exhausts high-belief cells within each UAV’s field of view before selecting a waypoint that balances surrogate detection probability against travel cost, with responsibility regions dynamically renegotiated among neighbors when local high-value cells are depleted. In numerical experiments, the proposed method achieved zero uncoordinated repeat detection in all simulations and significantly reduced first-discovery coverage relative to static-partition and no-communication baselines, while adapted external baselines required 90-fold and 6-fold larger communication payloads and had nonzero repeat-detection rates. The framework thus occupies a specific tradeoff point of zero revisit, sparse communication, and early discovery gain in scenes where targets span multiple UAV search regions. Full article