Search Results (18,257)

Converter-dominated power systems increasingly require flexible resources that can support frequency while preserving a physically interpretable energy trajectory. Thermal-energy-storage (TES)-based virtual energy storage systems (VESSs) can shift electrical demand within thermal-energy and comfort constraints, and have therefore attracted extensive interest. However, existing studies commonly coordinate requested or normalized power without fully connecting it to actuator execution and the electrical-to-thermal energy path. Command-level sharing cannot be directly equated with executed physical power, and controller storage cannot be combined with joule-valued hardware energy without dimensional separation. Therefore, this paper proposes a physically coupled and energy-traceable virtual asynchronous machine (VAM) control method for TES-based VESSs. First, a loss-resolved averaged model establishes the point-of-common-coupling (PCC)–converter–DC-link–actuator–TES physical chain and separates the hardware Hamiltonian from the dimensionless control Lyapunov function. Second, a neighbor-coupled marginal controller is embedded in a command–projection–execution chain so that frequency regulation and weighted sharing are evaluated using the executed service. Third, a constraint-handling mechanism combines directional headroom gating, actuator saturation and ramp limits, thermal comfort bounds, and request-inactive state reset to maintain executable trajectories under the declared constraints. Simulations under a sustained $120\mathrm{kW}$ disturbance show that primary-only control retains a $-0.08883\mathrm{Hz}$ steady-state offset, whereas the proposed nominal case restores frequency. In the constrained case, the 30 s terminal trend remains above the prescribed limit, while both terminal windows of the 60 s run satisfy the restoration criterion; the final-window mean error and dimensionless eligible-unit sharing spread are $-6.317\times {10}^{-5}\mathrm{Hz}$ and $6.564\times {10}^{-5}$, respectively. The model-internal electrical–thermal balance achieves a dimensionless relative RMS residual of $6.2361\times {10}^{-8}$. Because the PCC voltage/current pair is reconstructed from the same power source, this residual quantifies model-internal consistency rather than independent measured closure. These results demonstrate constrained frequency restoration, executed-power coordination, and energy traceability within the averaged-model scope. Full article

In this paper, the fixed-time fault-disturbance estimation problem for an emergency rescue quadrotor UAV during the takeoff–mission execution–landing process is investigated. Considering the complex operating conditions of rescue missions, actuator efficiency loss, actuator bias faults, rescue-environment airflow disturbances, payload-induced disturbances, near-ground aerodynamic effects, and unmodeled dynamic uncertainties are uniformly represented as a lumped fault disturbance entering the angular-velocity dynamic channel. A multi-stage disturbance model is first established according to the takeoff stage, mission execution stage, and landing stage. Then, a fixed-time fault-disturbance estimator with stage-dependent gains is designed to estimate the lumped disturbance. Based on matrix Lyapunov functions and fixed-time stability theory, it is proven that the angular-velocity estimation error and the lumped fault-disturbance estimation error can enter and remain in a small bounded neighborhood within a fixed time independent of the initial conditions. Moreover, the bounded disturbance jumps at the stage-transition instants are analyzed, and the post-transition recovery property of the estimator is established. The simulation results under a practical emergency rescue scenario show that the proposed estimator can reconstruct the main variation trends of the roll, pitch, and yaw channel lumped disturbances, keep the estimation error bounded, and recover after stage-transition-induced disturbance variations. Full article

Predictive control of pulse-width modulation (PWM)-driven electro-hydraulic proportional valves is challenging because PWM commands affect main-spool motion through pilot-spool dynamics and control-chamber pressure build-up. This study develops a compact pilot-opening-scheduled piecewise affine (PWA) predictor that distinguishes command-level PWM hysteresis from the internal pilot-opening modes. A guarded model-selection procedure, termed PhysGuard, compares a warm-start predictor with a bounded-refinement candidate on a short-horizon rollout record that is excluded from fitting and refinement. Under reset-horizon validation, the retained predictor yields displacement root-mean-square errors (RMSEs) of 0.382 mm and 0.596 mm at 80 ms and 120 ms, respectively. The retained predictor is then embedded in a solver-free kinematic dead-zone-crossing-scheduled event-triggered model predictive control (K-DCS-ET-MPC) law. At each complete-search event, the controller evaluates a finite set of pilot-spool references; an inner servo then converts the selected reference into PWM. Over the full 20 s Simulink benchmark, K-DCS-ET-MPC executes 1025 complete finite-set searches, compared with 5001 for time-triggered model predictive control (TT-MPC), giving a 79.5% reduction in search count. Over the common 5–10 s comparison interval, it achieves the lowest mean absolute error (MAE) and RMSE among nine simulated controllers (0.0371 mm and 0.0569 mm). In the same interval, it executes 373 complete searches, compared with 1251 for full-rate kinematic dead-zone-crossing-scheduled model predictive control (K-DCS-MPC), reducing the search count by 70.18%. Linear finite-set model predictive control produces the smallest maximum absolute error. In a nominal bench test with a sinusoidal reference of 5.5 mm amplitude, the MAE and RMSE averaged over three complete cycles are 0.2358 mm and 0.2781 mm, respectively. The robustness results are simulation-based, and complete-search timing is obtained from host-side replay. Full article

In the present paper, a reduced observer-bank synthesis and edge-oriented implementation framework for a model-based residual chlorine concentration soft sensor in water distribution networks is presented. For a benchmark network described through nonlinear hydraulic and chlorine transport–reaction dynamics, local discrete-time linear approximants are derived around admissible operating points. Based on these approximants, a finite bank of full-order Luenberger-type observers is designed for the estimation of nonmeasurable chlorine concentration variables. The observer parameters are selected through a metaheuristic multicriterion tuning procedure that combines discrete-time pole-placement requirements with estimation-performance objectives. A central contribution of the paper is an observer-bank construction algorithm that eliminates redundant target operating areas. The algorithm adaptively covers the normalized input domain by generating each observer operating area after nonlinear steady-state computation, local linearization, observer tuning, and validation for each operating point. The resulting observer bank is combined with a switching supervisor based on target-area overlap and measurable-output convergence. The resulting scheme is implemented in a Node-RED edge runtime, where multiple observers execute in parallel and the supervisor/switching mechanism selects the active observer that provides the appropriate estimate. Deterministic telemetry generated by a MATLAB R2025b simulation of the water distribution network is fed to the Node-RED instance, enabling repeatable evaluation of acquisition, estimation, switching, single-stream telemetry arbitration, and runtime adaptation. Computational and edge runtime experiments demonstrate the feasibility of deploying the proposed switching-observer soft sensor for real-time chlorine monitoring in water distribution networks. Full article

Accurate blood glucose prediction is essential for glycemic management in people with diabetes, but the size of many high-performing models complicates execution on resource-constrained artificial pancreas controllers. We propose GluKDnet, a lightweight glucose-forecasting model for prospective Android-smartphone-based mobile edge controllers. GluKDnet transfers the representational capacity of a time-series foundation model to a compact causal CNN through heterogeneous knowledge distillation. The teacher model, MOMENT, is adapted to continuous glucose monitoring (CGM) data through risk-event-aware masking, which prioritizes abnormal glucose levels, rapid glucose fluctuations, and CGM-defined dawn phenomenon and Somogyi effect patterns during masked reconstruction. A transient-state and steady-state distillation module jointly aligns ordered patch-level dynamics and day-level summaries between teacher and student. Using DLCP3 for teacher pretraining and leave-one-patient-out evaluation on OhioT1DM, GluKDnet achieves RMSE values of 20.04, 32.04, and 45.33 mg/dL for 30, 60, and 120 min prediction, respectively, with about 53K parameters. Auxiliary evaluation on T1D-UoM shows a similar offline accuracy–parameter count pattern. On a vivo V2072A Android smartphone, the 30 min model achieved a mean API inference latency of 0.470 ms (P95: 0.855 ms), a maximum sampled process proportional-set-size memory of 47.06 MiB, and a median incremental device energy estimate of 0.277 mJ per inference. These device measurements characterize the exported student model under one hardware and software configuration; insulin dosing and prospective closed-loop clinical evaluation remain outside the scope of this study. Full article

Multi-agent deep reinforcement learning (MARL) extends deep reinforcement learning (DRL) to environments involving multiple interacting agents and has enabled applications in domains such as autonomous vehicles, robotics, unmanned aerial vehicles (UAVs), and multi-player games. Compared with single-agent learning, MARL introduces additional challenges, including non-stationarity, partial observability, multi-agent credit assignment, and scalability. This paper presents a narrative survey of recent developments in MARL and discusses major approaches proposed to address these challenges. In particular, we examine research directions centred on centralised training with decentralised execution (CTDE), value decomposition, learned communication, graph-based methods, and model-based learning. We further discuss commonly used benchmark environments and evaluation practices, highlighting considerations related to reproducibility, robustness, and generalisation. Finally, we outline open research challenges and future directions concerning theoretical understanding, sample efficiency, scalable coordination, and deployment in real-world settings. Rather than providing an exhaustive systematic review, this survey aims to offer an organised and up-to-date synthesis of recent progress in MARL. Full article

This study evaluated the technical viability and thermodynamic performance of an open-loop upcycled heat pump dryer, repurposed from a decommissioned window-type air conditioner, for thin-layer red dragon fruit (Selenicereus costaricensis) drying. Experiments were executed at 50 °C under controlled air velocities (0.5, 1.0, and 1.5 m/s), using open-sun drying as a control. Mathematical modeling revealed that the Wang and Singh model best described the drying process ($R^2$: 0.996368–0.999539). Accounting for volumetric shrinkage via equivalent average thickness (L_eq = 0.75 L₀), corrected effective moisture diffusivity (D_eff) ranged from 1.194 × 10⁻⁹ to 3.138 × 10⁻⁹ m²/s, while convective mass transfer coefficients ($h_m$) ranged from 5.241 × 10⁻⁷ to 1.880 × 10⁻⁶ m/s, both peaking at 1.5 m/s due to thinned concentration boundary layers. Thermodynamic assessments showed a maximum COP_hp of 3.41 at 1.0 m/s and a maximum SMER of 0.231 kg/kWh at 1.5 m/s. Individual statistical analysis of L*, a*, and b* parameters confirmed no statistically significant differences (p > 0.05) between heat-pump-dried and fresh samples. Concurrently, a remarkably low descriptive total color difference (ΔE = 1.24) was obtained, compared to open-sun drying (ΔE = 16.22), confirming high color retention. These findings highlight e-waste upcycling as an efficient and sustainable agricultural solution. Full article

Background: The allocation of non-divisible inbound deliveries across multiple warehouses requires the simultaneous consideration of capacity constraints, category-specific restrictions, workload balance, and long-term allocation consistency. Methods: This study proposes a hierarchical two-level allocation framework combining strategic category-rotation policies, normalized marginal scoring, and signed historical feedback. The algorithm is executed once per day to generate warehouse assignments for the following operational day. Historical correction is based on a rolling window covering the preceding 30 daily planning periods. Results: The framework was evaluated using daily simulation instances ranging from 100 to 1500 pallets, with an average of approximately 130 lots per pallet. Across all evaluated instances, the complete allocation procedure was completed in less than 5 s on the specified test system. The results indicate balanced warehouse utilization, progressive reductions in category–location imbalance, stable historical correction, and preservation of hard operational constraints. Conclusions: The framework provides an interpretable and computationally efficient approach for next-day inbound allocation. By combining explicit feasibility filtering, strategic policy signals, and a 30-day historical correction mechanism, it supports both short-term operational decisions and longer-term allocation balance. Full article

Modern embedded systems are vulnerable to complex data-oriented attacks that subvert not only existing control-flow integrity protections but also have dire implications on safety-critical applications. To overcome this challenge, this work proposes a secure RISC-based architecture that incorporates hardware-assisted Data-Flow Integrity (DFI) enforcement into the processor pipeline. The architecture uses tag-based metadata propagation, a Security Monitor Unit, and compiler support for static analysis in order to ensure the validity of dynamic data flows without affecting execution. A complete RTL prototype was designed and tested with MiBench and CoreMark. Results show an average performance overhead of 11.3% and a logic utilization increase of <9.2% on FPGAs, while achieving 100% detection of all attempted pointer corruption and data tampering attacks. These results indicate the efficacy of the lightweight security-aware RISC architecture as a feasible compromise between security, performance, and hardware expense in today’s embedded systems. Full article

An invariant method is proposed for determining the expediency of implementing the structure and organization of an information system (IS). The method is based on determining time losses occurring during the execution of information processes and on assessing the functional reliability of the system with respect to the performed tasks, taking into account the reliability characteristics of the corresponding IS components. This study addresses the importance of the problem from both theoretical and applied perspectives, as well as the need for its solution under conditions of a limited number of methods described in the literature for assessing the influence of hardware and software reliability of information system elements. Full article

English-teaching agents use external knowledge retrieval to update instructional content, broaden domain coverage, and personalize support beyond standalone large language models (LLMs). However, open sources may introduce harmful, biased, or misleading content into retrieval-augmented generation (RAG) pipelines, affecting learners’ judgment, cultural understanding, and value formation. To address this problem, this study proposes a multi-stage secure knowledge retrieval method for English-teaching agents. The method coordinates safeguards across knowledge-source access, retrieval execution, and model output. At the access stage, custom rules and Semgrep-based static scanning perform preliminary risk screening. At the retrieval stage, LLM-based dynamic evaluation identifies tool-description contamination and cross-file data-flow risks. At the output stage, semantic-embedding pre-screening, LLM review, and bounded knowledge purification detect and rewrite risky responses. Our experiments use public safety benchmarks, a mixed corpus of benign and poisoned passages, synthetic purification cases, and controlled end-to-end teaching scenarios. Compared with vanilla RAG, the framework reduces Poison Exposure@5 from 92.0% to 3.0% and retrieval attack success from 86.0% to 2.0% while preserving retrieval coverage. These results provide preliminary evidence that the framework can empower English teaching by enabling agents to deliver safer materials and trustworthy support for classroom questioning, academic writing, and intercultural learning. Full article

Amid the profound restructuring of global value chains, supply chain risk has mainly been linked to visible shocks such as pandemics, wars, and geopolitical conflict. Much less is known, however, about whether exchange rate volatility can become a source of operational instability within firms. We examine this question using Chinese A-share listed firms from 2007 to 2021. We construct an industry-level exchange rate volatility measure by combining ADB input–output tables with bilateral real exchange rate volatility, and measure firms’ perceived and disclosed supply chain disruption risk from the MD&A sections of annual reports using a word-embedding approach. We find that higher industry-level exchange rate volatility is associated with a significant increase in firms’ perceived and disclosed supply chain disruption risk. The mechanism evidence indicates that this effect operates through both supply-side operating frictions and demand-side pressure: higher industry-level exchange rate volatility reduces inventory turnover and weakens overseas revenue realization. The effect is weaker in industries with longer backward production length but stronger among firms facing tighter financing constraints. It is also stronger among firms located in more open regions, firms with overseas-experienced executives, and firms with greater export intensity, but weaker among manufacturing firms. These findings extend research on the real effects of exchange rate volatility by showing how industry-level exchange rate uncertainty can materialize as firm-level perceived and disclosed supply chain disruption risk and undermine the operational continuity and long-term economic sustainability of internationally connected supply chains. Full article

Studies suggest that anticipation in sports-related tasks is better if the task maintains the coupling of perception and action required in competition. Study 1 addressed the question of whether gaze tracking varies between coupled tasks in baseball and softball batters. Fifteen subjects executed either a coupled task (partially swinging a bat at an approaching ball) or an uncoupled task (predicting the passing height of an approaching ball). The ball was stopped by a net 8 feet (2.44 m) in front of the subject. Rapid gaze shifts occurred for 53.0% of the pitches in the uncoupled task, and for 39.7% of the pitches in the coupled task (p = 0.039). The number of predictive gaze shifts toward the terminal ball location in each condition (i.e., the expected location of bat–ball contact in the coupled condition, and the observer in the uncoupled condition) did not vary between the conditions (p = 0.388). Study 2 examined whether gaze fixations on the net found in Study 1 occurred because passing height judgments could not be improved at distances closer than 8 feet from the observer. Passing height judgments were lower when the ball was allowed to pass by the observer, suggesting that subjects processed trajectory information within 8 feet. Full article

Two-Phase TMR conserves energy by partitioning redundancy operations into two stages and making the execution of the third task copy optional, yet it remains susceptible to permanent faults. Reactive TMR (R-TMR) counters this by isolating faulty cores, handling both transient and permanent faults. However, the lightweight hardware required by R-TMR not only increases complexity but also becomes a single point of failure itself. To bypass isolated node constraints, this paper proposes a Fault Tolerance and Isolation TMR (FTI-TMR) algorithm for interconnected multicore systems. We construct a stability metric characterized by its prior and posterior estimates to identify the most reliable nodes in the system. These nodes then perform periodic diagnostics to isolate permanent faults. The experimental results show that FTI-TMR reduces task workload by approximately 30% compared with baseline TMR, while achieving higher permanent-fault coverage. Full article

Industrial microgrids are emerging as important providers of demand-side flexibility for active distribution networks. However, their participation in source–load coordination is hindered by complex production constraints, limited dispatch executability, and the widespread reliance on baseline-based demand response mechanisms. To address these challenges, this paper proposes a baseline-free source–load coordination framework for industrial microgrid clusters. A linear state–task network (LSTN) model is employed to characterize industrial production processes while preserving equipment operation, material balance, buffer storage, and production target constraints. Based on the feasible operating regions of individual microgrids, a simplified optimal adjustable load model (OALM) is developed at the aggregator level to identify and aggregate cluster-level flexibility boundaries without disclosing detailed production information. Building upon the aggregated flexibility region, a baseline-free target power tracking strategy is established, in which the distribution network issues absolute power targets and the aggregator coordinates multiple industrial microgrids to achieve their realization. The proposed framework simultaneously ensures production feasibility, scalable flexibility aggregation, and practical dispatch implementation. Case studies demonstrate that the aggregated industrial load can accurately track dispatch targets while satisfying all production constraints, thereby enhancing the capability of industrial microgrid clusters to participate in large-scale source–load interaction and renewable energy accommodation. Case study results show that when the number of industrial microgrids increases from 10 to 200, the total computation time increases from 5.08 s to 337.40 s, while the target tracking error remains within numerical tolerance. Compared with the conventional baseline-based virtual-battery (VB) method, whose root mean square error (RMSE) with respect to the intended absolute target reaches 241.30 kW under a ±10% baseline estimation error, the proposed method achieves near-zero tracking error. In addition, the production-agnostic aggregation model expands the flexibility boundary by 21.84% and generates targets that are not exactly executable under the full LSTN production constraints. Full article