Search Results (4,276)

Space-based multi-target passive tracking is critical for space situational awareness, but faces severe challenges due to the limited field-of-view (FoV) and directional ambiguity of onboard sensors. These constraints often lead to target loss, poor observability, and decreased estimation accuracy. To address these issues, different fusion architectures have been explored. While centralized measurement-level fusion offers superior accuracy for estimating target states, distributed estimation-level fusion provides greater reliability for estimating the number of targets. To adaptively leverage these two complementary strengths, a dynamic hierarchical fusion method through real-time optimization of the fusion topology is proposed. Specifically, at each decision epoch, sensor nodes are dynamically partitioned into local fusion nodes (LFNs) and detection-only nodes (DONs). Each LFN receives measurements from selected DONs and executes an iterated-correction Gaussian-mixture probability hypothesis density filter. Subsequently, LFNs share and fuse their estimates using the intensity-dependent arithmetic average fusion. This dynamic process is achieved by applying a sensor management scheme based on partially observable Markov decision process (POMDP). To ensure accurate cardinality estimation, the reward function in POMDP utilizes the posterior expected number of targets. The resultant optimization is efficiently solved using a binary particle swarm optimization algorithm. Numerical and hardware-in-the-loop simulations demonstrate the effectiveness of the proposed method in balancing the accuracy of target number and state estimation. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. Full article

Accurately assessing students’ knowledge states and dynamically adapting instructional interactions to their cognitive levels are fundamental to optimizing personalized learning. However, conventional knowledge tracing (KT) approaches are constrained by three critical limitations: data sparsity undermines prediction robustness, the neglect of forgetting behavior misrepresents real learning processes, and static knowledge-state modeling fails to capture learners’ dynamic cognitive changes. To overcome these shortcomings, this study proposes DRAKT (Dynamic Reinforcement learning-based Adaptive Knowledge Tracing), a novel model that introduces two key innovations: (1) a Q-learning-based knowledge-state adjustment mechanism, which dynamically updates mastery levels via a reward structure integrated with the Ebbinghaus forgetting curve; and (2) a dynamic memory update module that combines a gated recurrent unit (GRU) with attention-based filtering to capture long-term learning dependencies and suppress irrelevant memory traces. Experiments conducted on three public ASSISTments datasets (2009, 2012, and 2017) demonstrate that DRAKT consistently outperforms state-of-the-art baselines. On ASSISTments2017 and ASSISTments2009, DRAKT achieves AUC scores of 82.08% and 81.47%, respectively, surpassing the second-best model (GKT) by 2.75–6.57 percentage points in AUC and 4.77–5.75 percentage points in accuracy. In practice, DRAKT offers a reliable technical foundation for enabling personalized learning-path recommendation and real-time cognitive adaptation in intelligent educational systems. Full article

To address the limitations of the Kernelized Correlation Filter (KCF) in handling scale variation and occlusion during visual tracking, this paper proposes a scale-adaptive and occlusion-robust KCF-based tracking method. The proposed approach integrates the Histogram of Oriented Gradients (HOGs) and Color Name (CN) features to fully exploit pixel-level information, thereby improving the accuracy of target localization. On this basis, a sub-region-based scale adaptation mechanism is introduced. Specifically, the target is partitioned into multiple sub-regions, and the KCF classifier is applied to each sub-region to estimate its center position. The relative displacement among these sub-region centers is then utilized to estimate target scale variation, enabling adaptive scale tracking. In addition, an occlusion-aware mechanism is designed to enhance robustness under occlusion. During tracking, occlusion detection is performed, and once occlusion is detected, template updating is suspended. Oriented FAST and Rotated BRIEF (ORB) features extracted from the template are subsequently matched with features from subsequent frames to re-acquire the target. Experimental results on the OTB2013 and OTB2015 benchmarks demonstrate that the proposed method achieves competitive precision and success rates compared with the baseline KCF and other representative trackers, while satisfying real-time tracking requirements using only CPU resources, indicating its practical applicability in resource-constrained environments. Full article

A tool-health digital twin for CNC predictive maintenance is developed and operationalised as a fusion-and-state-estimation core that produces a latent tool-health trajectory (wear level and wear-rate dynamics) from multi-rate sensor streams for diagnosis and remaining useful life (RUL) forecasting under strict edge latency constraints. The scope is tool-health–informed maintenance decisions (condition-based tool replacement/scheduling), rather than a comprehensive maintenance twin for all CNC subsystems. Multi-rate vibration, spindle-current, and temperature signals are synchronized and windowed, and a linear state-space model with Kalman filtering and innovation-guided adaptive noise estimation stabilizes the latent health state across operating-regime changes. The fused state is then used by compact sequence learners, an LSTM for edge feasibility, and a compact Transformer as a higher-accuracy comparison, to output fault categories and RUL estimates. Predictive uncertainty is quantified via a Monte Carlo dropout and linked to reliability-aware actions through a simple alarm/defer/schedule policy, while SHAP provides feature-level interpretability. On a CNC testbed, fusion improves fault F1 from 0.811 to 0.892 and PR-AUC from 0.867 to 0.918 while reducing RUL RMSE from 10.4 to 8.1 cycles; the compact Transformer reaches 0.903 F1 and 7.9-cycle RMSE at higher inference time. The end-to-end pipeline remains within a ≤100 ms breakdown, maintains in-band innovation statistics, supports rehearsal-based updates under drift, and is additionally evaluated on external tool-wear and turbofan datasets. Full article

Although cloud storage is widely trusted by users and enterprises, externally stored encrypted and fragmented data remain vulnerable to reconstruction and inference attacks following partial exposure. Existing decoy-based defenses often rely on static configurations or randomly generated artifacts that can be filtered during adversarial analysis. This paper presents an Artificial Intelligence (AI)-controlled modular decoy generation method to enhance reconstruction resistance in distributed storage systems. The method operates as a system-agnostic post-fragmentation layer and does not require modification of encryption or storage architecture. Given encrypted fragments as input, decoys are generated using a supervised Extreme Gradient Boosting (XGBoost) regression model that adapts decoy quantity based on system telemetry and resource conditions. Decoys maintain statistical alignment with real encrypted fragments in size and Shannon entropy characteristics. To address scalability, the method is evaluated across small, medium, and large deployments comprising up to 413 externally exposed fragments and compared against fixed-ratio (10%, 20%) and randomized baselines. Experimental evaluation demonstrates increased adversarial uncertainty without altering legitimate reconstruction procedures or encryption mechanisms. Kolmogorov–Smirnov analysis indicates no statistically significant difference between AI-generated decoys and real fragments, whereas baseline decoys produce significant deviations in size and entropy distributions, supporting reconstruction resistance at scale in multi-cloud environments. Full article

Self-supervised contrastive learning has become an effective approach for visual representation learning when large-scale annotation is impractical. In this study, we evaluate three widely used methods—SimCLR, MoCo v2, and BYOL—for large-scale stock keeping unit (SKU) recognition in retail environments. Experiments are conducted on the RP2K benchmark and a domain-specific in-house dataset (InSKU) using both linear probing and full fine-tuning. Under the original RP2K configuration with extended self-supervised pre-training, SimCLR achieves the highest Top-1 accuracy under linear evaluation (94.98%). In contrast, BYOL attains the highest performance under full fine-tuning (99.22% Top-1 accuracy). After filtering and deduplicating the dataset to reduce class imbalance and near-duplicate samples, MoCo v2 achieves competitive, and in some cases superior, linear performance under a reduced training budget. Cross-domain evaluation on InSKU indicates that SimCLR generalises more effectively under frozen-encoder constraints, whereas BYOL and MoCo v2 require full adaptation. These results highlight the sensitivity of contrastive representations to dataset composition, optimisation regime, and domain shift, providing practical guidance for deployment in dynamic retail settings. Full article

Accurate classification of fish trophic strategies based solely on gut contents can be misleading, especially when plant material is ingested incidentally during predatory benthic foraging. The Pátzcuaro chub (Algansea lacustris) is a critically endangered cyprinid endemic to Central Mexico. It has historically been described as omnivorous with a tendency toward algivory, despite limited anatomical evidence. In this study, integrated anatomical, morphometric, and functional approaches were used to reassess the feeding strategy of A. lacustris and inform conservation-oriented aquaculture. Double-staining techniques revealed a specialised filtering and crushing branchial–pharyngeal system adapted to capture and process animal prey. Relative intestinal length (RIL) was measured from freshly dissected intestines. Intestinal transit time was experimentally evaluated using a formulated diet and live Artemia. Algansea lacustris exhibited a short intestine (RIL = 0.86 ± 0.10) and rapid intestinal transit (<30 min), both of which are characteristics of carnivorous teleosts. These results provide consistent anatomical and physiological evidence that A. lacustris is primarily adapted to a low-trophic carnivorous or insectivorous feeding strategy, with important implications for its ecological characterisation. Moreover, intestinal transit was faster after ingestion of live Artemia than after the formulated diet, likely due to differences in moisture content. The observed short transit times indicate the need for more frequent feeding and support the refinement of diet formulation and feeding strategies in conservation aquaculture programmes. Full article

To address the insufficient capability of YOLO-series models in representing structural information for foreign objects with diverse scales and morphologies, an improved algorithm named SSFE-YOLO is proposed. First, the Space-to-Depth Convolution (SPDConv) is adopted into the backbone network to preserve edge and texture details in shallow features during downsampling, thereby maintaining the integrity of critical target structures at the feature generation stage. Second, an adaptive receptive field enhancement module (ARFE) is designed by introducing parallel feature branches with varying receptive fields. This module performs adaptive fusion to bolster the structural perception of the network towards polymorphic foreign objects. Furthermore, a distribution-feature stable compensation module (DFSC) is designed to suppress feature distribution shifts caused by illumination variations and noise interference through structural consistency enhancement and stable distribution constraints, which significantly improves the stability of feature representation in complex environments. Finally, a dual-dimension optimized loss function (D${}^2$-OL) is constructed to achieve differentiated supervision for samples of varying quality and balanced optimization for multi-scale target detection by modulating the supervisory weights of feature layers and filtering effective training samples. Experimental results on a self-built mine conveyor belt dataset demonstrate that the proposed method achieves an mAP@0.5 of 90.5% and an mAP@0.5:0.95 of 59.1%, consistently outperforming mainstream models such as YOLOv8, YOLOv11, and YOLOv13. Simulation results indicate that the proposed approach effectively enhances the detection accuracy and robustness of foreign objects in mining environments, showcasing substantial potential for engineering applications. Full article

Bipartite network community detection is of significant importance for understanding the underlying structure and functional organization of real-world complex systems. Although many mature community detection algorithms exist for unipartite networks, they cannot be directly applied to bipartite networks due to their unique topological structure, characterized by heterogeneous node types and cross-layer connections. Furthermore, some existing bipartite network community detection methods still rely heavily on manual experience to set key parameters, which limits their applicability and scalability in practical scenarios. To address these issues, this paper proposes an enhanced framework—the Adaptive Network Reconstruction Framework (ANRF)—by introducing an adaptive parameter optimization mechanism based on the existing Network Reconstruction Framework (NRF). This framework can be effectively integrated with traditional unipartite network community detection algorithms to achieve automatic community detection with reduced dependence on manual parameter tuning. The core procedure of the method consists of four main steps. First, we calculate the interaction forces between node pairs. Second, through comprehensive analysis of the network topological features, we adaptively determine the threshold parameter $\theta$ and related parameters for the interaction forces. Third, based on these thresholds and parameters, we perform edge filtering on the bipartite network to construct a reconstructed network. Finally, we apply unipartite community detection algorithms directly to the reconstructed network to obtain the community structure. To validate the effectiveness of ANRF, we combined it with the Louvain method and the Greedy modularity method, and conducted experimental evaluations on multiple synthetic and real-world network datasets. A systematic comparison with current state-of-the-art algorithms was made. The experimental results on multiple synthetic and real-world datasets within our evaluated scope demonstrate that ANRF achieves competitive performance in terms of community modularity and community density compared to state-of-the-art algorithms, while significantly reducing reliance on manual parameter tuning and enhancing robustness under the tested conditions. Full article

Sand production in an offshore hydrocarbon wells poses significant operational and integrity challenges, particularly in deviated wells, where complex flow geometries intensify particle transport and erosion risks. The traditional sand-monitoring method utilizes stationary acoustic sensors attached to the production flowline at the surface. However, these sensors provide limited spatial coverage and intermittent measurements, restricting their ability to detect early sanding onset or precisely localize sanding intervals. By combining with vertical seismic profiling (VSP), Distributed Acoustic Sensing (DAS) delivers continuous, high-density data along the entire length of the wellbore and is increasingly recognized as a powerful diagnostic tool for real-time downhole monitoring. This study presents a field application of DAS-VSP for detecting and characterizing sand transport in a deviated offshore production well equipped with 350 distributed fiber-optic channels spanning 0–1983 m true vertical depth (TVD) at 8 m spacing. A multistage workflow was developed, including SEGY ingestion and shot merging, channel and time window selection, trace normalization, and low-pass filtering below 20 Hz. Multi-domain signal analysis, such as RMS energy, spike-based time-domain attributes, FFT, PSD spectral characterization, and time–frequency decomposition, were used to isolate the characteristic im-pulsive low-frequency (<20 Hz) signatures associated with sand impact. An adaptive thresholding and event-clustering scheme was then applied to discriminate sanding bursts from background noise and integrate their acoustic energy over depth. The processed DAS section revealed distinct, depth-localized sand ingress zones within the production interval (1136–1909 m TVD). The derived sand log provided a quantitative measure of sand intensity variations along the deviated wellbore, with normalized RMS amplitudes ranging from 0.039 to 1.000 a.u., a mean value of 0.235 a.u., and 137 analyzed channels within the production interval. These results indicate that sand production is highly clustered within discrete depth intervals, offering new insights into sand–fluid interactions during steady-state flow. Overall, the findings confirm that DAS-VSP enables continuous real-time monitoring of the sanding behavior with a far greater depth resolution than conventional tools. This approach supports proactive sand management strategies, enhances well-integrity decision-making, and underscores the potential of DAS to evolve into a standard surveillance technology for hydrocarbon production wells. Full article

We developed a Bluetooth Low Energy-based indoor asset-tracking system oriented toward elderly care and cultural heritage stewardship. The system stabilizes the noisy received signal strength indicator using a Kalman filter, adapts a logarithmic path loss model to local attenuation via dynamic calibration, and estimates positions with an inverse distance weighted centroid. Built on inexpensive beacons and commodity gateways, it supports real-time updates and map-based visualization while remaining easy to deploy and scale across rooms and facilities. We validate the pipeline in a laboratory grid and discuss applicability to workflows such as geofenced reminders, caregiver situational awareness, and collection movement oversight, offering an affordable, interoperable path to reliable indoor tracking for care institutions, museums, and smart buildings. Full article

The wastewater generated during coffee processing contains high levels of acidity and organic matter, posing substantial environmental hazards, particularly in rural areas where traditional treatment methods are financially infeasible. This research assesses the field-scale effectiveness of Chrysopogon zizanioides (vetiver grass) in phytoremediation of coffee wastewater in Huánuco, Peru, with particular attention to how plant maturity affects treatment outcomes. A comparative analysis was performed on untreated and vetiver-filtered effluent from infiltration ponds at four growth stages (6, 8, 19, and 21 months), with measurements of pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD₅), and suspended solids (TSS, SS) conducted according to standardized methods. The findings indicate notable improvements in water quality, as the pH rose from 4.07 ± 0.32 to 5.82 ± 0.40 (p < 0.001) and organic loads decreased by 39–41% (COD: 38,600 ± 12,100 to 23,000 ± 8500 mg L⁻¹ O₂; BOD₅: 27,700 ± 9400 to 16,500 ± 5600 mg L⁻¹ O₂). Total Suspended Solids (TSS) were reduced by 26%, while the settleable suspended solids fraction (SS) decreased by 69%, indicating strong particulate removal through combined filtration and sedimentation mechanisms. Mature vetiver stands (21 months old) showed better results, underscoring the importance of root development for effective phytoremediation. Strong correlations were observed between COD and BOD₅ (r = 0.92), while pH negatively correlated with organic and particulate parameters. The study presents empirical evidence supporting vetiver-based systems as an economical and sustainable approach to decentralized wastewater treatment in coffee-growing areas. Furthermore, it provides actionable insights for improving phytoremediation by focusing on plant maturity, which can be readily adapted for large-scale implementation in resource-constrained settings. The findings underscore the potential of nature-based technologies to address environmental challenges while supporting local economies dependent on coffee production. Full article

This study investigates the influence of microgravity on the fundamental frequency (F0) of astronauts’ speech. A speech corpus was compiled, including recordings in microgravity and on Earth, matched by speaker and content. The signal processing methodology included filtering with consideration of human auditory perception, segmentation of speech fragments, F0 estimation using digital signal processing techniques, and visualization through fundamental frequency dynamics plots. Results revealed a consistent increase in F0 for most astronauts under microgravity, with maximum values of 450 Hz for female speakers and 245 Hz for male speakers. Elevated F0 levels were observed for approximately 86% of the total duration of speech fragments recorded in microgravity, compared with 14% on Earth. These findings confirm that microgravity affects the speech apparatus and acoustic characteristics of voice. Practical implications include adapting voice-controlled systems and automatic speech recognition for space environments, monitoring crew condition, and studying speech physiology under extreme conditions. Full article

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation. Full article