Search Results (398)

Pneumatic artificial muscles, including McKibben-type actuators, offer high power-to-weight ratio, compliance, and inherent safety, but their nonlinear pressure–contraction behavior, hysteresis, saturation, and load-dependent dynamics make accurate position control challenging. This study develops a practical data-driven workflow that derives a branchwise feedforward compensator and an LQI or PID controller from one open-loop characterization experiment. Quasi-static characterization first identifies a conservative control-ready voltage window. A bounded random excitation within this window is replayed with $4\mathrm{s}$ holds to expose terminal and transient behavior. The same experiment supplies branchwise discrete plant models and a feedforward lookup. Two open-loop-derived transient layers, voltage creep compensation and dynamic pressure referencing, are applied to the raw lookup before simulation. Four controller variants are compared on a common simulated closed-loop benchmark built from the identified plant: a feedforward-only baseline, a branchwise proportional–integral–derivative (PID) baseline, a base linear quadratic integral (LQI) controller with displacement and pressure feedback, and a velocity-state LQI extension with a filtered velocity estimate. A multi-metric optimization score balances tracking RMS, settled oscillation, command activity, saturation, and gain magnitude. The score selects the base LQI within the LQI family. The selected gains and transient layers are deployed in a real-time implementation with manually reduced position gains. The controllers are then evaluated on a common reference stream against the physical actuator. Although simulation metrics cannot be transferred directly to the real system, the combined-metric ranking of the controllers remains unchanged. Full article

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead. Full article

Pedestrian-detection research and pre-crash safety assessment predominantly represent upright pedestrians, although prone, supine, lateral, seated, crouched, kneeling, partially collapsed, and fall-transition states alter target geometry, visibility, sensor signatures, and intervention time. Cross-study comparison is further limited by inconsistent posture labels, data provenance, latency boundaries, uncertainty reporting, and vehicle-response assumptions. We developed NUP-REPORT 1.0 as a provisional reporting and benchmarking framework through a structured narrative synthesis of a 45-source derivation corpus covering epidemiology, sensing benchmarks, uncertainty and assurance methods, reporting-guideline methodology, and public safety protocols. A reconstructed decision ledger documented 46 candidate concepts: 30 were retained as checklist items, 10 were assigned to an extended descriptor set, and six were merged. Each retained item was mapped to supporting evidence and classified as universal core (n = 19), component-contingent core (n = 4), or conditional (n = 7). The framework comprises six domains, a scenario-coverage matrix, five non-overlapping event timestamps, detection-referenced stopping equations, and a 30-item checklist. A purposive feasibility audit of 20 publications, including the adjacent pedestrian-detection literature not designed specifically for non-upright evaluation, illustrated checklist use. Within this sample, target orientation and static-versus-transition state were each reported explicitly in five of 20 publications (25%); none of the 17 applicable papers reported both time-to-first-detection and detection distance, none evaluated confidence calibration, and none of the 20 reported independent-unit uncertainty intervals. Vehicle-response items were non-applicable to papers making no intervention claim. These observations are sample-specific and do not estimate field-wide reporting prevalence. NUP-REPORT is not a consensus standard, certification procedure, or safety score; it is a traceable Version 1.0 proposal for study design, retrospective audit, and stakeholder refinement. Full article

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions. Full article

Small gases pose an unusual problem for studies of molecular transport in proteins. O₂, CO, H₂, and NO can cross short-lived internal spaces opened by protein fluctuations, often faster than experiments can follow continuous migration. Time-resolved crystallography can localize sufficiently populated intermediates, whereas spectroscopy, isotope exchange, and kinetic measurements report molecular exchange over their respective timescales without resolving the complete route. Pressurized noble-gas structures expose internal accommodation sites but rely on surrogate molecules whose size and interactions differ from those of physiological gases. Geometry-based tunnel searches identify available space, while molecular dynamics follows explicit movement through a fluctuating protein. Free-energy and enhanced-sampling approaches can access states or transitions that remain undersampled in direct trajectories. These techniques resolve different quantities rather than progressively more accurate estimates of gas transport. In this Perspective, we argue that gas-migration pathways should be evaluated by the physical consistency of independent observables, with each method interpreted according to the quantity it resolves. This distinction explains why a cavity visible crystallographically may not carry substantial flux, why a rapidly crossed route can remain structurally inconspicuous, and why static narrowing can alter diffusion without predicting its magnitude. Agreement among methods can support a transport assignment when the quantities they resolve are physically consistent with the same mechanism; apparent disagreement may instead reflect differences among occupancy, accessibility, residence, energetic preference, and molecular traffic. Full article

This paper examines directional quantile dependence from green bonds, clean energy markets, and carbon markets to the return states of five literature-classified green cryptocurrencies. Using daily returns from 25 September 2019 to 23 May 2025, we estimate static cross-quantilograms on a $19\times 19$ quantile grid at lags 1, 5, and 22, 500-observation rolling cross-quantilograms, bootstrap surface tests, and green-specificity comparisons with five cryptocurrencies that used proof-of-work (PoW) consensus throughout the comparison sample. Point estimates display heterogeneous short-run patterns in selected green-bond and carbon-market pairs, but none of the 45 forward surfaces rejects the omnibus null at the 5% level. Rolling estimates vary across windows and tail cutoffs. The largest raw green-group contrast occurs for carbon quota prices at lag five, although its time-series bootstrap interval includes zero and factor-adjusted tests do not detect systematic green-minus-PoW separation. Descriptive quantile-on-quantile connectedness estimates are higher at extreme quantiles than at the median–median state. Overall, the evidence is more consistent with broad cryptocurrency-market conditions than with a uniform dependence pattern associated with the environmental label. Full article

Autonomous surface vessels (ASVs) operating in narrow and restricted waterways must follow a planned path while avoiding nearby static hazards and maintaining safe clearance from boundaries. This paper presents a LiDAR-based deep reinforcement learning framework for path following and static obstacle avoidance of an underactuated ASV. The vessel receives local pose information from a localization system and surrounding environment through a 2D LiDAR scan, which is converted into compact sector features using feasibility-inspired pooling method. A Soft Actor-Critic (SAC) policy is trained in simulation to output continuous rudder and propulsion commands, based on LiDAR features, estimated motion states, and path-relative errors. The policy is evaluated over 500 randomized simulation episodes ranging from 0–4 obstacles. The trained policy achieved an overall success rate of 95.0%, with an average cross-track error of 0.66 m. Obstacle and border collision rates are 3.80% and 1.20%, respectively; indicating that the policy can perform path tracking and collision avoidance in constrained layouts. A single field trial was then conducted in each of three fixed obstacle layouts using the model-scale Bluefin vessel. In these trials the policy executed on the physical platform and avoided static obstacles, with minimum obstacle clearances of 0.50–1.17 m. However, the field trajectories exhibit larger oscillations and longer path lengths than simulation, with an average RMS cross-track error of 1.14 m compared to 0.69 m in simulation. Full article

Human body static electricity is one of the major hazards in electrostatic-sensitive industrial environments. Existing noncontact measurement methods often rely on explicit physical modeling and generally require accurate prior knowledge of the sensor layout, target motion state, and scene geometry, which limits their applicability in complex dynamic scenarios. To address this issue, this paper proposes a neural network-based data-driven method for estimating human body electrostatic potential from noncontact electrostatic measurements. The proposed method uses four-channel noncontact electrostatic sensor signals as inputs and the synchronously measured reference body potential as the target output. Sixteen neural network architectures, including recurrent neural networks, convolutional neural networks, attention-based networks, and hybrid models, are systematically evaluated. The Gray Wolf Optimizer is further used to optimize key hyperparameters of each model. A 5 m × 5 m experimental scene is established, and 36 groups of synchronized time-series signals are collected from three subjects under two motion states. Training and validation datasets are constructed using a sliding time-window method, and the effects of model architecture, window length, and subject–motion condition on inversion performance are analyzed. The results show that the proposed method can effectively estimate human body electrostatic potential from noncontact measurements. Among the evaluated models, Trans-LSTM achieves the best performance. With a window length of 1 s, it obtains a validation normalized root-mean-square error of approximately 0.139 and a coefficient of determination of approximately 0.72. Compared with a representative existing method under the same coverage area and sensor layout, the proposed approach reduces the NRMSE from 0.22 to 0.139. These results demonstrate that the proposed method provides a feasible approach for remote, real-time, and noncontact monitoring of human body electrostatic potential in complex environments. Full article

Short-term traffic-flow forecasting predicts road-network states, yet spatio-temporal predictors can leave structured, context-dependent residuals. They are seldom reused at inference, while similarity-based transfer can introduce corrections that increase error. We propose Memory-Based Utility-Aligned Residual Evidence with Calibrated Activation for Selective Correction of Traffic-Flow Forecasts (MURECAST), which treats historical residuals as candidate interventions. After freezing a context-aware base forecaster, MURECAST builds a static out-of-sample residual memory from an independent period. At inference, same-node and time-valid constraints delimit records, a forecast-visible utility estimator re-ranks them, and utility-weighted top-K aggregation forms a multi-horizon proposal. A chronological calibration split provides an empirical one-sided lower score for applying the proposal or retaining the base forecast. Across PeMS03, PeMS04, PeMS07, and PeMS08, MURECAST ranked first in 11 of 12 reported dataset–metric comparisons, attaining the lowest mean absolute error (MAE) and root mean squared error (RMSE) on all four datasets and the lowest mean absolute percentage error (MAPE) on three; relative error reductions over the strongest published results were 1.69–9.76%. Non-beneficial corrections represented 17–26% of accepted proposals versus 41–48% of all valid proposals. These results show that MURECAST reuses observed errors while concentrating intervention on corrections with lower observed non-beneficial risk under the evaluated chronological protocol. Full article

Static forecast models used by gas distribution operators in regulatory submissions provide point estimates but cannot quantify the probability that approved targets will be met. This paper proposes an Extended Kalman Filter–Monte Carlo (EKF–MC) framework that (i) jointly estimates the residential client state and the parameters of the logistic S-curve growth model in a sequential Bayesian setting, and (ii) propagates the posterior parameter uncertainty through 100,000 Monte Carlo draws to construct calibrated probability distributions for the 2026–2027 regulatory forecast horizon. Applied to five years of regulatory submission data (2021–2025) for a licensed gas distribution operator in Sofia Province, Bulgaria, the EKF refines the saturation ceiling to M = 1995 ± 30 and the growth rate to r = 0.561 ± 0.083. The Monte Carlo analysis yields 90% forecast intervals of [1930–2002] for 2026 and [1939–2015] for 2027. Both intervals lie entirely below the regulator-approved targets (2039 and 2150), demonstrating a structural over-forecasting tendency in the regulatory approval process. The framework provides operators with a computationally efficient, auditable tool for quantifying forecast uncertainty in rate-case submissions and capital investment planning. Full article

Accurate demand forecasting for perishable goods is complicated by demand censoring: when stockouts occur, observed sales understate true demand and bias both forecasts and replenishment decisions. We present a leakage-free, end-to-end pipeline of three stages: an inverse-Mills-ratio (IMR) recovery heuristic benchmarked against a maximum-likelihood Tobit estimator; a LightGBM ensemble on 117 engineered features with train-only aggregate statistics and recursive multi-step inference; and 20 Newsvendor-family policies, the reported one fixed on a validation fold under a fill-rate floor by a rule encoded in the released code, then evaluated once on a held-out window. On FreshRetailNet-50K (50,000 store-product series, 90 days of daily sales with hourly stock-status vectors), the ensemble attains 33.54% WAPE before recentering; the validation-selected CPU-only calibration improves this to 32.79% and reduces forecast bias from $-8.23\%$ to $-2.62\%$. All headline figures come from one fold-causal run: the selected Q90 Direct policy reaches a 0.956 fill rate and 0.241 profit per store-product day, a 46% gain over the static baseline (store-clustered bootstrap 95% CI on the absolute difference, $[0.072,0.081]$), while meeting the 0.90 service floor. Because evaluation demand is censored observed sales, all profit figures are proxies whose direction of bias relative to latent-demand profit is not identified. Ablations attribute most of the measured gain to the multi-objective ensemble and the service-constrained policy; the full feature set mainly improves bias and stability, and recovery affects bias more than WAPE. The contribution is this leakage-free evaluation protocol and the CPU-feasible operational baseline it supports, released with its leakage tests and clustered-bootstrap inference, rather than a new state-of-the-art WAPE on this benchmark. Full article

Digital salary advance and earned wage access platforms require intelligent monitoring mechanisms capable of identifying behavioral anomalies, estimating operational risk, and preserving auditable transaction evidence. Existing financial anomaly detection approaches commonly rely on isolated predictive models, static anomaly thresholds, or rule-based monitoring, providing limited support for behavior-aware enterprise monitoring. This study proposes a behavioral drift-aware adaptive anomaly fusion framework for explainable risk monitoring in salary advance FinTech systems. The framework introduces the Behavioral Risk Deviation Index (BRDI), which integrates temporal irregularity, behavioral entropy, transaction velocity, burst activity, and historical behavioral drift into a unified behavioral state representation. BRDI adaptively regulates the fusion of Isolation Forest-based statistical anomaly scores and reconstruction-based nonlinear anomaly scores according to each employee’s behavioral profile. The framework further combines calibrated ensemble risk scoring, SHAP-based explainability, and blockchain-assisted audit verification using SHA-256 hashing and Merkle-root validation. Experimental evaluation on 52,846 anonymized salary advance transactions demonstrates that the framework supports behavior-aware anomaly detection, interpretable risk prioritization, and trustworthy audit traceability in enterprise financial environments. The proposed approach contributes a unified behavioral state representation, a deterministic adaptive anomaly fusion mechanism, and an integrated architecture for trustworthy AI-assisted salary advance monitoring. Full article

Predicting target intent in complex, dynamic multi-agent environments remains a formidable challenge due to incomplete perception and the highly dynamic nature of multi-target interactions. Conventional approaches—such as D-S evidence theory, expert systems, and Recurrent Neural Networks (RNNs)—are often constrained by data incompleteness and rigid behavioral assumptions, limiting their adaptability to dynamic high-value target identification and multi-target situational awareness on the ground. To address these challenges, a novel framework termed Threat Field–Gated Recurrent Unit (TF-GRU) is proposed. The TF-GRU framework integrates threat field modeling with a dynamic repair mechanism to enhance intent prediction under partial perception. Specifically, threat field modeling associates target attributes with intentions through the construction of static and dynamic threat fields, effectively capturing the temporal and semantic relationships among multiple targets. A particle filtering and dynamic time warping fusion strategy (PF-DTW) is employed to repair data gaps via short-term filtering and long-term trajectory matching, further refined by a neighborhood-angle constraint for accurate multi-target state estimation. In addition, trajectory and threat field features are processed using a Mish activation function and a threat-adaptive gating mechanism, which dynamically regulate information flow within the recurrent unit to model behavioral evolution. Experimental evaluations demonstrate that TF-GRU significantly enhances intent prediction accuracy under incomplete data conditions, thereby improving comprehensive situational awareness and supporting high-confidence decision-making in dynamic multi-target scenarios. Full article

Multi-energy virtual power plants (VPPs) aggregate electricity, heating, and natural gas resources to provide flexible regulation services to the external power grid. Their operational flexibility, however, cannot be accurately characterized using equipment capacities or single-period energy balances alone, because district heating and natural gas networks introduce heat transport delays, pipeline thermal storage, pressure dynamics, and linepack effects. This paper proposes an operational flexibility boundary assessment method for electricity–heating–gas VPPs based on a dynamic energy circuit model (ECM). The frequency-domain ECM converts heating-network temperature dynamics and gas-network pressure dynamics into algebraic constraints, which are integrated with electric-network and multi-energy coupling-device constraints. The net exchange power at the point of common coupling (PCC) is used as the external flexibility interface, and the period-wise upper and lower boundaries are determined subject to network and device constraints, terminal-state recovery requirements, and an economic feasibility limit. Case studies on an electricity–heating–gas VPP demonstrate that the dynamic ECM captures the intertemporal regulation capability provided by pipeline thermal storage and gas-network linepack. Compared with the static model, the dynamic ECM exhibits consistently greater downward flexibility and comparable or lower upward flexibility in several periods, thereby correcting the underestimation of electrical absorption capability and the optimistic estimation of power-export capability caused by the static approximation. The economic feasibility constraint further excludes high-cost boundary schedules, yielding a technically feasible and economically acceptable flexibility range. Full article

Bitcoin option prices reflect terminal variance and the cost of managing convex exposure in a market with changing depth and execution quality. This paper asks whether a liquidity state can be separated from fractional rough volatility in Bitcoin option valuation. The contribution is a modelling combination: standard stochastic-calculus and rough-volatility tools are joined to a regime-switching hedging-cost reserve, producing a leading-order at-the-money implied-volatility lift. The empirical design tests a liquidity–IV association and its scale using a 700-contract Deribit snapshot, a 4513-trade 24-h window spanning two UTC dates, a 775,315-trade panel over 92 dates, Ether replication, and placebos. The association is strong in open-interest-weighted specifications and for puts, but is absent for calls; it remains after controlling for option premium. Leave-one-expiry-out validation improves open-interest-weighted RMSE but not unweighted RMSE. A realised-volatility HMM is only a market-stress diagnostic, not an estimated liquidity regime. An empirical one-step hedging exercise does not validate the model’s simulated hedging comparative static. Accordingly, the evidence is associational, put-side, and narrower than a causal or fully structural validation. Full article