Search Results (398)

High-density cities require sustainable urban planning approaches that can screen recurrent surface heat while accounting for existing building-stock conditions. This study examines whether building-stock age composition, interpreted as a proxy for urban-renewal conditions and building-stock management needs, is associated with surface-heat persistence in Seoul, South Korea. Using 19 cloud-filtered summer Landsat Collection 2 Level-2 scenes from 2019 to 2025 and 485,473 building-age polygons, we construct a 250 m grid-cell evidence matrix. q75/q90 persistence is defined as the proportion of valid scenes in which a grid cell falls within the upper-tail scene-normalized LST distribution. Area-weighted mean building age is positively associated with q75 and q90 persistence; a p25-to-p75 increase in mean age corresponds to a 5.17 percentage-point increase in q75 persistence (95% CI: 4.54–5.81). The association is retained under spatial-filtering, spatial-lag-covariate, valid-pixel coverage-proxy, year-balanced, and threshold-universe checks. A planning-priority classification identifies 238 very-high-priority cells and 1166 high-priority cells for field review, roof/material surveys, vegetation/shading audits, and retrofit-feasibility assessment. The framework is a screening and prioritization tool, not evidence of causal redevelopment effects or direct human thermal exposure. Full article

Accurate state-of-charge (SOC) estimation is essential for preventing battery degradation, improving energy management, and providing reliable driving-range predictions in electric vehicles (EVs). The extended Kalman filter (EKF) is a widely adopted model-based estimation technique and remains an industry-standard approach in EV battery management systems (BMS). However, its performance can be degraded by model nonlinearities, parameter uncertainties, measurement noise, and interference bursts commonly encountered in real-world operating environments. To overcome these limitations, this paper proposes an event-triggered adaptive SOC estimation framework that integrates a recursive least squares (RLS) filter with the EKF. In the proposed approach, the RLS filter recursively updates its weighting coefficients in real time to compensate for model uncertainties and measurement disturbances, thereby generating an alternative residual signal for SOC estimation. An event-triggered mechanism dynamically selects the most reliable innovation sequence for updating the EKF state estimate, enhancing estimation robustness under adverse operating conditions. A second-order RC equivalent circuit model (ECM) is employed as the nominal battery model, and a Hybrid Pulse Power Characterization (HPPC)-based current profile is used to evaluate performance over the entire SOC operating range. Extensive simulations are conducted to assess the effectiveness of the proposed event-triggered RLS-EKF algorithm under various noise levels and interference-burst scenarios. The estimation accuracy is compared with that of the conventional EKF, cubature Kalman filter (CKF), and square root cubature Kalman filter (SR-CKF) using root mean square error (RMSE) and mean absolute error (MAE) as performance metrics. Simulation results demonstrate that, under regular noise conditions and short-term interference bursts, the proposed event-triggered RLS-EKF achieves estimation performance comparable to that of the SR-CKF while consistently outperforming the EKF and CKF in both RMSE and MAE. Under long-term interference-burst conditions, the proposed method further surpasses the SR-CKF, achieving approximately 10% improvement in overall estimation accuracy as measured by RMSE and MAE. These results confirm the effectiveness and robustness of the proposed framework, highlighting its potential for practical implementation in advanced EV battery management systems. Full article

This paper examines whether standard price-discovery measures can reliably identify directional predictive precedence in a highly correlated commodity-equity system. Using 21 years of daily data for Petrobras and the Ibovespa (2005–2026), the study separates a measurement problem in forecast error variance decomposition from the reduced-form question of directional predictability in the conditional mean. The empirical strategy combines Monte Carlo simulation, generalized and Cholesky forecast error variance decompositions, full-sample and rolling-window Granger causality tests, a continuous Granger Leadership Index, Gaussian mixture regime classification, robustness checks, and out-of-sample forecasting validation. The results show that Cholesky-based FEVDs can be systematically misleading in high-correlation settings: at the observed contemporaneous correlation, generalized FEVD symmetry is mechanically induced by row normalization, while Cholesky attribution changes sharply under alternative orderings. By contrast, first-moment predictability reveals a directional asymmetry from Petrobras to the Ibovespa, interpreted as conditional-mean predictive precedence rather than structural informed trading or definitive price discovery. This asymmetry survives alternative lag structures, weekly aggregation, univariate GARCH filtering, within-dataset proxy controls, and a stylized equal-weight ex-Petrobras benchmark. Rolling evidence further identifies five persistent predictive regimes that alternate between firm-led, neutral, and macro-dominant states, indicating that firm-index predictive relations are regime dependent rather than static. Out-of-sample forecasting shows that the identified predictive precedence does not generate exploitable one-step-ahead gains (RMSE ratio = 1.002, OOS-R² = −0.003, DM p = 0.451), thereby delimiting the economic scope of the findings. Overall, the results support a reduced-form interpretation of Petrobras–Ibovespa predictive dynamics and highlight the need to distinguish variance connectedness from conditional-mean predictive content when contemporaneous correlation is high. Full article

Low-light enhancement is a crucial task in computer vision; it can improve either the subjective experience of viewers or the usability of computer vision systems designed for normal-light images. In this paper, a variational Retinex model in the image domain is developed for low-light enhancement, which infuses classical/fractional differentiation of the input image into the illumination/reflectance component by means of a structure/texture-aware map (SAM/TAM). Firstly, the SAM (TAM) is generated by the inverse square of classical (fractional) differentiation of the input image. Secondly, the regularization term of illumination (reflectance) is defined by utilizing the SAM (TAM) as a weighted matrix, and an illumination guidance term is incorporated into the objective function. The illumination guidance term encourages the estimated illumination to encompass more structural information by penalizing deviation of illumination from the illumination pre-estimated by a dark channel prior to a guided image filtering. Finally, an alternative algorithm is employed to solve the minimization problem involved in the model. The performance of the proposed method is evaluated on three datasets for low-light enhancement and compared with eight state-of-the-art Retinex methods, qualitatively and quantitatively. Evaluation results show that the proposed method generally achieves higher performance in terms of low-light enhancement. Full article

Long-range airborne streak tube imaging lidar (ASTIL) raw echo images are degraded by atmospheric speckle, detector noise, and weak-return fluctuations, which can bias centroid localization before range calculation. This study presents a lightweight preprocessing method combining row–column geometry-aware echo region pre-classification with frequency-domain histogram-based adaptive suppression. Candidate regions are extracted from normalized streak images, classified by row–column morphology, filtered using local magnitude-spectrum percentile thresholds, and fused with a background-constrained weighted strategy. Simulated echo images, simulated point clouds, and 6 km airborne data were used for validation. In selected building roof control regions, the mean elevation root mean square error (RMSE) decreased from 0.34 m to 0.29 m, the mean absolute error (MAE) from 0.30 m to 0.26 m, and the mean roof elevation standard deviation from 0.19 m to 0.15 m, corresponding to an approximately 21% reduction in roof-level point cloud thickness. The results show that preprocessing before centroid extraction can improve roof-level vertical consistency without neural-network training or complex point cloud post-processing. Full article

Accurate and robust localization is essential for intelligent vessels operating in complex marine and port environments. However, single-sensor localization is often affected by limited observation range, environmental occlusion, local interference, and sensor degradation. Although multi-sensor fusion can improve localization reliability, unknown cross-correlated measurement noise arising from shared disturbances, time synchronization errors, communication delays, and inconsistent fusion rates may degrade traditional information-filter-based fusion methods. To address this problem, this paper proposes an Adaptive Correlation Information Filter Network (ACIFNet) for multi-sensor fusion localization of intelligent vessels. ACIFNet preserves the recursive structure of the extended information filter and uses a Transformer-based network to learn adaptive information-domain fusion weights, thereby compensating for unknown inter-sensor correlations without explicitly estimating the full correlation covariance matrix. Experiments on constant-velocity, coordinated-turn (CV), and three-degree-of-freedom vessel motion models, together with a real-world restricted-waterway dataset, demonstrate that ACIFNet achieves higher localization accuracy and stability than Edge Incorporative Fusion (EIF)-inexact fusion, measurement fusion, and KalmanNet. In the CV and three-degree-of-freedom experiments, ACIFNet reduces the mean RMSE by 48.7%, 23.2%, and 26.1%, respectively, compared with KalmanNet. On the real-world dataset, ACIFNet achieves a mean position error of 9.90 m, an RMSE of 11.24 m, and a cross-track error of 8.72 m. These results show that ACIFNet effectively combines the interpretability of information filtering with the adaptive representation capability of neural networks for robust multi-sensor fusion localization under unknown cross-correlated measurement noises. Full article

Software defect prediction (SDP) aims to identify defect-prone modules before testing, but severe class imbalance and redundant software metrics often limit prediction performance. Many conventional feature selection methods estimate feature relevance with the original imbalanced empirical distribution and mainly emphasize marginal relevance or global classifier-oriented criteria, which may under-prioritize features that are informative for the minority defective class. To address this issue, this paper proposes SCAR-CMB, a simplified class-reweighted and interaction-aware feature selection method for imbalanced SDP. SCAR-CMB estimates feature-label dependency with a class-balanced empirical distribution, controls redundancy using weighted conditional dependency information, and incorporates an interaction-aware conditional-gain term as an auxiliary re-prioritization signal within a relevance-screened feature pool. Rather than performing full causal structure discovery or formal synergy estimation, SCAR-CMB adopts a Markov-blanket-inspired conditional dependency design as a practical guide for feature selection. The final configuration excludes both hardness-aware weighting and false discovery rate filtering. SCAR-CMB is evaluated on ten public NASA and PROMISE defect datasets under a leakage-free cross-validation protocol. Compared with seven representative baselines, SCAR-CMB achieves competitive overall performance and obtains the highest average defective-class recall, G-mean, and balanced accuracy. However, it is not uniformly superior across all metrics, and the recall advantage is not confirmed by the omnibus Friedman test. Additional mechanism-level, stability, and sensitivity analyses show that class reweighting changes feature prioritization, the selected feature subsets are relatively stable across folds, and the interaction-aware term provides limited and dataset-dependent auxiliary effects. Sensitivity analyses further indicate that the main conclusions are not solely determined by a specific feature budget, discretization-bin setting, or downstream classifier. Overall, SCAR-CMB should be interpreted as a practical minority-class-oriented feature selection method that provides a trade-off among defective-class detection, feature subset control, and computational cost. Full article

Vertically linked commodity markets—global futures, regional spot, and farm-gate prices—transmit information through directed cross-market channels whose strength varies with latent volatility regimes. Standard deep learning forecasters absorb both the directed cross-market dependence and the regime dependence of intrinsic-mode-aligned latent components into shared model weights, with no explicit architectural mechanism that exposes either as an inspectable structure. This paper proposes HVB-RA, a modular framework that combines two such mechanisms with a per-tier Variational Mode Decomposition and bidirectional LSTM backbone: (i) a directed cross-market attention layer in which the upstream-to-downstream topology is supplied from domain knowledge and the time-varying upstream-source attention intensities at the farm-gate tier (the regional-spot tier, with a single upstream key, reduces algebraically to a fixed residual upstream fusion) are extracted from data, and (ii) a regime-informed modal-weighting layer that mixes two trainable softmax weight profiles over IMF-aligned latent components through a filtered Markov-switching state probability fitted in a separate stage. An auxiliary post hoc projection enforces an exact linear constraint defined by long-run sample-mean ratios across tiers; the paper does not claim that these descriptive ratios are cointegrating relations or equilibrium coefficients. The framework is evaluated on three tiers of daily natural-rubber prices spanning 2038 trading days, against three external benchmarks (random walk, ARIMA(2,0,2), and an exogenous-only LSTM) and a contemporary neural hierarchical-interpolation forecaster (NHITS). Root mean squared error is reported per tier-horizon cell; a decision-aware income-smoothing metric quantifies the operational value of $h=5$ farm-gate forecasts under a 5-day selling rule; and a within-method comparison evaluates the marginal contribution of the auxiliary constraint projection. On the present single-regime test window, HVB-RA attains a lower point error than the contemporary NHITS baseline at every tier-horizon cell, while no method—including HVB-RA—improves on the random-walk floor at most cells; the regime-conditional components of the architecture are not identifiable because every calibration and test origin is classified as a high-volatility regime by the trained Markov-switching model. The paper contributes to machine learning and knowledge extraction by demonstrating how time-varying upstream-source attention intensities at the farm-gate tier and regime-dependent latent-component-weight profiles—two forms of latent structure typically absorbed into model weights—can be exposed as explicit, inspectable, and individually testable components of a multi-tier forecasting architecture, and by providing a reproducibility package documenting the conditions under which each component is expected to be identifiable. Full article

This study proposes VulnPattern-TKG, an end-to-end temporal knowledge graph framework that forecasts the emergence of CVE-derived vulnerability-pattern relations from Common Vulnerabilities and Exposures (CVE) descriptions. The framework does not aim to predict the real-world exploitation of individual CVEs; instead, it models how standardized relations among Weakness Factor (WF), Exploitation Outcome (EO), and Exploitation Prerequisite (EP) categories evolve over time in vulnerability disclosure text. It processes 205,600 National Vulnerability Database (NVD) CVE descriptions from 2014 to 2024 using a hybrid pipeline combining SecureBERT+CRF-based entity extraction, dependency-parsing-based relation rules, and four-stage hierarchical standardization. The resulting compact Knowledge Layer contains 26 standardized category nodes and 48,371 confidence-filtered triples. VulnTEC is a lightweight confidence- and time-weighted Node2Vec graph embedding framework that ranks relation-compatible candidate tails using cosine similarity over shared node embeddings. An internal four-component priority-score framework, integrating prediction confidence, temporal rise, exploitation-prerequisite prevalence-risk proxy, and extraction confidence, supports an analyst-side review of the forecasted relations. Under the novel-only triggers evaluation, VulnTEC achieves a mean MRR of 0.410 ± 0.020; however, the theoretical random baseline already reaches 0.408 because the candidate tail space contains only six EO categories. The results are interpreted as directional ranking evidence, and query-level Top-K results are reported only as descriptive analyst-side review evidence. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

Consolidated bioprocessing (CBP) is difficult to monitor because enzyme production, lignocellulose degradation, sugar release, and fermentation occur simultaneously under sparse measurement, feedstock variability, and plant–model mismatch conditions. This study proposes a computational sensor-set design framework for digital-twin-assisted CBP monitoring. A five-state virtual plant, consisting of active biomass, cellulolytic enzyme activity, residual insoluble substrate, soluble sugar, and ethanol, was used to evaluate all 16 ethanol-mandatory measurement packages formed from ethanol, sugar, biomass, enzyme, and residual-substrate proxy channels. Candidate sensor sets were assessed using finite-difference output sensitivities, Fisher-information-based state-observability and parameter-identifiability analyses, eigenvalue and parameter-correlation diagnostics, and paired Monte Carlo unscented Kalman filter soft-sensing reconstruction. Within the tested five-state virtual-plant benchmark and with the specified excitation schedule, noise assumptions, burden indices, and scoring objective, ethanol-only sensing provided the weakest support for state-aware CBP digital-twin reconstruction. At a $6\mathrm{h}$ sampling interval, the state-observability log-pseudodeterminant increased from 4.18 with ethanol-only sensing to 8.56 after adding soluble sugar and to 16.42 with full-proxy monitoring. The ethanol–sugar–biomass–substrate package also gave strong reduced state-observability performance, with log-pseudodeterminants of 15.12, 13.76, and 12.51 at 6, 12, and $24\mathrm{h}$, respectively. Biomass and enzyme proxies contributed strongly to parameter learning, and the ethanol–sugar–biomass–enzyme package gave the strongest active parameter-identifiability performance, with log-pseudodeterminants of 10.82, 9.06, and 6.67 at 6, 12, and $24\mathrm{h}$, respectively. In the paired soft-sensing analysis, full-proxy monitoring reduced the mean latent-state RMSE from 1.1899 to 0.3756, followed by ethanol–biomass–enzyme–substrate with 0.3843 and ethanol–sugar–biomass–substrate with 0.4121. The primary aggregate ranking identified ethanol–sugar–biomass–substrate as the best overall package, with a sensor-value score of 0.8432 and a burden index of 7.0, followed by full-proxy monitoring with a score of 0.8173 and a burden index of 10.0. Robustness tests showed that ethanol–sugar–biomass–substrate remained top-ranked under uniform noise scaling, full UKF missingness, delay and bias stress test conditions, most scoring-weight scenarios, and all tested sensor-specific burden workflows. Full-proxy monitoring remained a close competitor under independent sensor-specific noise variation conditions and became top-ranked for some alternative operating trajectories. The proposed framework provides a simulation-based method for prioritizing informative measurement packages before implementing CBP digital twins in laboratory and pilot-plant settings. Full article

The introduction of noise during image acquisition and transmission is inevitable, leading to a significant reduction in the accuracy of image processing tasks, such as target classification, localization, and recognition. To address this issue, this paper proposes a novel robustness-oriented pooling module called Gaussian adaptive pooling. Drawing on the principles of Gaussian filters, the method introduces a Gaussian weight for feature values in the pooling operation, thus integrating filtering and pooling in a novel manner. This approach is both lightweight and versatile, requiring no additional learnable parameters, and enables seamless integration into neural network architectures with pooling layers. Rigorous mathematical derivations and simulation experiments show that our proposed Gaussian adaptive pooling method surpasses conventional methods (average-pooling and max-pooling) in noise handling. Furthermore, its robustness is comparable to traditional pooling methods in addressing challenges such as rotations, scalings, and translations. Extensive evaluations across multiple computer vision tasks—including image classification (CIFAR-10/100), object detection (MS COCO and RTTS), and semantic segmentation (CamVid)—confirm its effectiveness. Specifically, under varying levels of noise and degraded conditions, Gaussian adaptive pooling achieves significant improvements in standard performance metrics compared to conventional pooling methods. For instance, it delivers notable quantitative gains across different tasks including up to a 12.67% increase in mean intersection over union on the CamVid dataset for semantic segmentation and a 1.1% mAP50 enhancement on the real-world RTTS dataset for object detection. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Achieving long-term, continuous, and accurate localization for Unmanned Aerial Vehicles (UAVs) in outdoor GNSS-denied environments where pre-existing reference maps are available is challenging. To this end, this paper proposes a Cross-view Particle Filter Localization (CPFL) framework. Unlike existing particle filter approaches that rely on inertial sensors for state propagation or sparse semantic labels for observation updates, CPFL is a vision-driven solution. This framework introduces specific adaptations into the two core stages of particle filtering: In the motion propagation stage, it achieves visual state transition by calculating a feature-based inter-frame homography mapping to estimate the 2D global relative motion components, eliminating the dependency on inertial priors; in the observation correction stage, a Dual-Granularity Adaptive Gating (DGAG) cross-view network is designed to mitigate perceptual aliasing and generate discriminative absolute position weights for the particles. By fusing these two stages through a filter mechanism, the framework transforms unbounded cumulative drift into bounded absolute localization errors. Furthermore, addressing the measurement deficiencies of traditional single-frame metrics, this paper also proposes a Trajectory Continuity Index (TCI@d) tailored for continuous localization tasks. Experiments on the real-world MAFS dataset confirm that this framework achieves a mean localization error of 5.28 m and a localization success rate of 89.7% under a 10-m threshold. Compared with mainstream vision-only algorithms and IMU-fusion baselines, this framework demonstrates lower mean errors and improved trajectory continuity, validating its effectiveness for long-term robustness. Full article

This study proposes the Quadruple Exponentially Weighted Moving Average (QEWMA) control chart, a novel monitoring scheme designed to enhance the detection of small-to-moderate process mean shifts in the presence of autocorrelation. While traditional EWMA-based charts often struggle with dependent data, the proposed QEWMA utilizes a four-layered smoothing mechanism to effectively filter noise in Moving Average processes. The performance of the QEWMA chart was rigorously evaluated using the Numerical Integral Equation (NIE) approach to calculate the Average Run Length (ARL) and the Standard Deviation of Run Length (SDRL). Comparative results across MA(1), MA(2), and MA(3) models demonstrate that the QEWMA chart significantly outperforms the standard EWMA, DEWMA, and TEWMA charts, particularly for subtle shifts ($\delta \le 0.10$). The practical utility of the proposed chart was further validated through two real-world applications: monitoring Thailand’s daily median income (MA(3)) and gold futures prices (MA(2)). In both applications, the QEWMA chart exhibited superior sensitivity and faster detection rates, providing more reliable signals for economic and financial surveillance. These findings suggest that the QEWMA chart is a robust and highly efficient tool for quality control in complex, autocorrelated industrial and economic environments. Full article