Search Results (759)

Multimodal emotion recognition systems based on electroencephalography (EEG) and eye tracking (ET) provide complementary information about neural and visual responses; however, practical deployment requires balancing classification performance and computational cost. This study investigates this trade-off by systematically evaluating 60 feature representation configurations generated from different EEG channel regions, frequency bands, feature types, and signal modalities. Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) classifiers were evaluated under subject-independent leave-one-subject-out (LOSO) cross-validation, resulting in 180 realizable classifier-feature configuration systems. Median Macro-F1 was used as the primary performance objective, while median classifier inference time was used as the computational-cost objective. A multi-stage selection strategy integrating Δ-based near-optimal filtering and Pareto dominance analysis was applied to identify performance-efficient systems. The highest median Macro-F1 (0.6474) was achieved by an ANN using temporal delta-band PSD features combined with ET information, with a median classifier inference time of 0.0057 s. This system remained the final selected system across Δ values of 0.01, 0.02, and 0.03. An ET-only ANN baseline achieved a median Macro-F1 of 0.6265, indicating a modest improvement when temporal delta-band EEG information was added. These findings demonstrate that feature representation, modality composition, classifier choice, and computational cost should be considered jointly when designing subject-independent multimodal emotion recognition systems. Full article

Multi-object tracking (MOT) in low-light environments presents significant real-world application value. Despite substantial progress in MOT, low-light MOT remains constrained by the scarcity of specialized datasets, largely because collecting and manually annotating low-light tracking data is difficult and often prohibitively expensive. This paper specifically addresses these challenges through methodological and dataset innovations. We first present low-light multi-object tracking (LLMOT), the first comprehensive low-light MOT dataset containing 11,580 images, including 5316 labeled and 6264 unlabeled images, consisting of nighttime-enhanced MOT17 sequences and multiple unannotated low-light videos. To simultaneously alleviate the constraint of annotation costs and address the damage that low-light-induced image degradation causes to pseudo-label quality, we propose Consistency Regularization Track (CRTrack), a semi-supervised framework tailored for low-light scenarios. Specifically, we introduce a consistent adaptive sampling assignment mechanism that calibrates and filters noisy and shifted pseudo-bounding boxes under low-illumination conditions. We then design an adaptive semi-supervised network update strategy that enables the model to more stably exploit unlabeled low-light videos for iterative optimization. Extensive experiments on the LLMOT dataset validate the effectiveness and robustness of the proposed method. CRTrack achieves 62.472 HOTA, 71.544 MOTA, and 75.864 IDF1 on the LLMOT dataset, demonstrating its effectiveness in low-light MOT. Our approach provides a practical solution for low-light MOT tasks with significant real-world implications. Full article

In Global Navigation Satellite System (GNSS)-denied deep-sea environments, traditional scalar gravity matching navigation methods frequently suffer severe performance degradation in weak-feature, highly repetitive gravity anomaly regions. Inspired by the hippocampal spatial memory mechanism and natural graded foraging behavior of benthic marine organisms, this paper proposes a full-chain bionic framework named the Physics-Consistent Multi-Scale Adaptive Particle Filter for Gravity Matching Navigation (PC-MAPF-GM). This method endows the particle filter with four layers of biologically mimicked autonomous regulation capabilities: quantitative gravity field local suitability assessment, dynamically adjusted time-varying search scope, three-level multi-scale stepwise matching, and along-track trajectory motion physics consistency constraint. The verification of long-term shipborne lake experiments confirms that the proposed method reduces the final gravity matching positioning root mean square error (RMSE) to only 528.2 m, which is more than 41% lower than the classical terrain contour matching (TERCOM) benchmark and 31% lower than iterative closest contour point (ICCP). This biomimetic full-design-chain solution provides a robust new practical navigation paradigm for long-endurance fully autonomous underwater vehicles operating without any external auxiliary positioning information. Full article

This research presents a comparative performance evaluation of two advanced maximum power point tracking (MPPT) methodologies, namely sliding mode control (SMC) and the Kalman filter (KF), specifically applied to a standalone photovoltaic water pumping system (PVWPS). To achieve economic viability, the system is designed for storage-less operation, driving a three-phase induction motor (IM) via a high-dynamic direct torque control (DTC) scheme and a three-level inverter. The core technical contribution addresses the critical challenge of maximizing energy yield under partial shading conditions (PSCs). PSCs result in a complex, non-convex power–voltage (P−V) characteristic, containing multiple peaks, where conventional MPPT algorithms fail to consistently locate the global maximum power point (GMPP). To overcome this deficiency, we implemented the SMC-based MPPT algorithm to exploit its inherent robustness and rapid dynamic response, and compared it with the Kalman filter MPPT, which relies on stochastic state estimation to achieve accurate tracking and effective disturbance rejection. MATLAB/Simulink analysis compares the proposed techniques with the perturb and observe (P&O) MPPT method. The comparison considers tracking efficiency, convergence speed, and steady-state ripple under various shading conditions to identify the most effective control strategy for improving PVWPS performances. The reported performance evaluations are based on numerical simulations conducted within the MATLAB/Simulink environment, using a validated system model. Full article

Accurate and smooth path tracking is important for autonomous tracked agricultural robots operating in greenhouse-like environments. Existing adaptive look-ahead pure-pursuit methods mainly adjust the look-ahead distance according to vehicle speed or path geometry, while the influence of time-varying localization reliability has not been sufficiently considered. This study proposes a noise-aware adaptive pure-pursuit controller that combines Extended Kalman Filter (EKF) estimation with causal Savitzky–Golay (SG) endpoint smoothing. A bounded look-ahead law is designed by jointly considering normalized vehicle speed, lateral error, path curvature, and an innovation-derived localization-noise indicator. Numerical simulations were conducted on straight, circular, S-shaped, and U-shaped reference paths under prescribed localization disturbances. Under the 0.5 m positional-noise condition, the proposed method achieved an root mean square error (RMSE) of 0.087 m and an angular-velocity root mean square (RMS) of 0.28 rad/s, compared with 0.112 m and 0.36 rad/s, respectively, for conventional fixed-look-ahead pure pursuit. Compared with proportional-integral-derivative (PID), Stanley, model predictive control (MPC), and conventional pure-pursuit controllers, the proposed method provides a favorable balance between tracking accuracy and control smoothness. It also has better computational efficiency than MPC while retaining the low-computational-burden advantage of geometric control. In the sensitivity analysis, the relative RMSE increase from 0.1 to 0.8 m was 36.5% for the proposed method and 103.4% for conventional pure pursuit. These results indicate that the proposed lightweight noise-aware control strategy can improve tracking accuracy, control smoothness, and tolerance to localization disturbances under the specified numerical conditions, providing a practical design reference for low-speed greenhouse agricultural robots. Full article

Precise data regarding the overall mass of the machinery and the gradient of the track are crucial for optimizing the control of monorail cranes and enhancing energy efficiency. Within the context of electric monorail cranes (EMCs), accurately estimating the total mass of the machinery and the track gradient poses a formidable challenge. This challenge arises from the strong coupling between the overall mass of the machine and the track gradient, the robustness of parameter estimation methods under varying operational conditions, and the generalizability of the algorithm to real-world operations and rail scenarios of EMCs. To address these challenges, this paper proposes a novel parameter estimation scheme that comprehensively considers the impact of parameter coupling relationships and multiple influencing factors in the transportation scenarios of EMCs under actual working conditions. First, to overcome measurement difficulties induced by strong coupling between the EMC mass and track gradient, a decoupling estimation method based on cascaded dual observers is proposed to jointly estimate the two states. Secondly, to mitigate track slope estimation errors under complex track types and diverse operating conditions, an enhanced immune optimization algorithm, integrating a Weibull function and Levy flight mechanism, in conjunction with an unscented Kalman filter (UKF), is developed. Furthermore, to achieve high-precision and stable parameter identification results, a Weibull dynamic forgetting factor is incorporated into the RLS algorithm, leading to the design of a WDFF-RLS estimator. Finally, real vehicle experiments were conducted on complex tracks at the test site to validate the accuracy and robustness of the proposed estimation method. Full article

In this paper, a fractional-order active disturbance rejection repetitive control (FADRRC) scheme is proposed for fractional-order time-delay systems. The unified control framework integrates a bandwidth-parameterized fractional extended state observer (FESO), modified repetitive control and a filtered Smith predictor. The FESO online estimates system states and lumped disturbances, the filtered Smith predictor compensates time-delay deviation to weaken sensitivity to model mismatch, and repetitive control eliminates steady-state error for periodic reference signals. By fractional frequency-domain stability theory and the small-gain theorem, two BIBO stability criteria for nominal and mismatched cases are derived, reformulating the controller design as a pole placement problem of fractional-order transfer functions. Numerical simulations on a fractional-order time-delay PMSM servo system verify that the proposed FADRRC possesses superior periodic tracking accuracy and disturbance rejection capability compared with existing ESO-RC and DOB-RC methods, which confirms the validity of the presented control strategy. Full article

This paper proposes a hybrid machine-learning approach based on the genetic-neural online adaptation of the process noise covariance matrix in an adaptive Kalman filter, designed to improve the accuracy of dynamic tracking in electro-pneumatic systems. The method combines offline parameter optimisation using a genetic algorithm with online adaptation via a neural network. A two-level architecture was developed to generate the process noise covariance matrix, based on adaptive coefficients through which the statistical characteristics of the mathematical model are aligned with the current dynamic state of the electro-pneumatic system. This approach ensures that the algorithm adapts to changes in speed, acceleration, external load and motion profile, leading to a more accurate state estimation, improved feedback quality and enhanced accuracy of dynamic tracking across diverse operating modes. A key feature of the proposed method is the use of an independent reference measurement channel. Unlike current adaptive structures, in which the Kalman filter is tuned solely based on measurements from internal sensors, the proposed method determines the adaptive coefficients by optimisation based on minimising the actual tracking error, measured by an independent reference metrological system implemented using a laser interferometer. Full article

In order to maintain stable formations of the underactuated unmanned surface vehicles (USVs) under ocean disturbances and reduce control chattering, a fuzzy adaptive super-twisting sliding mode control method combined with dynamic cooperative error correction is introduced in this paper. At the kinematic level, a dynamic cooperative error correction scheme is presented to include the relative positions of the neighboring vehicles. This results in a transition from independent tracking to interactive cooperation, thus improving the rigidity of the formation during maneuvers. Secondly, a composite inner-loop structure is designed by employing a nonlinear disturbance observer to counteract the effects of external loads. A fuzzy logic system is included to modify the super-twisting sliding mode gains in real-time. This approach effectively combines rapid error reduction with signal smoothness, addressing the trade-off between response speed and chatter suppression. Moreover, tracking differentiators and low-pass filters are utilized to obtain continuous control signals. The Lyapunov analysis indicates that the closed-loop system achieves semi-global uniform ultimate boundedness. Simulation results show that the proposed method can maintain high formation precision and obtain smooth control outputs with reduced chattering. Full article

High-level autonomous driving requires an exceptionally accurate and robust perception of surrounding road environments to support reliable vehicle localization. While utilizing multiple LiDAR sensors expands the field of view and provides high-density point clouds, it introduces severe challenges, such as multi-LiDAR mutual interference (crosstalk) and significant computational overhead. This paper proposes a multi-stage front-end perception pipeline designed for robust high-altitude structural landmark extraction and crosstalk suppression to provide clean geometric reference points for downstream positioning systems. The proposed framework first employs a Binary Bayes Filter combined with a mesh-graph representation to segment stable overhead road traffic signs while minimizing environmental noise. To ensure real-time operation, a hybrid cascade consisting of 2D-grid spatial projection and voxelized 3D Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is introduced, drastically reducing point cloud density bottlenecks. Finally, a cluster-refinement stage is applied to filter out residual false positives induced by crosstalk. The system was validated using real-world driving data collected over an urban sequence (1.88 km) and a highway sequence (37 km). Experimental results demonstrate that the pipeline achieves high landmark tracking success rates of 79.9% and 98.5%, respectively, while suppressing crosstalk-induced false alarms down to 12.3%. Operating entirely on CPU threads with an average latency of 24.5 ms per frame, the framework satisfies real-time execution bounds for standard 10 Hz LiDAR setups, establishing a high-fidelity front-end capable of preventing tracking drift in autonomous vehicle localization. Full article

Background/Objectives: Current pulmonary nodule detection systems often neglect the spatiotemporal continuity inherent in computed tomography (CT) sequences within a single scan. They treat individual slices as independent images and lack mechanisms to recover missed detections. As a result, nodules missed in individual slices cannot be recovered. This study aims to develop a detection–tracking co-design framework that improves both sensitivity and specificity for pulmonary nodule detection. Methods: We propose a detection-tracking co-design framework. It couples an enhanced YOLOX detector with a Kalman filter-based tracker in Stage I, to associate nodule candidates across consecutive slices and recover missed detections. Stage II employs cross-slice Maximum Intensity Projection and a lightweight ResNet-50 classifier, to reduce false positives through morphological feature discrimination. Results: Systematic ablation studies on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset using the criteria of the Lung Nodule Analysis 2016 (LUNA16) dataset demonstrate the complementary contributions of each component. The complete framework achieves a Competition Performance Metric (CPM) score of 0.9344 and exhibits particular strength in medium-to-high sensitivity regions. Conclusions: The proposed detection–tracking co-design provides an interpretable paradigm that balances high sensitivity and improved specificity, offering a promising solution for computer-aided diagnosis of pulmonary nodules. Full article

Periodic manual oversight is difficult to scale for large portfolios of funded research projects. Progress delays, budget irregularities, and superficial reporting often go undetected until final acceptance. Many conventional detection methods also generate false positives when contextually supported schedule adjustments resemble anomalous patterns. We present the Intelligent Project Monitoring System (IPMS), which couples a feature-decoupled multi-task LSTM-Attention network with an Adaptive Isolation Forest. The LSTM-Attention component models project workflows through finite state machines and predicts milestone deviations. The Adaptive Isolation Forest then flags records after a context gate screens cases meeting the study’s legacy legitimate-deviation criteria before final alerting. A multi-head attention module tracks how execution performance evolves over the project lifecycle, and the system includes a loss-ratio signal for candidate-shift review and feedback-gated controlled recalibration; its response was evaluated only under one researcher-designed synthetic global policy-change injection. On a real-world dataset from a provincial management platform, in which approximately 7% of legacy-labeled records carried an anomalous reference label, IPMS achieved an AUC of 0.924 and a false-positive rate of 4.2% against the available legacy binary reference labels, a 76.9% relative reduction in observed FPR compared with standard Isolation Forest. Milestone deviation prediction reached an MAE of 1.85 days, 34.2% lower than standard LSTM. Execution profiling achieved an MAE of 0.082. Removing the deviation filter alone degraded F1 by 12.3%, and removing multi-scale fusion increased the miss rate for long-duration stalls by 23%. Full article

Coastal green tides may alter nearshore bioaerosols through coupled marine, atmospheric, and meteorological processes, yet their effects on airborne microbial communities remain poorly resolved. Atmospheric samples were collected in Aoshan Bay, Qingdao, China, during five phases of the Ulva prolifera green tide in 2019 (pre-bloom, 19 April; early bloom, 15 June; middle bloom, 15 July; late bloom, 6 August; post-bloom, 30 August); seawater samples were collected at one nearshore site on each of the five sampling dates, with microbial sequencing performed for the middle-bloom (15 July) and late-bloom (6 August) phases. Bacterial and fungal communities were characterized; although bioaerosols may also contain microalgae and viruses, this study profiled only the bacterial and fungal fractions, using bacterial 16S rRNA gene (V3-V4 region) and fungal internal transcribed spacer (ITS2) amplicon sequencing and evaluated together with meteorological variables, air-pollutant concentrations, and 72-h backward air-mass trajectories. Proteobacteria dominated the airborne bacterial assemblages (81.28–97.83%), with Sphingomonas as the most abundant genus (47.85–89.84%). Basidiomycota and Ascomycota dominated the fungal assemblages, whereas Cryptococcus and Alternaria were the major fungal genera. Community richness and composition varied across bloom phases. Chytridiomycota was undetected before the bloom (0%), appeared after bloom onset, and reached its highest relative abundance during the middle phase (8.19%). Spatial patterns indicated joint terrestrial and marine influences, although bacterial communities in seawater and air remained highly dissimilar. Temperature, relative humidity, particulate matter, ozone, and air-mass origin were associated with changes in microbial diversity and composition. These findings provide an observational baseline for coastal bioaerosol dynamics during a macroalgal green tide, extending the HAB–bioaerosol literature—which has focused predominantly on cyanobacterial blooms—to a large green macroalga. Bacteria and fungi showed contrasting environmental responses: bacterial richness increased with temperature, whereas fungal diversity declined. Greater compositional similarity between seawater and air for fungi than for bacteria suggests differential environmental filtering at the air–sea interface and implies that multiple source pathways—direct aerosolization, sea-surface release, and in-situ atmospheric production—may differentially shape the two domains. Given the single-date-per-phase sampling design, the absence of sequenced laboratory contamination controls, and the lack of absolute abundance data, these results should be regarded as preliminary and hypothesis-generating, underscoring the need for ASV-level source tracking, controlled chamber experiments, and replicated multi-year designs in future assessments of bloom–atmosphere interactions. Full article

Weld tracking aims to provide real-time compensation for weld deviations caused by groove assembly inaccuracies and thermal shrinkage during Gas Metal Arc Welding (GMAW). To achieve precise tracking control in swing-arc narrow-gap GMAW based on passive visual sensing, a Self-Adaptive Coefficient-of-Variation Recognition (SCVR) algorithm is proposed for adaptively detecting weld deviation by filtering out welding interference. SCVR adaptively constructs a data window by discriminating the original variation coefficient to acquire the raw data distribution of the groove centerline. It then designs an in situ bandpass data filter to locally search the data segment with the minimal coefficient of variation for adaptive bandwidth determination. By applying the filter to the raw data, the disturbed data are removed, in situ retaining the data with the globally minimized variation coefficient. Finally, SCVR recognizes the real groove center from the filtered data, accurately detecting a weld deviation by comparing this center to the torch position. Additionally, an SCVR-based real-time tracking control system incorporating a PLC-based actuator with a PI controller for optimal stability is developed to correct the torch position in real time, achieving a high tracking precision of −0.161~+0.126 mm. Experimental results demonstrate the robust adaptability and effectiveness of the SCVR-based weld detection and tracking control system. Full article

In space target surveillance missions, effective detection of dim space targets remains a challenge due to dense star interference and the random nature of target motion. To quickly and accurately extract dim space targets from complex star backgrounds, this paper proposes a dim space target detection and track association method for dense star backgrounds. This paper analyzes various features of the target and background from a combined spatiotemporal perspective, including three main stages. First, inter-frame registration is used to filter out bright stars, followed by connected domain post-processing, which simplifies the star map background while enhancing the signal-to-noise ratio of dim targets. Secondly, an image difference fusion coarse processing module is proposed. The reconstructed multi-impulse function is derived from the spectral phase difference to estimate the displacement parameters of different components, after which the image difference fusion is designed to obtain candidate targets. Third, a directional track association algorithm is designed, with the candidate targets as the center and the motion parameters as thresholds, narrowing the association range to a fan-shaped region. This enables fast detection of target tracks while removing excess false alarms. The experimental results on four datasets demonstrate that this method outperforms traditional baseline methods in terms of target detection and localization accuracy. Full article