Search Results (525)

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0-1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. 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

To address the challenges of linguistic fuzziness, cognitive variability across fault modes, and the risk of model distortion during optimization, this paper proposes an interpretable belief rule-based fault diagnosis method for complex equipment considering linguistic fuzzy information. First, to address the difficulty experts face in providing precise probability values, an interval grey number table is constructed. By converting linguistic fuzzy information into interval grey representations, the approach quantifies the uncertainty inherent in expert judgments while fully preserving the boundary information of the underlying knowledge. Second, recognizing that expert familiarity varies across different fault modes, a certainty degree fusion method is introduced. This method utilizes fusion weights to mitigate the interference of low-confidence evidence during rule generation. Finally, an interpretable parameter optimization method featuring dynamic knowledge anchoring is designed to constrain model parameters within the reasonable bounds defined by expert knowledge. Validation on an electromechanical actuator demonstrates that the proposed method not only achieves superior diagnostic performance but also ensures model usability and interpretability in practical engineering applications. Full article

In maritime electronic support measures (ESMS), multipath-scattered pulses are often suppressed during pulse sorting, although their delay, amplitude, and angular differences may provide information for passive scatterer localization. This paper investigates a front-end path-classification task positioned after emitter-level clustering and before multipath-assisted passive localization. Pulses produced by the same non-cooperative emitter but received through different propagation paths are classified as direct-path or multipath-scattered pulses. The task is formulated as supervised binary classification over PDW sequences. Five representative solution families are evaluated under a common protocol: FCM, DBSCAN, temporal sequence analysis (TSA), Single-LSTM, and a residual two-layer unidirectional LSTM with residual fusion. The input features are RF, PA, PW, PRI, TOA, DOA, and ΔTOA; the recurrent models use class-weighted training to address the direct/scattered class imbalance. Across 36 coupled scenarios with pulse-loss rates from 0% to 50% and parameter-jitter levels from 0.0 to 1.0, the residual LSTM obtains the highest average macro-F1 score (0.8717), compared with Single-LSTM (0.7726), DBSCAN (0.7686), TSA (0.6511), and FCM (0.5917). Repeated training over four random seeds yields a validation macro-F1 of 0.9821 ± 0.0007 on the original validation set. The ablation results indicate that ΔTOA is the principal temporal cue in this setting, while LayerNorm, residual fusion, class weighting, and augmentation mainly contribute to optimization stability and perturbation robustness. Measured-data verification suggests that the learned temporal representation can provide usable inputs for subsequent scatterer localization. The current validation is limited to a one-emitter simulation and rule-assisted measured-data annotation; mixed-emitter validation and quantitatively calibrated localization evaluation remain subjects for future study. Full article

Invariant-rule-based anomaly detection is attractive for industrial control systems (ICSs) because its rules are interpretable, auditable, and learnable from normal-operation data alone. However, mined invariants can miss attacks that induce weak, localized, transient, or rule-consistent deviations, because such attacks may not sufficiently violate the specific variable relationships captured by the rules. Aggregate time-step metrics can also obscure these failures, since they do not reveal which documented attack windows remain uncovered. Therefore, we analyze rule-only detection failures at the attack-window level and evaluate a rule-preserving hybrid detector that keeps the original invariant-rule alarm unchanged while adding learned anomaly evidence from per-sensor XGBoost residual models and an Anomaly Transformer. The final alarm uses OR fusion and matched-FPR results are reported as an evaluation-time operating-point analysis under a common system-level false-positive budget. On the SWaT benchmark, the reproduced rule-only detector detects 16/36 attacks at an attack-window recall threshold of 0.05 and 13/36 at 0.4. At the Zhu-matched evaluation-time false-positive budget ($\alpha \approx 0.00447$), the pre-specified equal-weight hybrid reaches 19/36 and 16/36, respectively. For localization, SHAP attribution on the XGBoost residual models places the attacked sensor in the top-5 for 70.6% of direct sensor attacks and a variable from the correct process stage in the top-5 for 94.4% of all attacks. These results indicate that rule-preserving residual learning modestly improves attack-level coverage while providing operator-oriented localization evidence rather than definitive root-cause identification. Full article

Geometric relation parsing is a prerequisite for automated geometry problem solving, especially when diagram interpretation depends jointly on visual appearance and textual conditions. In this study, we examine a text-conditioned parsing setting derived from PGDP5K and propose a lightweight parser with atomic cue extraction, iterative visual–semantic feedback, and differentiable logic regularization. Because the active high-level labels are derived through a rule-based weak-supervision protocol, the results should be interpreted as parser-level evidence under Ext-PGDP5K rather than proof of general geometric semantic understanding. The nominal label space contains five candidate relations, while the current evaluation focuses on four active relations with positive instances: Intersect, Parallel, Perpendicular, and Bisect. Compared with text-only, image-only, global-fusion, and shuffled-text controls, the proposed parser improves Edge-F1 and Macro-F1, with the clearest gains for Parallel and Perpendicular. Ablations show that the atomic probe is the main source of improvement, while logic regularization and feedback exhibit non-monotonic interactions. Although limited by weak labels, lexical cues, and the absence of downstream solver validation, this study provides a reproducible protocol-aligned testbed for analyzing text-conditioned relation prediction and low-order logic regularization in geometric diagram parsing. Full article

Kernel-combination procedures used in classification often return only a combined kernel matrix on the training sample, rather than a kernel function that can be evaluated consistently at new points. This limitation is especially important for supervised or label-aware combinations, whose entries may depend on training labels and therefore have no immediate out-of-sample meaning. We study the problem of constructing an inductive, finite-rank kernel extension from such empirical matrices. The proposed framework makes the non-uniqueness of this extension explicit: it is determined by empirical coordinates, a positive-semidefinite coefficient matrix, and a continuation model for the coordinates. Experiments on vector, tabular, and relational classification problems give a deliberately diagnostic picture. Smooth direct combinations are stable: on Synthetic, the direct mean gives error $0.0793\pm 0.0227$, essentially matching the best individual RBF kernel ($0.0809\pm 0.0231$), and on Telco it remains close to the best individual polynomial kernel ($0.2061\pm 0.0154$ versus $0.2045\pm 0.0154$). In the controlled Synthetic oracle diagnostic, reconstructing a smooth sum/mean gives relative Frobenius error $4.13\times {10}^{-6}\pm 9.41\times {10}^{-6}$ and functional MSE at numerical scale. By contrast, abrupt label-aware matrix-only rules are less robust: the Synthetic percentile_inout_auto rule has error $0.1404\pm 0.1198$, Telco matrix-only supervised rules are around $0.307$–$0.326$ error, and the Chickenpieces pickout_auto rule fails under strict out-of-sample reconstruction ($0.3545\pm 0.2666$ error), whereas direct relational combinations match the best individual relational kernel within ${10}^{-3}$. Overall, the empirical evidence supports the method as a bridge from finite matrix-level information fusion to deployable kernels, while also identifying abrupt label-aware geometries as the main limitation for stable generalization. Full article

Process data plays a vital role in diagnosing fault sources in chemical production. However, such data contain rich process information and are often sensitive, making direct analysis infeasible due to privacy concerns. Although federated learning mitigates data leakage risks, the conventional averaging strategy falls short in achieving high fault identification accuracy, especially under non-independent and identically distributed (non-IID) client data. To overcome this challenge, we propose a personalized federated learning framework, in which a Takagi–Sugeno (T–S) fuzzy fusion rule is designed. Then, the personalized model is constructed through a structured procedure: fuzzification of model parameter distances, definition of fuzzy rules, fuzzy inference, and defuzzification. Moreover, layer-wise fusion is employed to enhance the precision of aggregation. Evaluations on the Tennessee Eastman (TE) process demonstrate that our method achieves superior fault identification accuracy. The results validate the efficacy of the proposed Fuzzy Rule-Based Federated Layer-wise Fusion (FedFZ) framework in industrial fault diagnosis under heterogeneous data distributions. Full article

Introduction: Schizophrenia is characterised by distributed abnormalities in electrophysiological dynamics and large-scale brain networks, yet unimodal EEG or MRI alone cannot fully explain how fast neural computations relate to spatially organised circuit dysfunction. Multimodal EEG–MRI approaches offer a bridge across temporal and anatomical scales by explicitly modelling cross-modal coupling. Methods: Following PRISMA 2020 guidance, we conducted a systematic, mechanistic review of human studies (adults ≥ 18 years) comparing schizophrenia-spectrum groups with healthy controls using EEG combined with at least one MRI modality (fMRI, structural MRI, and/or diffusion MRI) and explicit EEG–MRI integration (e.g., EEG-informed fMRI, joint ICA, mCCA/MCCA, coupled matrix–tensor factorisation, DCM-based fusion). Searches were performed in PubMed/MEDLINE, Embase, Web of Science, Scopus, PsycINFO, IEEE Xplore, ResearchGate, and Google Scholar for January 2000–December 2025, supplemented by citation tracking. Risk of bias was assessed with ROBINS-I, and due to heterogeneity, results were synthesised narratively by integration of families. Results: From 148 records, 23 studies met the inclusion criteria. Studies used mainly simultaneous EEG–fMRI at 3T and spanned resting-state designs and task paradigms dominated by auditory processing (oddball, MMN/N100–P200, ASSR/aeGBR), with additional work in affective context, working memory, semantic processing (N400), sensory gating, and pharmacologic challenge. Across tasks, the most reproducible multimodal signature was disrupted coupling between electrophysiological markers and the recruitment of large-scale networks, rather than isolated changes in EEG or fMRI metrics. Target detection/oddball paradigms converged on reduced late ERP responses (especially P300, sometimes N2) alongside reduced expression or loss of coupling to salience/ventral attention and control circuitry (including ACC/anterior insula/TPJ). Resting-state studies most consistently indicated altered “coupling rules” (frequency specificity, timing/lag structure, and directionality), including abnormalities detectable even when unimodal summaries were weak. Extended multimodal studies (adding sMRI/DTI and/or classification) suggested that combining modalities can improve discrimination, though performance was sensitive to sample size, demographic imbalance, and feature-selection/validation choices. Conclusions: Multimodal EEG–MRI studies support schizophrenia as a disorder involving persistent structural and circuit-level abnormalities whose functional expression varies dynamically across cognitive states and task demands. Future progress will depend on harmonised acquisition/artefact-control practices for simultaneous EEG–fMRI, larger and more diverse samples (including early/CHR and longitudinal designs), and cross-site replication of mechanistically interpretable coupling biomarkers. Full article

The emergence of massive knowledge in online learning systems has increased the difficulty for learners to acquire the necessary information. Due to unclear information expression and excessive knowledge redundancy, learners face challenges in identifying relevant knowledge. Furthermore, the presence of substantial unstructured knowledge in subject domains also hinders the effective transmission and application of knowledge. To address these issues, a framework for constructing a subject domain knowledge graph is proposed in this work. The framework primarily aims to visualize isolated information and connect knowledge into graph structures. The knowledge graph can help learners quickly and efficiently acquire the knowledge they need. The novel framework is constructed with three steps. The first step is to design the ontology rules based on the domain-specific subject knowledge from the perspective of classification, and also to construct the schema layer of the knowledge graph. The second step is to propose a domain-optimized BiLSTM-ResNet-CRF model for subject domain entity recognition, which introduces residual blocks to enhance fine-grained local contextual feature extraction for multi-word technical terms, addressing the limitations of traditional BiLSTM-CRF models in educational text processing. The BERT relation extraction model is used to extract relations between knowledge entities. Then the data layer is constructed. Finally, the third step is to achieve knowledge fusion through entity linking and two-layer entity alignment against results stored in a database. The result comparisons on the dataset show that the novel BiLSTM-ResNet-CRF model has higher scores than several other classical models, achieving an F1-score of 80.26%. The proposed framework’s effectiveness is rigorously validated using high school mathematics as a representative case study with a well-structured knowledge system. Full article

This paper investigates attack- and channel-aware decision fusion for Reconfigurable Intelligent Surface (RIS)-enhanced Cooperative Spectrum Sensing (CSS) in Cognitive Radio Networks (CRNs) to mitigate the challenge from Byzantine attacks. Specifically, we first propose the optimal hard decision fusion rule for the Fusion Center (FC) based on maximum-likelihood criterion, which simultaneously accounts for channel impairments and statistical characteristics of Byzantine attacks. Following from this result, we then derive three suboptimal and low-complexity decision fusion rules when the Channel State Information (CSI) cannot be perfectly achieved at the FC. The correspondingly results indicate that negative weighting coefficients can be adaptively assigned to malicious reports based on attack intensity, which can successfully transform adversarial interference into effective detection gains for the FC in some scenarios. This finding profoundly reveals the intrinsic mechanism of how Byzantine attacks impact the decision fusion, and thus provide a rigorous theoretical perspective for developing robust decision fusion rule capable of adaptively suppressing and conversely exploiting malicious reports. Furthermore, to make practical implementation of our decision fusion rules, we develop simple and unbiased attack parameter estimation algorithms based on the first-order statistics of received reports at the FC, which also exhibits good convergence. Our results indicate that we can insert a virtual source under control, and send false data to the Byzantine attackers. This deception strategy can help the FC successfully learn the attack parameter aided by its collected data. Finally, extensive simulations are conducted and the correspondingly results demonstrate that our proposed fusion rules can effectively mitigate Byzantine attacks across a wide range of attack scenarios, and they can outperform traditional malicious report filtering defense algorithm by successfully reversing and exploiting malicious reports. Full article

As the role of remote sensing continues to grow, the fusion technology of low-spatial-resolution hyperspectral images and high-spatial-resolution multispectral images has become increasingly critical. Traditional methods rely on fixed rules and exhibit poor robustness, whereas deep learning methods struggle to establish efficient interactions between local and global information due to the complexity of their underlying networks. Therefore, we propose a deep learning fusion module that combines pixel-wise adaptive wavelet transform with a spectral–spatial dual-branch extraction. Firstly, by utilizing the unique properties of the wavelet transform, it is possible to effectively preserve spectral information and extract spatial edge features, thereby achieving preliminary fusion by leveraging both low-frequency and high-frequency components. To compensate for the lack of nonlinear expression capability in the wavelet transform, a dual-branch parallel extraction of spectral and spatial features is subsequently performed in the deep learning module. The Multi-Scale Group Convolution module (MSGC) is utilized to extract spectral information, while the Spectral Compression and Spatially Guided Gating Module (SCSGM) is employed to extract spatial information, thereby enhancing the data’s adaptive capability. A bidirectional attention mechanism is interspersed within the module to capture complementary information across different scales, ultimately reconstructing a high-resolution hyperspectral image. Finally, the proposed fusion strategy demonstrates superior performance in practical image reconstruction, outperforming more than ten state-of-the-art fusion methods. Full article

Class distribution asymmetry (imbalanced data) is a prevalent problem in the field of Industrial Internet cybersecurity, where normal data far outnumber abnormal data. This causes traditional machine learning classifiers to be biased towards the majority class, severely degrading their attack detection capability. To address this issue while meeting the requirement for traceability of the decision-making process in industrial scenarios, this paper proposes an imbalanced data classification method based on the Belief Rule Base (BRB). First, the Cluster-Based Oversampling (CBO) algorithm is employed to restore the symmetry of class distribution at the data level. Then, the Evidential Reasoning (ER) iterative algorithm is used to perform attribute fusion, which reduces the number of antecedent attributes of BRB while maintaining the information, effectively alleviating the rule explosion problem. Finally, interpretable classification is realized based on BRB, and the Circle chaotic mapping Gray Wolf Optimizer (Circle-GWO) algorithm is introduced to complete model construction, parameter optimization and fine-tuning. Experimental results on the UNSW-NB15 and TON_IoT datasets demonstrate that the proposed method can effectively handle imbalanced data classification tasks in this field, providing a practical technical solution to improve the accuracy and efficiency of cybersecurity decision-making in the Industrial Internet. Full article

The residual ratio of ultimate bending moment is a critical indicator for hull structural safety assessment of damaged ships. In maritime emergency scenarios, the empirical formula method has insufficient prediction accuracy, while nonlinear finite element (FE) simulation bears prohibitive computational cost. To address this limitation, we propose a rapid surrogate model for predicting the residual ultimate bending moment ratio of side-damaged ships. The model integrates a lightweight one-dimensional U-Net (1D U-Net) for nonlinear feature extraction and multi-scale feature fusion and a fuzzy inference module for embedding engineering prior constraints. Trained on a 1D structured dataset generated via the modified Smith method (covering multiple damage conditions, hogging and sagging), the model achieves an overall mean absolute error (MAE) of 1.79% and root mean squared error (RMSE) of 2.39% on the test set. It outperforms empirical formulas in accuracy with ultra-short inference time, far lower computational cost than FE simulation, and provides engineering interpretability via activated fuzzy rules. This work offers an efficient alternative tool for rapid safety assessment of damaged hull structures. Full article

Case-based reasoning (CBR) is an efficient intelligent decision-making approach, but traditional methods often neglect the weight and reliability of decision information and struggle with attribute heterogeneity and missing data. This study proposes a novel CBR method based on the generalized combination (GC) rule to overcome these limitations. We design differentiated similarity calculations for heterogeneous attributes, and construct basic probability assignments (BPAs) by grouping historical cases with identical similarity to handle missing data. Then, Deng entropy and Jousselme distance are used to characterize attribute weight and reliability, respectively. Discounted BPAs are recursively fused via the GC rule, and final decisions are derived through Bayesian approximation. A case study of typhoon disaster emergency decision-making demonstrates the superior performance of the proposed method. Full article