Search Results (243)

Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed covariance matrices, which limits their robustness under degraded measurement conditions. This study proposes a Student-t robust quantum-behaved particle swarm optimization-based extended Kalman filter (ST-QPSO-EKF) for adaptive GPS state estimation. The proposed framework combines quantum-behaved particle swarm optimization (QPSO) with a Student-t-based robust measurement update, where the process-noise scaling factor, measurement-noise scaling factor, and Student-t degrees-of-freedom parameter are jointly optimized. The optimized parameters are obtained through an offline calibration stage and subsequently applied in the recursive GPS filtering process. A nonlinear GPS navigation simulation was conducted using Gaussian, Student-t heavy-tailed, and outlier-contaminated pseudorange measurement scenarios. The proposed method was compared with conventional EKF, QPSO-EKF, and Student-t EKF using 20 independent Monte Carlo realizations. The results demonstrate that QPSO-EKF provides improved accuracy under nominal Gaussian conditions, whereas ST-QPSO-EKF achieves superior performance under non-Gaussian measurement environments. Under Student-t heavy-tailed noise, ST-QPSO-EKF reduced the position RMSE to 3.814 m, while under outlier-contaminated noise it achieved a position RMSE of 3.952 m, outperforming the other compared methods. In addition, the proposed method maintained comparable online computational cost because the QPSO optimization was performed offline. The results indicate that jointly optimizing covariance parameters and Student-t robustness provides an effective strategy for improving GPS positioning reliability under complex pseudorange measurement conditions. Full article

Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50–70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50–70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation. Full article

Determining the parameters of a manipulator for optimal performance is a challenging task. This is primarily due to possible conflicting objectives, various tasks that should be considered, and the highly non-linear behavior that is involved. This work proposes an enhanced version of the concept-based design space exploration (C-DSE) approach for the design of manipulators. According to the C-DSE approach, prior to the search, the designers divide the set of feasible solutions into meaningful subsets, which are termed concepts. The design space exploration involves a simultaneous search for optimal solutions within each of the pre-defined concepts. This enhanced framework integrates the following: (1) kinematics, dynamics, and control co-design, and the simultaneous optimization of manipulator morphology and controller parameters; (2) surrogate-assisted optimization using Gaussian process (GP) and neural network (NN) models to reduce computational cost; (3) approximately 30 performance metrics spanning kinematic, dynamic, structural, control, and task performance domains; (4) task-aware feasibility verification applying a multi-level hierarchy; (5) a generative AI integration pathway using diffusion models and LLM-guided concept generation (proposed in this preliminary investigation). The results demonstrate a 95.7% reduction in high-fidelity function evaluations (50,000 to 2150), corresponding to a 23.3 times reduction in evaluation count and a 6.6 times reduction in wall-clock computation time (25 h to 3.8 h). Co-design yields up to a 35% improvement in energy efficiency and a 28% reduction in tracking error compared to sequential morphology-only optimization. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

Groundwater-storage anomalies (GWSA) derived from the Gravity Recovery and Climate Experiment (GRACE) mission provide valuable information for regional groundwater monitoring. Improving the spatial representation and temporal continuity of GRACE-derived GWSA is important for supporting groundwater assessment at subregional scales. A sequential framework combining Gaussian Process (GP) temporal gap filling and Random Forest (RF) spatial downscaling was developed for GWSA reconstruction in Henan Province, China, during 2002–2022. The GP model was used to reconstruct missing observations and characterize temporal variations, while the RF model statistically redistributed the GRACE-based GWSA signal using multi-source hydroclimatic predictors. The resulting dataset comprises model-derived GWSA estimates on a 1 km output grid constrained by the coarse spatial support of GRACE observations and the relationships learned from the auxiliary variables. Therefore, the 1 km grid spacing should not be interpreted as an independent 1 km resolving capability for groundwater-storage variations. Agreement with the parent GRACE-based GWSA product was used to assess coarse-scale reconstruction consistency rather than independent fine-scale accuracy. Comparison with groundwater-level anomalies from 63 monitoring wells yielded a correlation coefficient of 0.88, indicating temporal agreement at the sampled locations. Because the groundwater-level observations were not converted into storage anomalies using specific yield, this comparison does not establish absolute GWSA accuracy or independently validate the model-derived fine-scale spatial patterns. The reconstructed estimates revealed pronounced spatial heterogeneity in groundwater-storage changes, with persistent depletion concentrated in northern Henan, where groundwater decline rates exceeded 20 mm yr⁻¹. Overall, the framework improved the temporal continuity and spatial representation of GRACE-based groundwater-storage estimates while retaining the fundamental spatial constraints of satellite gravimetry. The results demonstrate the potential of integrating GRACE observations, machine learning, and multi-source Earth observation data to support regional groundwater assessment. Full article

Temperature–time profiles obtained through thermistor-based monitoring provide a rich but noise-sensitive source of information for estimating kinetic and thermal parameters in exothermic batch reactions. Conventional workflows typically combine deterministic smoothing with numerical differentiation, an approach that can amplify measurement noise and fail to propagate preprocessing uncertainty into the resulting reaction-rate and parameter estimates. To address these limitations, this study presents an uncertainty-aware Bayesian machine-learning framework that integrates scalable random-Fourier-feature Gaussian-process (RFF–GP) smoothing, analytical differentiation, temperature-derived apparent conversion, Bayesian parameter inference, posterior validation, predictive calibration, model comparison, ablation, sensitivity analysis, probabilistic benchmarking, simulation of thermal nonideality, and endpoint diagnostics. The framework was applied to $379,631$ cleaned thermistor observations. The production RFF–GP achieved a validation root-mean-square error of $0.04805\mathrm{K}$, yielding a stable latent temperature trajectory and an uncertainty-aware estimate of $dT/dt$. On a smaller matched subset, exact Gaussian-process regression achieved the highest predictive accuracy and the best probabilistic scores, whereas the RFF–GP reduced central-processing-unit runtime by approximately $4.1$-fold and remained applicable to the larger production fit. A Monte Carlo dropout neural comparator produced larger prediction errors and substantially wider predictive intervals. Six apparent thermokinetic structures were evaluated using mean-field variational inference, after which the nth-order and autocatalytic structures were validated using the No-U-Turn Sampler (NUTS). Under mean-field variational inference, the apparent autocatalytic structure achieved the lowest point estimate of the widely applicable information criterion (WAIC), the lowest derivative-domain error, and the lowest full-profile temperature-reconstruction root-mean-square error of $0.2920\mathrm{K}$. Its posterior obtained using NUTS yielded $E_a=40.98\mathrm{kJ}{\mathrm{mol}}^{-1}$, $k_{\mathrm{ref}}=0.005815{\min }^{-1}$, $\Delta T_{\mathrm{ad}}=56.11\mathrm{K}$, $m=0.1694$, and $n=1.0784$. The sampling diagnostics indicated satisfactory convergence, large effective sample sizes, and no divergent transitions. Although the MFVI posterior means and NUTS posterior medians were similar, variational inference produced narrower uncertainty intervals for several correlated parameters. Moving-block bootstrap intervals did not establish a decisive separation in WAIC among the leading structures. Expanded sensitivity, ablation, imperfect-insulation simulation, and endpoint-holdout analyses further showed that the apparent parameter estimates were sensitive to optimization, thermal nonideality, sensor response, and Gaussian-process boundary behavior. The autocatalytic formulation should therefore be interpreted as the best-performing apparent structure among the candidates tested rather than as evidence of a unique chemical mechanism. Overall, the framework extracted physically plausible apparent thermokinetic information from noisy temperature-only measurements while explicitly quantifying uncertainty arising from prediction, parameter estimation, model form, computation, thermal nonideality, and boundary behavior. Full article

Crop straw-to-grain ratio (SGR) estimation underpins regional straw resource assessment, yet national inventories rely on fixed coefficients that ignore structured variation across variety, environment, and phenotype. We introduce a Structured Multi-Kernel Heteroscedastic Gaussian Process (GP) framework that models SGR variation through three additive kernels heuristically motivated by the genotype–environment–phenotype (G+E+P) framework—capturing variety-associated variation, spatially structured variation, and environmental and management covariates—and employs an input-dependent noise model for prediction-specific uncertainty quantification. To prevent information leakage, target encoding and feature scaling are recomputed within each cross-validation fold. Evaluated via internal leave-one-out cross-validation on 80 rice samples (42 varieties, six Chinese provinces), the model achieves $R^2=0.541$ with a prediction interval coverage probability of 0.95. Ablation identifies variety-associated variation as the largest contributor among the modeled factors ($\Delta R^2=-0.024$) and the multi-kernel design, by incorporating variety-specific information, substantially improves upon a covariate-only RBF GP ($\Delta R^2=0.103$). On point-prediction accuracy, Gradient Boosting achieves $R^2=0.58$, slightly ahead of the Heteroscedastic GP ($R^2=0.54$), underscoring that the primary advantage of the GP lies in its input-dependent uncertainty quantification. However, leave-one-county-out validation yields $R^2\approx 0$ (with $\sigma$ escalating to 24.4), confirming that the model does not yet generalize to unsampled counties; all reported performance is therefore internal to the nine sampled counties. The framework couples an agronomically motivated additive kernel structure with input-dependent uncertainty quantification, offering a path toward uncertainty-aware prediction from small field datasets. Full article

This study investigates adhesively bonded composite patch repair to enhance the load-carrying capacity of damaged metallic structures, introducing a novel FE-augmented machine learning (ML) framework that addresses the limited availability of experimental data in structural repair applications. To evaluate this approach, aluminum and steel specimens with central fatigue cracks were repaired using glass-fiber/epoxy and carbon-fiber/epoxy composite patches and tested under quasi-static loading at room ($70 \mathrm{F} °$), high ($145 \mathrm{F} °$), and low (−$60 \mathrm{F} °$) temperatures. Finite element (FE) models were then developed in ABAQUS© to predict the failure loads of the patched specimens under varying temperature conditions, showing excellent agreement with the experimental data. The high accuracy of the FE predictions enabled their use as additional training data, effectively augmenting the limited experimental dataset and allowing the development of more robust regression models. Ten machine learning (ML) regression models, including linear regression (LR), polynomial regression (PR), support vector regression (SVR), random forest (RF), gradient boosting (GB), XGBoost (XGB), LightGBM (LGBM), Gaussian process (GP) regression, artificial neural networks (ANNs), and Kolmogorov–Arnold networks (KANs), were trained to predict the failure load of both unpatched and patched specimens as a function of material type, temperature, specimen thickness, crack length, and, for patched specimens, patch type and thickness. The datasets combined a limited set of physical results (75 patched samples: 63 experimental and 12 finite-element; 72 unpatched samples: 27 experimental and 45 theoretical) with Gaussian-mixture-model synthetic samples used only to augment the training data up to 300 samples per case. Under a configuration-grouped, leakage-free nested cross-validation (entire configurations held out for testing, hyperparameters tuned on inner folds only), the best models predicted the failure load of unseen configurations with mean absolute percentage errors of 2.78% (Gradient Boosting, patched, $R^2=0.87$) and 3.33% (Gaussian Process, unpatched, $R^2=0.98$). A paired ablation showed that Gaussian-mixture-model augmentation did not improve accuracy and, for several models, actually reduced it; the final models therefore rely on the real multi-source (experimental, FE, and theoretical) data, with the synthetic pipeline reported as a validated but non-beneficial component for these datasets. Overall, this study provides a novel, data-efficient framework combining experimental testing, FE simulation, and validated regression modeling to predict the performance of adhesively bonded composite patch repairs under varying thermal and mechanical conditions. Full article

Long-endurance unmanned aerial vehicle (UAV) missions beyond 72 consecutive flight hours face a reliability boundary at which thermal gyroscope drift drives the inertial navigation system (INS) position error into rapid nonlinear divergence at a predictable threshold t_c. This paper presents BAZ II, a simulation-validated multi-agent navigation system that extends the analytical BAZ (bifurcation-aware zonal navigation) framework. Its central idea is to treat communication quality as a planning resource and combine it with multi-agent collaboration, making the navigation cliff a manageable degradation event rather than a hard operating limit. Four contributions support this idea: a thermalhysteresis MEMS gyroscope drift model reproduces the analytical cliff in simulation and supplies its physical mechanism; a distributed collaborative simultaneous localization and mapping (SLAM) filter coupled to a stochastic continuous-time Markov chain (CTMC) interagent channel sustains GPS-denied localization within the operational accuracy budget; a 3D Gaussian process RF-aware model predictive controller (MPC) with cognitive radio frequency-hopping restores link availability under jamming, while an analytic hierarchy process (AHP)-weighted multi-objective communication cost improves latency and jitter at negligible signal-to-noise ratio cost; finally, the integrated controller executes within the onboard real-time budget of an NVIDIA Jetson Xavier NX. All results are obtained in simulation, with hardware-in-the-loop and field testing remaining as priority future work. Full article

Confining a phase-change material (PCM) within a polymer shell yields leak-proof, mechanically robust latent-heat storage media, but selecting a shell chemistry and polymerization route requires balancing competing targets: latent-heat storage density (ΔH, the melting enthalpy per unit capsule mass), core loading content (LC), capsule diameter (d), and a melting temperature (T_m) matched to the application. Because the literature characterizes each method–shell–core combination in isolation, these structure–property relationships cannot be compared quantitatively across studies. We present a proof-of-concept, data-driven framework linking shell and process descriptors to encapsulation performance. From a curated dataset of 90 micro- and nano-encapsulated PCM records (53 with measured ΔH) spanning 11 encapsulation routes and eight shell material families, Random Forest (RF) and Gaussian Process (GP) surrogates predict ΔH, and a non-dominated sorting genetic algorithm (NSGA-II) optimizes ΔH, LC, and d over the continuous (T_m, LC) space for every method–shell–core trio with at least three records (n = 11). Benchmarked against mean, linear-LC, and physics-informed baselines under repeated cross-validation, the surrogates match but do not exceed the elementary baselines (median R² ≈ 0.33), a result we report honestly given the modest sample size. The Matérn GP provides borderline-calibrated uncertainty, supporting a robust, extrapolation-penalizing NSGA-II. Hypervolume rankings place emulsion polymerization, sol–gel silica, and in situ polymerization as the top-performing methods under both nominal and robust criteria. Presented as a methodology demonstration rather than a definitive ranking, the framework, with full code and data, is a reusable approach for structure–property quantification of polymer-encapsulated PCMs as experimental data accumulate. Full article

State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power systems, robotics, and autonomous vehicles. Traditional estimators, including the Kalman Filter (KF) and its variants, provide physically interpretable residuals for fault detection but often fail to deliver reliable performance under nonlinear dynamics, modeling uncertainties, sensor faults, and non-Gaussian noise. This paper presents a comprehensive review of state estimation-based FDD approaches, with a particular focus on Artificial Intelligence (AI)-augmented Kalman filtering and hybrid frameworks that integrate Machine Learning (ML) models, including Neural Networks (NNs), Support Vector Machines (SVMs), and Gaussian Processes (GPs), with classical estimation theory. The review systematically evaluates model-based, data-driven, and hybrid methods, comparing their robustness, accuracy, computational efficiency, scalability, and interpretability in complex Cyber-Physical Systems (CPSs). Furthermore, emerging trends and open research challenges are identified, including online adaptation, fault-tolerant estimation, sensor fusion, explainable artificial intelligence (XAI), and deployment in Industry 4.0 and Internet of Things (IoT)-enabled environments. By bridging classical estimation theory with modern AI techniques, this review provides a roadmap for designing intelligent, adaptive, and resilient FDD systems capable of enhancing reliability, operational safety, and real-world applicability. Full article

This study presents an extended Blade Element Momentum Theory (BEMT) framework for predicting the hover performance of an EC135-class light-twin helicopter rotor while quantifying the impact of sectional aerodynamic uncertainty on global rotor metrics. Because the proprietary EC135 airfoils are not publicly available, a reproducible surrogate blade based on the ONERA OA213 and OA209 airfoils is adopted. The airfoil substitution is explicitly treated as an epistemic modelling assumption, and its effect on rotor-level hover predictions is assessed through a dedicated geometric comparison and bounded sensitivity analysis. The classical BEMT formulation is enhanced with Prandtl tip-loss corrections, Mach-dependent sectional aerodynamics, and an iterative non-uniform inflow model. Aerodynamic coefficients are obtained from Gaussian Process (GP) surrogate models trained on XFOIL-generated databases and calibrated using cross-validation techniques. The calibrated GP models are coupled with the rotor solver and their predictive uncertainty is propagated through Monte Carlo simulations. For the nominal hover trim condition, the rotor was trimmed to $C_T=0.00607$, while the model predicted a power coefficient of 0.00041 and a figure of merit of 0.819. The propagated GP/XFOIL-conditioned uncertainty yields a 95% confidence interval of 0.8074–0.8285 for the figure of merit, indicating limited sensitivity of rotor performance to sectional aerodynamic uncertainty. The influence of compressibility and tip-loss effects is also quantified. In a separate Caradonna–Tung solver-verification case, the thrust-coefficient error is reduced from 32.57% to 7.78% when finite-aspect-ratio corrections are included. The proposed framework provides a fast, reproducible, and uncertainty-aware approach for helicopter rotor hover analysis suitable for preliminary design and performance assessment. Full article

Lithium-ion batteries (LIBs) have been widely used in different fields as energy storage systems, such as electric vehicles and power grids. The performance of LIBs degrades with usage, which poses challenges for battery management. Thus, accurate online estimation of the state of health (SOH) is critical to ensure reliability and prolong the service time of LIBs. To achieve this, data-driven methods have become popular due to the capability of learning the mapping between SOH and measurements without prior knowledge of aging mechanisms. However, the online estimation performance of these methods cannot be guaranteed, since the models are trained offline and do not have the capability of online updating when new data are collected. In addition, the inputs for these methods are constructed with the voltage–capacity (V-Q) curve within a fixed voltage interval, which can hardly be realized in real-life applications due to the randomness of the charging or discharging process. This study proposes a Sequential Gaussian Process (Seq-GP) model-based LIB SOH estimation method, where model parameters can be updated using newly collected LIB data, such that online estimation can be fulfilled. Moreover, a novel feature extraction method is presented using random parts of the LIB V-Q curve to meet the requirements for practical applications. The proposed method is evaluated on two public battery datasets, showing competitive estimation accuracy together with online updating, uncertainty quantification, and low computational cost under the tested protocol. The results will be beneficial for online SOH estimation of LIBs in practical scenarios. Full article

Given the heterogeneity in data on heat pump performance curves across manufacturers, selecting the appropriate one for more detailed studies is complex. Machine learning (ML) techniques can be very helpful in this endeavor. In this case, four techniques were used: artificial neural networks (ANN), support vector machines (SVM), Gaussian Process (GP), and decision trees (DT), to predict HP performance maps. These four techniques were then applied to a hybrid installation consisting of an air-water HP boiler and a biomass boiler modeled with TRNSYS and connected in series. Performance maps were generated using TRNSYS type 581. Key aspects, including overall efficiency, emissions, lifetime costs, and design and control parameters, were then analyzed. The study found that the coefficient of variation of root-mean-square error (CVRMSE) was 14.9% for the DT model, 11.4% for the ANN, 11.1% for the SVM, and 10.7% for the GP model. The GP model was ultimately used to develop an HP performance map due to its highest accuracy, and comparisons with baseline data revealed significant differences in efficiency, operational costs, and emissions, among others. Full article

Gaussian process-based Bayesian optimization (BO) is a sample-efficient sequential strategy for optimizing expensive black-box functions. The Gaussian process provides a probabilistic approximation of the unknown function, while an acquisition function balances exploration and exploitation to select the next evaluation point. Despite significant research efforts, no master acquisition function has been identified. This paper proposes a novel adaptive acquisition function that dynamically adjusts the exploration–exploitation trade-off based on the evolution of the optimization process, rather than using fixed or random scheduling. While implemented here within a GP-based BO framework, the core switching mechanism is surrogate-agnostic: the exploitative component requires only a surrogate point prediction, and the explorative component is entirely model-free. Unlike traditional approaches, where mechanisms like UCB/LCB lean toward exploration over iterations, or fixed strategies that switch from exploratory (EI) to exploitative (PI) behavior at predetermined points, the proposed method makes purely exploitative decisions using only the GP’s prediction. However, it discards these decisions when they have low potential for significant improvement, instead focusing on uncertainty reduction. Notably, this approach uses inverse distance weighting for uncertainty quantification rather than the GP’s predictive uncertainty, avoiding bias from the GP’s predictions. Testing on benchmark functions demonstrates that the proposed acquisition function is almost always Pareto optimal, offering the most balanced trade-off between convergence to the global optimum and exploration capability compared to state-of-the-art alternatives. Full article